{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Usuario\\anaconda3\\lib\\site-packages\\pandas\\core\\computation\\expressions.py:20: UserWarning: Pandas requires version '2.7.3' or newer of 'numexpr' (version '2.7.1' currently installed).\n",
      "  from pandas.core.computation.check import NUMEXPR_INSTALLED\n"
     ]
    }
   ],
   "source": [
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from pandas import DataFrame\n",
    "from cmapPy.pandasGEXpress.parse import parse\n",
    "from scipy.stats import hypergeom\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_adrs_sexbias = pd.read_csv(\"df_drug_adr_p_value_sex-bias_logodd_21_11_23.csv\", sep=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Drug</th>\n",
       "      <th>ADR</th>\n",
       "      <th>Number adrs in Male</th>\n",
       "      <th>Number adrs in Female</th>\n",
       "      <th>P-value</th>\n",
       "      <th>p_value_FDR</th>\n",
       "      <th>Significance</th>\n",
       "      <th>Sex-biased</th>\n",
       "      <th>odds_ratio</th>\n",
       "      <th>Log2_Odds_ratio</th>\n",
       "      <th>sex_biased_log_oddratio</th>\n",
       "      <th>Sum_ADRS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>DB00398</td>\n",
       "      <td>10061428</td>\n",
       "      <td>150</td>\n",
       "      <td>52</td>\n",
       "      <td>2.832808e-02</td>\n",
       "      <td>(array([ True]), array([0.02832808]), 0.050000...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>sex-biased male</td>\n",
       "      <td>0.699466</td>\n",
       "      <td>-0.515673</td>\n",
       "      <td>sex-biased male</td>\n",
       "      <td>202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>DB00398</td>\n",
       "      <td>10055798</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>4.579148e-02</td>\n",
       "      <td>(array([ True]), array([0.04579148]), 0.050000...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>sex-biased female</td>\n",
       "      <td>2.180080</td>\n",
       "      <td>1.124381</td>\n",
       "      <td>sex-biased female</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>DB00398</td>\n",
       "      <td>10001760</td>\n",
       "      <td>35</td>\n",
       "      <td>47</td>\n",
       "      <td>8.193338e-06</td>\n",
       "      <td>(array([ True]), array([8.19333766e-06]), 0.05...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>sex-biased female</td>\n",
       "      <td>2.748978</td>\n",
       "      <td>1.458896</td>\n",
       "      <td>sex-biased female</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>DB00398</td>\n",
       "      <td>10018388</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>1.362716e-02</td>\n",
       "      <td>(array([ True]), array([0.01362716]), 0.050000...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>sex-biased female</td>\n",
       "      <td>4.576790</td>\n",
       "      <td>2.194336</td>\n",
       "      <td>sex-biased female</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>DB00398</td>\n",
       "      <td>10043942</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4.333231e-02</td>\n",
       "      <td>(array([ True]), array([0.04333231]), 0.050000...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>sex-biased female</td>\n",
       "      <td>5.081070</td>\n",
       "      <td>2.345132</td>\n",
       "      <td>sex-biased female</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3958</th>\n",
       "      <td>0</td>\n",
       "      <td>DB12332</td>\n",
       "      <td>10010305</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>2.164259e-02</td>\n",
       "      <td>(array([ True]), array([0.02164259]), 0.050000...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>sex-biased female</td>\n",
       "      <td>0.101973</td>\n",
       "      <td>-3.293734</td>\n",
       "      <td>sex-biased male</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3959</th>\n",
       "      <td>0</td>\n",
       "      <td>DB12332</td>\n",
       "      <td>10061818</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2.632173e-03</td>\n",
       "      <td>(array([ True]), array([0.00263217]), 0.050000...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>sex-biased female</td>\n",
       "      <td>0.027176</td>\n",
       "      <td>-5.201535</td>\n",
       "      <td>sex-biased male</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3960</th>\n",
       "      <td>0</td>\n",
       "      <td>DB12332</td>\n",
       "      <td>10066274</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1.770550e-03</td>\n",
       "      <td>(array([ True]), array([0.00177055]), 0.050000...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>sex-biased female</td>\n",
       "      <td>0.020381</td>\n",
       "      <td>-5.616655</td>\n",
       "      <td>sex-biased male</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3961</th>\n",
       "      <td>0</td>\n",
       "      <td>DB12332</td>\n",
       "      <td>10043071</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1.071887e-03</td>\n",
       "      <td>(array([ True]), array([0.00107189]), 0.050000...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>no sex-biased</td>\n",
       "      <td>0.013586</td>\n",
       "      <td>-6.201700</td>\n",
       "      <td>sex-biased male</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3962</th>\n",
       "      <td>0</td>\n",
       "      <td>DB12332</td>\n",
       "      <td>10076476</td>\n",
       "      <td>16</td>\n",
       "      <td>55</td>\n",
       "      <td>1.237521e-15</td>\n",
       "      <td>(array([ True]), array([1.23752125e-15]), 0.05...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>sex-biased female</td>\n",
       "      <td>0.044078</td>\n",
       "      <td>-4.503794</td>\n",
       "      <td>sex-biased male</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3963 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0     Drug       ADR  Number adrs in Male  \\\n",
       "0              0  DB00398  10061428                  150   \n",
       "1              0  DB00398  10055798                   14   \n",
       "2              0  DB00398  10001760                   35   \n",
       "3              0  DB00398  10018388                    4   \n",
       "4              0  DB00398  10043942                    2   \n",
       "...          ...      ...       ...                  ...   \n",
       "3958           0  DB12332  10010305                    2   \n",
       "3959           0  DB12332  10061818                    2   \n",
       "3960           0  DB12332  10066274                    2   \n",
       "3961           0  DB12332  10043071                    2   \n",
       "3962           0  DB12332  10076476                   16   \n",
       "\n",
       "      Number adrs in Female       P-value  \\\n",
       "0                        52  2.832808e-02   \n",
       "1                        15  4.579148e-02   \n",
       "2                        47  8.193338e-06   \n",
       "3                         9  1.362716e-02   \n",
       "4                         5  4.333231e-02   \n",
       "...                     ...           ...   \n",
       "3958                     15  2.164259e-02   \n",
       "3959                      4  2.632173e-03   \n",
       "3960                      3  1.770550e-03   \n",
       "3961                      2  1.071887e-03   \n",
       "3962                     55  1.237521e-15   \n",
       "\n",
       "                                            p_value_FDR Significance  \\\n",
       "0     (array([ True]), array([0.02832808]), 0.050000...          Yes   \n",
       "1     (array([ True]), array([0.04579148]), 0.050000...          Yes   \n",
       "2     (array([ True]), array([8.19333766e-06]), 0.05...          Yes   \n",
       "3     (array([ True]), array([0.01362716]), 0.050000...          Yes   \n",
       "4     (array([ True]), array([0.04333231]), 0.050000...          Yes   \n",
       "...                                                 ...          ...   \n",
       "3958  (array([ True]), array([0.02164259]), 0.050000...          Yes   \n",
       "3959  (array([ True]), array([0.00263217]), 0.050000...          Yes   \n",
       "3960  (array([ True]), array([0.00177055]), 0.050000...          Yes   \n",
       "3961  (array([ True]), array([0.00107189]), 0.050000...          Yes   \n",
       "3962  (array([ True]), array([1.23752125e-15]), 0.05...          Yes   \n",
       "\n",
       "             Sex-biased  odds_ratio  Log2_Odds_ratio sex_biased_log_oddratio  \\\n",
       "0       sex-biased male    0.699466        -0.515673         sex-biased male   \n",
       "1     sex-biased female    2.180080         1.124381       sex-biased female   \n",
       "2     sex-biased female    2.748978         1.458896       sex-biased female   \n",
       "3     sex-biased female    4.576790         2.194336       sex-biased female   \n",
       "4     sex-biased female    5.081070         2.345132       sex-biased female   \n",
       "...                 ...         ...              ...                     ...   \n",
       "3958  sex-biased female    0.101973        -3.293734         sex-biased male   \n",
       "3959  sex-biased female    0.027176        -5.201535         sex-biased male   \n",
       "3960  sex-biased female    0.020381        -5.616655         sex-biased male   \n",
       "3961      no sex-biased    0.013586        -6.201700         sex-biased male   \n",
       "3962  sex-biased female    0.044078        -4.503794         sex-biased male   \n",
       "\n",
       "      Sum_ADRS  \n",
       "0          202  \n",
       "1           29  \n",
       "2           82  \n",
       "3           13  \n",
       "4            7  \n",
       "...        ...  \n",
       "3958        17  \n",
       "3959         6  \n",
       "3960         5  \n",
       "3961         4  \n",
       "3962        71  \n",
       "\n",
       "[3963 rows x 13 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_adrs_sexbias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_adrs_sexbias_without_top = df_adrs_sexbias[(df_adrs_sexbias[\"ADR\"]!= 10028813) & (df_adrs_sexbias[\"ADR\"]!= 10011906)\n",
    "               & (df_adrs_sexbias[\"ADR\"]!= 10053762) & (df_adrs_sexbias[\"ADR\"]!= 10012735)\n",
    "               & (df_adrs_sexbias[\"ADR\"]!= 10016256) & (df_adrs_sexbias[\"ADR\"]!= 10047700)]\n",
    "\n",
    "##without nausea, death, fatigue,off label use,vomiting, diarrhoea "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "drug_mapping = pd.read_csv(\"drug_mapping.csv\", sep=\";\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "adrs_info = pd.read_csv(\"adrs_information.txt\", sep=\"\\t\", dtype=str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "adrs_info['PT'] = pd.to_numeric(adrs_info['PT'], errors='coerce').astype('int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_drug_name = df_adrs_sexbias.merge(drug_mapping, left_on=\"Drug\", right_on=\"DrugBank ID\", how = \"left\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_drug_adr_name = df_drug_name.merge(adrs_info, left_on=\"ADR\", right_on='PT', how = \"left\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'Drug', 'ADR', 'Number adrs in Male',\n",
       "       'Number adrs in Female', 'P-value', 'p_value_FDR', 'Significance',\n",
       "       'Sex-biased', 'odds_ratio', 'Log2_Odds_ratio',\n",
       "       'sex_biased_log_oddratio', 'Sum_ADRS', 'Drug_name ', 'DrugBank ID',\n",
       "       'PT', 'PT_name', 'HLT', 'HLT_name', 'HLGT', 'HLGT_name', 'SOC',\n",
       "       'SOC_name', 'SOC_abbr'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_drug_adr_name.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_drug_adr_name_filter = df_drug_adr_name[[\"Drug\",\"Drug_name \",\"ADR\",\"PT_name\",\"SOC_name\",\"Number adrs in Male\",'Number adrs in Female', 'P-value', 'p_value_FDR', 'Significance',\n",
    "       'Sex-biased', 'odds_ratio', 'Log2_Odds_ratio',\n",
    "       'sex_biased_log_oddratio', 'Sum_ADRS']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Drug</th>\n",
       "      <th>Drug_name</th>\n",
       "      <th>ADR</th>\n",
       "      <th>PT_name</th>\n",
       "      <th>SOC_name</th>\n",
       "      <th>Number adrs in Male</th>\n",
       "      <th>Number adrs in Female</th>\n",
       "      <th>P-value</th>\n",
       "      <th>p_value_FDR</th>\n",
       "      <th>Significance</th>\n",
       "      <th>Sex-biased</th>\n",
       "      <th>odds_ratio</th>\n",
       "      <th>Log2_Odds_ratio</th>\n",
       "      <th>sex_biased_log_oddratio</th>\n",
       "      <th>Sum_ADRS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DB00398</td>\n",
       "      <td>sorafenib</td>\n",
       "      <td>10061428</td>\n",
       "      <td>decreased appetite</td>\n",
       "      <td>general disorders and administration site cond...</td>\n",
       "      <td>150</td>\n",
       "      <td>52</td>\n",
       "      <td>2.832808e-02</td>\n",
       "      <td>(array([ True]), array([0.02832808]), 0.050000...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>sex-biased male</td>\n",
       "      <td>0.699466</td>\n",
       "      <td>-0.515673</td>\n",
       "      <td>sex-biased male</td>\n",
       "      <td>202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DB00398</td>\n",
       "      <td>sorafenib</td>\n",
       "      <td>10055798</td>\n",
       "      <td>haemorrhage</td>\n",
       "      <td>vascular disorders</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>4.579148e-02</td>\n",
       "      <td>(array([ True]), array([0.04579148]), 0.050000...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>sex-biased female</td>\n",
       "      <td>2.180080</td>\n",
       "      <td>1.124381</td>\n",
       "      <td>sex-biased female</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DB00398</td>\n",
       "      <td>sorafenib</td>\n",
       "      <td>10001760</td>\n",
       "      <td>alopecia</td>\n",
       "      <td>skin and subcutaneous tissue disorders</td>\n",
       "      <td>35</td>\n",
       "      <td>47</td>\n",
       "      <td>8.193338e-06</td>\n",
       "      <td>(array([ True]), array([8.19333766e-06]), 0.05...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>sex-biased female</td>\n",
       "      <td>2.748978</td>\n",
       "      <td>1.458896</td>\n",
       "      <td>sex-biased female</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DB00398</td>\n",
       "      <td>sorafenib</td>\n",
       "      <td>10018388</td>\n",
       "      <td>glossodynia</td>\n",
       "      <td>gastrointestinal disorders</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>1.362716e-02</td>\n",
       "      <td>(array([ True]), array([0.01362716]), 0.050000...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>sex-biased female</td>\n",
       "      <td>4.576790</td>\n",
       "      <td>2.194336</td>\n",
       "      <td>sex-biased female</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DB00398</td>\n",
       "      <td>sorafenib</td>\n",
       "      <td>10043942</td>\n",
       "      <td>tongue blistering</td>\n",
       "      <td>gastrointestinal disorders</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4.333231e-02</td>\n",
       "      <td>(array([ True]), array([0.04333231]), 0.050000...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>sex-biased female</td>\n",
       "      <td>5.081070</td>\n",
       "      <td>2.345132</td>\n",
       "      <td>sex-biased female</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3958</th>\n",
       "      <td>DB12332</td>\n",
       "      <td>rucaparib</td>\n",
       "      <td>10010305</td>\n",
       "      <td>confusional state</td>\n",
       "      <td>nervous system disorders</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>2.164259e-02</td>\n",
       "      <td>(array([ True]), array([0.02164259]), 0.050000...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>sex-biased female</td>\n",
       "      <td>0.101973</td>\n",
       "      <td>-3.293734</td>\n",
       "      <td>sex-biased male</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3959</th>\n",
       "      <td>DB12332</td>\n",
       "      <td>rucaparib</td>\n",
       "      <td>10061818</td>\n",
       "      <td>disease progression</td>\n",
       "      <td>general disorders and administration site cond...</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2.632173e-03</td>\n",
       "      <td>(array([ True]), array([0.00263217]), 0.050000...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>sex-biased female</td>\n",
       "      <td>0.027176</td>\n",
       "      <td>-5.201535</td>\n",
       "      <td>sex-biased male</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3960</th>\n",
       "      <td>DB12332</td>\n",
       "      <td>rucaparib</td>\n",
       "      <td>10066274</td>\n",
       "      <td>cytopenia</td>\n",
       "      <td>blood and lymphatic system disorders</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1.770550e-03</td>\n",
       "      <td>(array([ True]), array([0.00177055]), 0.050000...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>sex-biased female</td>\n",
       "      <td>0.020381</td>\n",
       "      <td>-5.616655</td>\n",
       "      <td>sex-biased male</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3961</th>\n",
       "      <td>DB12332</td>\n",
       "      <td>rucaparib</td>\n",
       "      <td>10043071</td>\n",
       "      <td>tachycardia</td>\n",
       "      <td>cardiac disorders</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1.071887e-03</td>\n",
       "      <td>(array([ True]), array([0.00107189]), 0.050000...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>no sex-biased</td>\n",
       "      <td>0.013586</td>\n",
       "      <td>-6.201700</td>\n",
       "      <td>sex-biased male</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3962</th>\n",
       "      <td>DB12332</td>\n",
       "      <td>rucaparib</td>\n",
       "      <td>10076476</td>\n",
       "      <td>product use in unapproved indication</td>\n",
       "      <td>injury, poisoning and procedural complications</td>\n",
       "      <td>16</td>\n",
       "      <td>55</td>\n",
       "      <td>1.237521e-15</td>\n",
       "      <td>(array([ True]), array([1.23752125e-15]), 0.05...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>sex-biased female</td>\n",
       "      <td>0.044078</td>\n",
       "      <td>-4.503794</td>\n",
       "      <td>sex-biased male</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3963 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Drug Drug_name        ADR                               PT_name  \\\n",
       "0     DB00398  sorafenib  10061428                    decreased appetite   \n",
       "1     DB00398  sorafenib  10055798                           haemorrhage   \n",
       "2     DB00398  sorafenib  10001760                              alopecia   \n",
       "3     DB00398  sorafenib  10018388                           glossodynia   \n",
       "4     DB00398  sorafenib  10043942                     tongue blistering   \n",
       "...       ...        ...       ...                                   ...   \n",
       "3958  DB12332  rucaparib  10010305                     confusional state   \n",
       "3959  DB12332  rucaparib  10061818                   disease progression   \n",
       "3960  DB12332  rucaparib  10066274                             cytopenia   \n",
       "3961  DB12332  rucaparib  10043071                           tachycardia   \n",
       "3962  DB12332  rucaparib  10076476  product use in unapproved indication   \n",
       "\n",
       "                                               SOC_name  Number adrs in Male  \\\n",
       "0     general disorders and administration site cond...                  150   \n",
       "1                                    vascular disorders                   14   \n",
       "2                skin and subcutaneous tissue disorders                   35   \n",
       "3                            gastrointestinal disorders                    4   \n",
       "4                            gastrointestinal disorders                    2   \n",
       "...                                                 ...                  ...   \n",
       "3958                           nervous system disorders                    2   \n",
       "3959  general disorders and administration site cond...                    2   \n",
       "3960               blood and lymphatic system disorders                    2   \n",
       "3961                                  cardiac disorders                    2   \n",
       "3962     injury, poisoning and procedural complications                   16   \n",
       "\n",
       "      Number adrs in Female       P-value  \\\n",
       "0                        52  2.832808e-02   \n",
       "1                        15  4.579148e-02   \n",
       "2                        47  8.193338e-06   \n",
       "3                         9  1.362716e-02   \n",
       "4                         5  4.333231e-02   \n",
       "...                     ...           ...   \n",
       "3958                     15  2.164259e-02   \n",
       "3959                      4  2.632173e-03   \n",
       "3960                      3  1.770550e-03   \n",
       "3961                      2  1.071887e-03   \n",
       "3962                     55  1.237521e-15   \n",
       "\n",
       "                                            p_value_FDR Significance  \\\n",
       "0     (array([ True]), array([0.02832808]), 0.050000...          Yes   \n",
       "1     (array([ True]), array([0.04579148]), 0.050000...          Yes   \n",
       "2     (array([ True]), array([8.19333766e-06]), 0.05...          Yes   \n",
       "3     (array([ True]), array([0.01362716]), 0.050000...          Yes   \n",
       "4     (array([ True]), array([0.04333231]), 0.050000...          Yes   \n",
       "...                                                 ...          ...   \n",
       "3958  (array([ True]), array([0.02164259]), 0.050000...          Yes   \n",
       "3959  (array([ True]), array([0.00263217]), 0.050000...          Yes   \n",
       "3960  (array([ True]), array([0.00177055]), 0.050000...          Yes   \n",
       "3961  (array([ True]), array([0.00107189]), 0.050000...          Yes   \n",
       "3962  (array([ True]), array([1.23752125e-15]), 0.05...          Yes   \n",
       "\n",
       "             Sex-biased  odds_ratio  Log2_Odds_ratio sex_biased_log_oddratio  \\\n",
       "0       sex-biased male    0.699466        -0.515673         sex-biased male   \n",
       "1     sex-biased female    2.180080         1.124381       sex-biased female   \n",
       "2     sex-biased female    2.748978         1.458896       sex-biased female   \n",
       "3     sex-biased female    4.576790         2.194336       sex-biased female   \n",
       "4     sex-biased female    5.081070         2.345132       sex-biased female   \n",
       "...                 ...         ...              ...                     ...   \n",
       "3958  sex-biased female    0.101973        -3.293734         sex-biased male   \n",
       "3959  sex-biased female    0.027176        -5.201535         sex-biased male   \n",
       "3960  sex-biased female    0.020381        -5.616655         sex-biased male   \n",
       "3961      no sex-biased    0.013586        -6.201700         sex-biased male   \n",
       "3962  sex-biased female    0.044078        -4.503794         sex-biased male   \n",
       "\n",
       "      Sum_ADRS  \n",
       "0          202  \n",
       "1           29  \n",
       "2           82  \n",
       "3           13  \n",
       "4            7  \n",
       "...        ...  \n",
       "3958        17  \n",
       "3959         6  \n",
       "3960         5  \n",
       "3961         4  \n",
       "3962        71  \n",
       "\n",
       "[3963 rows x 15 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_drug_adr_name_filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "74"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_drug_adr_name_filter[\"Drug_name \"].unique())\n",
    "## de los 95 farmacos seleccionados, tenemos ADRs para 74 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1342"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_drug_adr_name_filter[\"PT_name\"].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "drug_adrs = df_drug_adr_name_filter[[\"Drug_name \",\"PT_name\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "drug_adrs_group = drug_adrs.groupby(\"Drug_name \").count().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Drug_name</th>\n",
       "      <th>PT_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>abiraterone</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>afatinib</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>anagrelide</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>anastrozole</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>axitinib</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>vemurafenib</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>vinblastine</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>vincristine</td>\n",
       "      <td>104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>vinorelbine</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>vorinostat</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>74 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Drug_name   PT_name\n",
       "0   abiraterone       22\n",
       "1      afatinib        2\n",
       "2    anagrelide        2\n",
       "3   anastrozole       25\n",
       "4      axitinib       17\n",
       "..          ...      ...\n",
       "69  vemurafenib        2\n",
       "70  vinblastine        9\n",
       "71  vincristine      104\n",
       "72  vinorelbine        8\n",
       "73   vorinostat        3\n",
       "\n",
       "[74 rows x 2 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drug_adrs_group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "drug_adrs_group = drug_adrs_group.sort_values(by='PT_name', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 12))\n",
    "\n",
    "#sns.set_palette( my_colors )\n",
    "sns.barplot(data=drug_adrs_group, x=\"PT_name\", y=\"Drug_name \", orient='h').set_title('Number of ADRs by cancer drug')\n",
    "#plt.xticks(rotation=90)\n",
    "sns.despine(top=False, bottom=True)\n",
    "ax.bar_label(ax.containers[0])\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel(\"\")\n",
    "#ax.legend(loc=(0.6,0), title='', fontsize=12)\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"adrs_by_drugs.svg\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PT_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>74.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>53.554054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>93.011288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>20.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>62.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>599.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          PT_name\n",
       "count   74.000000\n",
       "mean    53.554054\n",
       "std     93.011288\n",
       "min      1.000000\n",
       "25%      7.000000\n",
       "50%     20.500000\n",
       "75%     62.500000\n",
       "max    599.000000"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drug_adrs_group.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#### volcano plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-41-dd12ffe0921a>:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_drug_adr_name_filter['log_p_values'] = -np.log10(df_drug_adr_name_filter['P-value'])\n",
      "<ipython-input-41-dd12ffe0921a>:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_drug_adr_name_filter['log_odd_ratios'] = np.log2(df_drug_adr_name_filter['odds_ratio'])\n"
     ]
    },
    {
     "data": {
      "image/png": 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ve9/7Xt10001qbm6WJK1atUq7d+9O67WSbaPJkydLUqfv+Z73vOdAt0/J9mFf9evUCwAAAMCgV1lpY/Qka9FraLBxe1dc0etV7tq1S9dcc43q6+tVWFioI488UjfffLOKiop011136XOf+5waGhrU0tKiL3zhCyosLNQvfvELPfvssxo5cqTOPPNMXX/99SmnGDjllFN0wQUXaP369brkkkvadeGUpI9//OM677zzdNxxx2n27Nk65phjJFkr3Je//GUVFBRo6NChuummmw6U56qrrtK11157oDwzZszQr3/9a11++eVyzh3oCpls4cKFevDBB9st+/CHP6yFCxfq1FNP1V//+ledcMIJB6Ze+MlPfqK5c+emXNe3vvUt/eM//qM+/elPq6Cg6zawf/qnf9LatWt14oknynuvcePG6Z577unyNYnmz5+vj3zkIyovL9e73vWuA+P9Ev3kJz/RVVddpYqKCrW0tOjMM8/U//zP/6T9Hqm45H6s+WT27Nk+eYDoYDZQUxGj59iX8cB+jAf2Y3ywL+NhoO7HFStWtOsu2K2aGhujV1trLXqVlb0er5ePupt6AamlOs6cc4u997NTPZ+WPQAAAKC/VVQMquAOucGYPQAAAACIIYI9AAAAAIghunECGTDIu90DAABgAKJlD+ijmhpLqFVXZ1Pm1NXZ/zU1uS4ZAAAABjOCPaCPqqqk8nL7KSiI/q6qynXJAAAAMJgR7AF9VFtrU+QkKiuz5QAAAP3le9/7nmbMmKGKigrNmjXrwMTlfXHLLbfo6quvTvnY6aef3uf1d2bt2rWaOXNmp4//+Mc/VklJiRoSJqevrq5WWVmZTjjhBB199NE688wz9cADDxx4fP78+Zo8ebJmzZqlt73tbbr99ttTrnv+/PlyzunVV19t937OOXU37ducOXO6fU5/ItgD+mjaNJsLNVFDgy0HAADoD88884weeOABLVmyRDU1NXr00Uc1derUrL7n008/ndX1d+X222/XySefrKqkrlRnnHGGXnjhBa1cuVI/+clPdPXVV+uxxx478PgXv/hFLV26VPfee6/++Z//Wc3NzSnXf9xxx2nhwoUH/r/zzjs1Y8aM7HyYLCLYA/qostLG6dXVSa2t0d+VlbkuGQAAGKhqaqT586XLL7fffR3rv3HjRo0dO1bFxcWSpLFjx+rggw+WJC1evFjvfOc7ddJJJ+m9732vNm7cqIaGBh199NFauXKlJOljH/uYfv7zn6dc97p16zRnzhwdddRR+s53vnNg+YgRIyRJu3bt0ty5c3XiiSfquOOO07333itJ2r17tz7wgQ/o+OOP18yZM/WHP/zhQHne9773tStPWH788cfr+OOP189+9rNOP+trr72mXbt26frrr++0dU6SZs2apW9961u64YYbOjx21FFHqbS0VHV1dSlf+6EPfejA53jttddUVlamsWPHHnj8M5/5jGbPnq0ZM2bo29/+dsp1/PnPf9bb3/52nXjiifrIRz6iXbt2dVrWbCHYA/qookKaN8/G6a1fb7/nzSMbJwAASC0byd3e8573aN26dZo+fbo++9nP6oknnpAkNTc365prrtFdd92lxYsX6/LLL9d1112nsrIy3XDDDbr00ku1cOFC1dXV6dOf/nTKdT/77LP64x//qJqaGt15550duimWlJTo7rvv1pIlS/T444/r2muvlfdeDz/8sA4++GC9+OKLWrZsmc4555wD5fntb3/brjySdNlll+mnP/2pXnzxxS4/68KFC3XxxRfrjDPO0MqVK7Vp06ZOn3viiSfqlVde6bB8yZIlOuqoozR+/PiUrxs1apSmTp2qZcuWaeHChfroRz/a7vHvfe97ev7551VTU6MnnnhCNUk7b+vWrbr++uv16KOPasmSJZo9e7b+67/+q8vPlQ1MvQBkQEUFwR0AAEhPYnI3KfpdVdX7+sSIESO0ePFi/fWvf9Xjjz+uj370o/rBD36g2bNna9myZTr77LMlSfv379ekSZMkSWeffbbuvPNOXXXVVV0GWGeffbbGjBkjSaqsrNRTTz2l2bNnH3jce6+vf/3revLJJ1VQUKANGzZo06ZNOu6443TttdfqK1/5is4991ydccYZWrZsmZYtW6bzzz9fBQUFB8pTX1+v+vp6nXnmmZKkT3ziE3rooYdSluf222/X3XffrYKCAl1wwQW68847Ox1X6L1v9/+PfvQj/frXv9aqVat0//33d7lNL774Yi1cuFB/+tOf9Nhjj+nXv/71gcfuuOMO3XzzzWppadHGjRv18ssvqyJh5y1atEgvv/yy3vGOd0iSmpqa9Pa3v73L98sGgj0AAACgH9XWWoteokwkdxsyZIjmzJmjOXPm6LjjjtOtt96qk046STNmzNAzzzzT4fmtra1asWLFge6MU6ZM0d13332gq+YvfvELSZJzrt3rkv+/7bbbtGXLFi1evFhDhw7VoYceqsbGRk2fPl1LlizRgw8+qG984xuaO3euPvzhD2vGjBn685//rJEjRx5YR319fVqf8aWXXtLq1asPBK9NTU067LDDOg32XnjhBR177LEH/v/iF7+oefPm6b777tMVV1yh1157TSUlJSlfe+655+rLX/6yZs+erVGjRh1Y/vrrr2vBggV67rnnVF5erksvvVSNjY3tXuu919lnn91lN9P+QDdOAAAAoB9lI7nbypUrtXr16gP/L126VIcccoiOPvpobdmy5UCw19zcrOXLl0uyVq5jjz1Wv//973XZZZepublZH/7wh7V06VItXbr0QOvdI488ou3bt2vv3r265557DrRWRWVv0Pjx4zV06FA9/vjjeuONNyRJb775pkpLS3XJJZfoy1/+spYsWXKgPCFTaCjP6NGjNXr0aD311FOSLIBM5fbbb9f8+fO1du1arV27Vm+++abefPPNA++ZqKamRt/97nd11VVXdXjsgx/8oGbPnq1bb721021aWlqqf//3fz/QzTTYsWOHhg8frrKyMm3atCllC+Rpp52mv/3tbwcyeu7evVurVq3q9L2yhZY9AACAwaamxvoM1tZahFFZyXiEflRZaWP0JGvRa2iwcXtXXNH7de7atUvXXHON6uvrVVhYqCOPPFI333yzioqKdNddd+lzn/ucGhoa1NLSoi984QsqLCzUL37xCz377LMaOXKkzjzzTF1//fXtErAEp5xyii644AKtX79el1xySbsunJL08Y9/XOedd56OO+44zZ49W8ccc4wka4X78pe/rIKCAg0dOlQ33XTTgfJcddVVuvbaaw+UZ8aMGfr1r3+tyy+/XM45vec970n5ORcuXKgHH3yw3bIPf/jDWrhwoU499VT99a9/1QknnKA9e/Zo/Pjx+slPfqK5c+emXNe3vvUt/eM//qM+/elPq6AgdRvYxRdf3GHZ8ccfrxNOOEHHHHOMpk6d2iH4laRx48bplltu0cc+9jHt27dPknT99ddr+vTpKd8nW1xyP9Z8Mnv2bD+Q5rHIterqas2ZMyfXxUAGsC/jgf0YD+zH+GBftgnZQcrL20caeZJdbKDuxxUrVrTrLtidwR5v79y5s103TqQn1XHmnFvsvZ+d6vm07AEAAAwm2cgOgh4juRv6A2P2AAAABpPaWmvRS5SJ7CAABhyCPQAAgMEkG9lBIKljmn8gk3pzfBHsAQAADCaVlTZGr65Oam2N/q6szHXJ8lpJSYm2bdtGwIes8N5r27ZtnU4T0RnG7AEAAAwmFRWWjCUxO8gVVzCArI+mTJmi9evXa8uWLbkuSl5obGzsceAy2JWUlGhK8gSN3SDYAwAAGGzIDpJxQ4cO1WGHHZbrYuSN6upqnXDCCbkuRuzRjRMAAAAAYohgDwAAAABiiGAPAAAAAGKIYA8AAAAAYohgDwAAAABiiGAPAAAAAGKIYA8AAAAAYohgDwAAAABiiGAPAAAAAGKIYA8AAAAAYohgDwAAAABiiGAPAAAAAGKIYA8AAAAAYohgDwAAAABiiGAPAAAAAGKIYA8AAAAAYohgDwAAAABiiGAPAAAAAGKIYA8AAAAAYohgDwAAAABiiGAPAAAAAGKIYA8AAAAAYohgDwAAAABiiGAPAAAAAGKIYA8AAAAAYohgDwAAAABiiGAPAAAAAGKIYA8AAAAAYohgDwAAAABiiGAPAAAAAGKIYA8AAAAAYohgDwAAAABiiGAPAAAAAGKIYA8AAAAAYohgDwAAAABiiGAPAAAAAGKIYA8AAAAAYohgDwAAAABiiGAPAAAAAGKIYA8AAAAAYohgDwAAAABiKGvBnnPuV865zc65ZQnL/tM594pzrsY5d7dzbnTCY19zzr3qnFvpnHtvtsoFAAAAAINBNlv2bpF0TtKyRyTN9N5XSFol6WuS5Jx7m6SLJc1oe82NzrkhWSwbAAAAAMRa1oI97/2TkrYnLfuz976l7d9Fkqa0/X2+pIXe+33e+9clvSrplGyVDQAAAADiznnvs7dy5w6V9ID3fmaKx+6X9Afv/e+cczdIWuS9/13bY7+U9JD3/q4Ur7tS0pWSNGHChJMWLlyYtfLnm127dmnEiBG5LgYygH0ZD+zHeGA/xgf7Mh7Yj/HAfsycs846a7H3fnaqxwr7uzCS5Jy7TlKLpNt6+lrv/c2Sbpak2bNn+zlz5mS2cHmsurpabI94YF/GA/sxHtiP8cG+jAf2YzywH/tHvwd7zrlLJZ0raa6PmhU3SJqa8LQpbcsAAAAAAL3Qr1MvOOfOkfSvkj7ovd+T8NB9ki52zhU75w6TdJSkZ/uzbAAAAAAQJ1lr2XPO3S5pjqSxzrn1kr4ty75ZLOkR55xk4/T+xXu/3Dl3h6SXZd07r/Le789W2QAAAAAg7rIW7HnvP5Zi8S+7eP73JH0vW+UBAAAAgMGkX7txAgAAAAD6B8EeAAAAAMQQwR4AAAAAxBDBHgAAAADEEMEeAAAAAMQQwR4AAAAAxBDBHgAAAADEEMEeAAAAAMQQwR4AAAAAxBDBHgAAAADEEMEeAAAAAMQQwR4AAAAAxBDBHgAAAADEEMEeAAAAAMQQwR4AAAAAxFBhrgsAAANBTY1UVSXV1krTpkmVlVJFRa5LBQAA0Hu07AEY9GpqpAULpLo6acoU+71ggS0HAADIVwR7AAa9qiqpvNx+Cgqiv6uqcl0yAACA3iPYAzDo1dZKZWXtl5WV2XIAAIB8RbAHYNCbNk1qaGi/rKHBlgMAAOQrgj0Ag15lpY3Tq6uTWlujvysrc10yAACA3iPYAzDoVVRI8+bZOL316+33vHlk4wQAAPmNqRcAQBbYEdwBAIA4oWUPAAAAAGKIYA8AAAAAYohgDwAAAABiiGAPAAAAAGKIYA8AAAAAYohgDwAAAABiiGAPAAAAAGKIYA8AAAAAYohgDwAAAABiiGAPAAAAAGKIYA8AAAAAYohgDwAAAABiiGAPAAAAAGKIYA8AAAAAYohgDwAAAABiiGAPAAAAAGKIYA8AAAAAYohgDwAAAABiiGAPAAAAAGKIYA8AAAAAYohgDwAAAABiiGAPAAAAAGKIYA8AAAAAYohgDwAAAABiiGAPAAAAAGKIYA8AAAAAYohgDwAAAABiiGAPAAAAAGKIYA8AAAAAYohgDwAAAABiiGAPAAAAAGKIYA8AAAAAYohgDwAAAABiiGAPAAAAAGKIYA8AAAAAYohgDwAAAABiiGAPAAAAAGKIYA8AAAAAYohgDwAAAABiiGAPAAAAAGKIYA8AAAAAYihrwZ5z7lfOuc3OuWUJyw5yzj3inFvd9ru8bblzzv3EOfeqc67GOXditsoFAAAAAINBNlv2bpF0TtKyr0p6zHt/lKTH2v6XpPdJOqrt50pJN2WxXAAAAAAQe1kL9rz3T0ranrT4fEm3tv19q6QPJSz/jTeLJI12zk3KVtkAAAAAIO6c9z57K3fuUEkPeO9ntv1f770f3fa3k1TnvR/tnHtA0g+890+1PfaYpK94759Psc4rZa1/mjBhwkkLFy7MWvnzza5duzRixIhcFwMZwL6MB/ZjPLAf44N9GQ/sx3hgP2bOWWedtdh7PzvVY4X9XZjAe++dcz2ONL33N0u6WZJmz57t58yZk+mi5a3q6mqxPeKBfRkP7Md4YD/GB/syHtiP8cB+7B/9nY1zU+ie2fZ7c9vyDZKmJjxvStsyAAAAAEAv9Hewd5+kT7X9/SlJ9yYs/2RbVs7TJDV47zf2c9kAAAAAIDay1o3TOXe7pDmSxjrn1kv6tqQfSLrDOXeFpDckXdT29AclvV/Sq5L2SLosW+UCAAAAgMEga8Ge9/5jnTw0N8VzvaSrslUWAAAAABhscpagBQAA9EFNjVRVJdXWStOmSZWVUkVFrksFABhA+nvMHgAA6KuaGmnBAqmuTpoyxX4vWGDLAQBoQ7AHAEC+qaqSysvtp6Ag+ruqKtclAwAMIAR7AADkm9paqays/bKyMlsOAEAbgj0AAPLNtGlSQ0P7ZQ0NthwAgDYEewAA5JvKShunV1cntbZGf1dW5rpkAIABhGAPAIB8U1EhzZtn4/TWr7ff8+aRjRMA0A5TLwAAkI8qKgjuAABdomUPAAAAAGKIYA8AAAAAYohgDwAAAABiiGAPAAAAAGKIYA8AAAAAYohgDwAAAABiiGAPAAAAAGKIYA8AAAAAYohgDwAAAABiiGAPAAAAAGKIYA8AAAAAYohgDwAAAABiiGAPAAAAAGKIYA8AAAAAYohgDwAAAABiiGAPAAAAAGKIYA8AAAAAYohgDwAAAABiiGAPAAAAAGKIYA8AAAAAYohgDwAAAABiiGAPAAAAAGKIYA8AAAAAYohgDwAAAABiiGAPAAAAAGKIYA8AAAAAYohgDwAAAABiiGAPAAAAAGKIYA8AAAAAYohgDwAAAABiiGAPAAAAAGKIYA8AAAAAYohgDwAAAABiiGAPAAAAAGKIYA8AAAAAYohgDwAAAABiiGAPAAAAAGKIYA8AAAAAYohgDwAAAABiiGAPAAAAAGKIYA8AAAAAYohgDwAAAABiiGAPAAAAAGKIYA8AAAAAYohgDwAAAABiiGAPAAAAAGKIYA8AAAAAYohgDwAAAABiiGAPAAAAAGKIYA8AAAAAYohgDwAAAABiiGAPAAAAAGKIYA8AAAAAYohgDwAAAABiiGAPAAAAAGKIYA8AAAAAYignwZ5z7ovOueXOuWXOududcyXOucOcc393zr3qnPuDc64oF2UDAAAAgDjo92DPOTdZ0uckzfbez5Q0RNLFkv5d0o+890dKqpN0RX+XDQAAAADiIlfdOAslDXPOFUoqlbRR0rsk3dX2+K2SPpSbogEAAABA/nPe+/5/U+c+L+l7kvZK+rOkz0ta1NaqJ+fcVEkPtbX8Jb/2SklXStKECRNOWrhwYb+Ve6DbtWuXRowYketiIAPYl/HAfowH9mN8sC/jgf0YD+zHzDnrrLMWe+9np3qssL8L45wrl3S+pMMk1Uu6U9I56b7ee3+zpJslafbs2X7OnDmZL2Seqq6uFtsjHtiX8cB+jAf2Y3ywL+OB/RgP7Mf+kYtunO+W9Lr3fov3vllSlaR3SBrd1q1TkqZI2pCDsgEAAABALPR7y56kWkmnOedKZd0450p6XtLjki6UtFDSpyTdm4OyAQBypKZGqqqSamuladOkykqpoiLXpQIAIH/1e8ue9/7vskQsSyS91FaGmyV9RdKXnHOvShoj6Zf9XTYAQG7U1EgLFkh1ddKUKfZ7wQJbDgAAeicXLXvy3n9b0reTFq+RdEoOigMAyLGqKqm83H6k6HdVFa17AAD0Vq6mXgAA4IDaWqmsrP2ysjJbDgAAeietlj3n3HhZEpWDZePslkl63nvfmsWyAQAGiWnTrOtmaNGTpIYGWw4AAHqny5Y959xZzrk/Sfo/Se+TNEnS2yR9Q9JLzrnvOOdGZb+YAIA4q6y0YK+uTmptjf6urMx1yQAAyF/dtey9X9KnvfcdOtK0TZNwrqSzJf0xC2UDAAwSFRXSvHnts3FecQXj9QAA6Isugz3v/Ze7eKxF0j2ZLhAAYHCqqCC4AwAgk9JK0OKcm+Cc+6Vz7uG2/9/mnLsiu0UDAAAAAPRWutk4b5H0J9mYPUlaJekLWSgPAAAAACAD0g32xnrv75DUKh3owrk/a6UCAAAAAPRJusHebufcGElekpxzp0lqyFqpAAAAAAB9ktY8e5K+JOk+SUc45/4maZykC7NWKgAAAABAn6QV7Hnvlzjn3inpaElO0krvfXNWSwYAAAAA6LW0gj3n3CeTFp3onJP3/jdZKBMAAAAAoI/S7cZ5csLfJZLmSloiiWAPAAAAAAagdLtxXpP4v3NutKSF2SgQAAAAAKDv0s3GmWy3pMMyWRAAAAAAQOakO2bvfrVNuyALEN8m6Y5sFQoAAAAA0DfpjtlbkPB3i6Q3vPfrs1AeAADQn2pqpKoqqbZWmjZNqqyUKipyXSoAQAak1Y3Te/9Ews/fCPQAAIiBmhppwQKprk6aMsV+L1hgywEAea/Llj3n3E5F3TfbPSTJe+9HZaVUAAAg+6qqpPJy+5Gi31VVtO4BQAx0Gex570f2V0EAAEA/q621Fr1EZWW2HACQ99IdsydJcs6Nl82zJ0ny3nM1AAAgX02bZl03Q4ueJDU02HIAQN5La8yec+6DzrnVkl6X9ISktZIeymK5AABAtlVWWrBXVye1tkZ/V1bmumQAgAxId56970o6TdIq7/1hkuZKWpS1UgEAgOyrqJDmzbOWvfXr7fe8eYzXA4CYSLcbZ7P3fptzrsA5V+C9f9w59+NsFgwAAPSDigqCOwCIqXSDvXrn3AhJT0q6zTm3WdLu7BULAAAAANAX6XbjPF/SHklflPSwpNcknZetQgEAAAAA+ibdlr1/lvQH7/0GSbdmsTwAAAAAgAxIt2VvpKQ/O+f+6py72jk3IZuFAgAAAAD0TVrBnvf+O977GZKukjRJ0hPOuUezWjIAAAAAQK+l27IXbJb0lqRtksZnvjgAAAAAgExId1L1zzrnqiU9JmmMpE9778nTDAAAAAADVLoJWqZK+oL3fmkWywIAAAAAyJB0x+x9LQR6zrmbs1oiAAAAAECf9XTMniTNzngpAAAAAAAZ1Ztgb3PGSwEAAAAAyKgeBXvOuVGSPpKlsgAAAAAAMiTdbJwnO+deklQj6SXn3IvOuZOyWzQAAAAAQG+lm43zl5I+673/qyQ55/5B0q8lMf0CAAAAAAxA6Xbj3B8CPUny3j8lqSU7RQIAAAAA9FW6LXtPOOf+V9Ltkrykj0qqds6dKEne+yVZKh8AAAAAoBfSDfaOb/v97aTlJ8iCv3dlrEQAAAAAgD5LK9jz3p+V7YIAAAAAADKnyzF7zrlLnHOdPsc5d0RbshYAAAAAwADSXcveGEkvOOcWS1osaYukEklHSnqnpK2SvprVEgIAAAAAeqzLYM97/9/OuRtkY/LeIZtqYa+kFZI+4b2vzX4RAQAAAAA91e2YPe/9fkmPtP0AAAAAAPJAl8Gec65Q0hWSPiRpctviDZLulfRL731zVksHAAAAAOiV7lr2fiupXtJ3JK1vWzZF0qck/U423x4AAAAAYIDpLtg7yXs/PWnZekmLnHOrslQmAAAAAEAfdTn1gqTtzrmPJE6/4JwrcM59VFJddosGAAAAAOit7oK9iyVdKGmTc25VW2veW5Iq2x4DAAAAAAxA3U29sFZt4/Kcc2Palm3LfrEAAAByoKZGqqqSamuladOkykqpoiLXpQKAXumuZe8A7/22xEDPOXd2dooEAACQAzU10oIFUl2dNGWK/V6wwJYDQB5KO9hL4ZcZKwUAAECuVVVJ5eX2U1AQ/V1VleuSAUCvdDfP3n2dPSRpTOaLAwAAkCO1tdail6iszJYDQB7qbuqFMyRdImlX0nIn6ZSslAgAACAXpk2zrpvl5dGyhgZbDgB5qLtgb5GkPd77J5IfcM6tzE6RAAAAcqCy0sboSdai19Bgwd8VV+S2XADQS12O2fPev897/3gnj52ZnSIBAADkQEWFNG+eteytX2+/580jGyeAvNVdyx4AAMDgUVFBcAcgNtIK9pxzOyX5pMUNkp6XdK33fk2mCwYAAAAA6L10W/Z+LGm9pN/LkrNcLOkISUsk/UrSnCyUDQAAAADQS+nOs/dB7/3/eu93eu93eO9vlvRe7/0fJJV392IAAAAAQP9KN9jb45y7yDlX0PZzkaTGtseSu3cCAAAAAHIs3WDv45I+IWlz288nJF3inBsm6eoslQ0AAAAA0EtpjdlrS8ByXicPP5W54gAAAAAAMiGtlj3n3BTn3N3Ouc1tP390zk3p7Zs650Y75+5yzr3inFvhnHu7c+4g59wjzrnVbb8ZCwgAAAAAvZRuN85fS7pP0sFtP/e3Leut/5b0sPf+GEnHS1oh6auSHvPeHyXpsbb/AQAAAAC9kG6wN857/2vvfUvbzy2SxvXmDZ1zZZLOlPRLSfLeN3nv6yWdL+nWtqfdKulDvVk/AAAAAEBy3nefTNM595isJe/2tkUfk3SZ935uj9/QuVmSbpb0sqxVb7Gkz0va4L0f3fYcJ6ku/J/0+islXSlJEyZMOGnhwoU9LUJs7dq1SyNGjMh1MZAB7Mt4YD/GA/sxPtiX8cB+jAf2Y+acddZZi733s1M9lm6wd4ikn0p6u2yqhaclfc57X9vTwjjnZktaJOkd3vu/O+f+W9IOSdckBnfOuTrvfZfj9mbPnu2ff/75nhYhtqqrqzVnzpxcFwMZwL6MB/ZjPLAf44N9GQ/sx3hgP2aOc67TYC/dbJxvSPpghsqzXtJ67/3f2/6/SzY+b5NzbpL3fqNzbpJsigcAAAAAQC90Gew5536qLiZN995/rqdv6L1/yzm3zjl3tPd+paS5si6dL0v6lKQftP2+t6frBgAAAACY7lr2UvWRnCRpYx/f9xpJtznniiStkXSZLFnMHc65KyS9IemiPr4HAAAAAAxaXQZ73vtbk5c555Z470/sy5t675dKStWvtMcJXwAgl2pqpKoqqbZWmjZNqqyUKipyXSoAAID0p15I5DJeCgDIQzU10oIFUl2dNGWK/V6wwJYDAADkWm+CvZ9nvBQAkIeqqqTycvspKIj+rqrKdckAAAB6Eex572/MRkEAIN/U1kplZe2XlZXZcgAAgFzrTcseAEA2Rq+hof2yhgZbDgAAkGsEewDQS5WVNk6vrk5qbY3+rqzMdckAAAAI9gCg1yoqpHnzbJze+vX2e948snECAICBobt59gAAXaioILgDAAADEy17AAAAABBDBHsAAAAAEEN04wT6SU2Nzb9WW2vZGisr6f4HIE9wAgOAvETLHtAPamqkBQssU+OUKfZ7wQJbDgADGicwAMhbBHtAP6iqskyN5eVSQUH0d1VVrksGAN3gBAYAeYtgD+gHtbVSWVn7ZWVlthwABjROYACQtwj2gH4wbZrU0NB+WUODLQeAAY0TGADkLYI9oB9UVtowl7o6qbU1+ruyMtclA4BucAIDgLxFsAf0g4oKad48G+ayfr39njePZHYA8gAnMADIW0y9APSTigrqRgDyFCcwAMhLtOwBAAAAQAwR7AEAAABADBHsAQAAAEAMEewBAAAAQAyRoAUYBGpqpKoqmwN52jTLmE6uBQAAgHijZQ+IuZoaacECmxZryhT7vWCBLQcAAEB8EewBMVdVZdNilZdLBQXR31VVuS4ZAAAAsolgD4i52lqprKz9srIyWw4AAID4ItgDYm7aNKmhof2yhgZbDgAAgPgi2ANirrLSxunV1UmtrdHflZW5LhkAAACyiWAPiLmKCmnePBunt369/Z43j2ycAAAAccfUC8AgUFFBcAcAADDY0LIHAAAAADFEsAcAAAAAMUSwBwAAAAAxRLAHAAAAADFEghYAPVJTI1VV2aTs06bZFA4kfwEAABh4aNkDkLaaGmnBApunb8oU+71ggS0HAADAwEKwByBtVVU2T195uVRQEP1dVZXrkgEAACAZwR6AtNXWSmVl7ZeVldlyAAAADCwEewDSNm2a1NDQfllDgy0HAADAwEKwByBtlZU2Tq+uTmptjf6urMx1yQAAAJCMYA9A2ioqpHnzbJze+vX2e948snECAAAMREy9AKBHKioI7gAAAPIBLXsAAAAAEEMEewAAAAAQQwR7AAAAABBDBHsAAAAAEEMEewAAAAAQQwR7AAAAABBDBHsAAAAAEEMEewAAAAAQQwR7AAAAABBDhbkuAAAMFjU1UlWVVFsrTZsmVVZKFRW5LhUAAIgrWvYAoB/U1EgLFkh1ddKUKfZ7wQJbDgAAkA0EewDQD6qqpPJy+ykoiP6uqsp1yQAAQFwR7AFAP6itlcrK2i8rK7PlAAAA2UCwBwD9YNo0qaGh/bKGBlsOAACQDQR7ANAPKittnF5dndTaGv1dWZnrkgEAgLgi2AOAflBRIc2bZ+P01q+33/PmkY0TAABkD1MvoF+Reh6DWUUFxzsAAOg/tOyh35B6HgAAAOg/BHvoN6SeBwAAAPoPwR76DannAQAAgP7DmD30m2nTrOtmeXm0jNTzyDeMOwUAAPmClj30G1LPI98x7hQAAOQTgj30G1LPI98x7hQAAOSTnHXjdM4NkfS8pA3e+3Odc4dJWihpjKTFkj7hvW/KVfmQHaSeRz6rrbUWvUSMOwUAAANVLlv2Pi9pRcL//y7pR977IyXVSboiJ6UCgE5Mm2bjTBMx7hQAAAxUOQn2nHNTJH1A0i/a/neS3iXprran3CrpQ7koGwB0hnGnAAAgnzjvff+/qXN3Sfo3SSMlzZN0qaRFba16cs5NlfSQ935mitdeKelKSZowYcJJCxcu7K9iD3i7du3SiBEjcl0MZAD7cuDau1eqr5eamqSiImn0aGnYsNTPZT/GA/sxPtiX8cB+jAf2Y+acddZZi733s1M91u9j9pxz50ra7L1f7Jyb09PXe+9vlnSzJM2ePdvPmdPjVcRWdXW12B7xwL6MB/ZjPLAf44N9GQ/sx3hgP/aPXCRoeYekDzrn3i+pRNIoSf8tabRzrtB73yJpiqQNOSgbAAAAAMRCv4/Z895/zXs/xXt/qKSLJf3Fe/9xSY9LurDtaZ+SdG9/lw0AAAAA4mIgzbP3FUlfcs69Kpt+4Zc5Lg8AAAAA5K2czbMnSd77aknVbX+vkXRKLssDAEBeqKmRqqpsksdp0ywl7GCbxJRtAADdGkgtewAAoDs1NdKCBTbvx5Qp9nvBAls+WLANACAtBHsAAOSTqiqpvNx+Cgqiv6uqcl2y/sM2AIC0EOwBAJBPamulsrL2y8rKbPlgwTYAgLTkdMweAAxWDDfqu0G7DadNs26L5eXRsoYGWz5YsA0AIC207AFAP2O4Ud8N6m1YWWkfuK5Oam2N/q6szHXJ+g/bAADSQrAHAP2M4UZ9N6i3YUWFNG+efeD16+33vHmDpFmzDdsAANJCN04A6Ge1tdYalYjhRj0z6LdhRQWBDdsAALpFyx4A9LNp02x4USKGG/UM2xAAgO4R7AFAP2O4Ud+xDQEA6B7BHgD0M4Yb9R3bEACA7jFmDwBygOFGfcc2BACga7TsAQAAAEAMEewBAAAAQAwR7AEAAABADBHsAQAAAEAMkaAFAADkn5oaqapKqq21CRYrK8nYAwBJaNkDAAD5paZGWrDAJlecMsV+L1hgywEABxDsAQCA/FJVZZMrlpdLBQXR31VVuS4ZAAwoBHsAACC/1NZKZWXtl5WV2XIAwAGM2QOAPmLoENDPpk2zrpvl5dGyhgZbDgA4gJY9AOgDhg4BOVBZaV+2ujqptTX6u7Iy1yUDEEc1NdL8+dLll9vvPLrIE+wBQB8wdAjIgYoKad48+7KtX2+/582jSR1A5uX5XV26cQJAH9TW2rk/EUOHgH5QUUFwByD7Eu/qStHvqqq8OAfRsgcAfTBtmg0VSsTQIQAAYiLPE0IR7AFAHzB0CLGUx+NTACCj8vyuLsEeAPQBQ4cQO3k+PgUAMirP7+oyZg8A+oihQ4iVPB+fAgAZFe7qJs6xdMUVeXM+JNgDAACRwZh1iMkyAXQlj+/q0o0TAABE8nx8So/RbRVAjBHsAQCASJ6PT+kxJssEEGMEewAAIDLYsg7leVp1AOgKY/YAAEB7eTw+pcemTbOWy5CIRop3t1UAgwotewAAYPAabN1WAQwqtOwBAJBtZHscuPI8rToAdIVgDwCAbArZHsvL22d7jPM4uHwzmLqtAhhU6MYJAEA2ke0RAJAjtOwBQIzQW3AAGoyTlAMABgRa9gAgJrI5N3RNjTR/vnT55fab+aZ7YLBNUg4AGDBo2QOAmEjsLShFv6uq+ta6l2rI2XXXSZMnS01NtCB2q7LSNqBkLXoNDbYRr7git+UCAMQeLXsAEBPZmhs6echZU5P06qvSCy9kvgUxlgbbJOUAgAGDlj0AiIlszQ2dPORsxQpp5EgL+kK+EanvLYix1t/ZHhm8CQAQLXsAEBvZmhs6echZ+DuxFZF8IwNINgdvAgDyCsEeAMREtnoLJgeRRUXSzp3SMcdEzyHfyADCVA8AgDZ04wSAGMlGb8EQRIZegSeeKK1bJxUXW/BHvpEBhqkeAABtCPYyiCESAOIqOYhMPt9dcQXnuwEjW4M344qLN4AYI9jLkFSpyRcsIOEaMNgk1hvPPtv+j+M5oL/zjaAHmOohfVy8AcQcY/YyhCESAJLzYrS0xCcvBpOq5xGmekgfF28AMUfLXoYwRAJA8qTmhYVRvTGf69k0fuQhml7Tw8UbQMzRspchyanJJYZIAINNtiY1zzUaPxBbXLwBxBzBXoZka34rAPkjrvXGuAaxABdvAHFHsJchDJEAkFxvbGmJR70xrkEswMUbQNwxZi+DGCIBDG7J89Ede2w86o0kd0SscfEGEGMEewCQQYn1xurqeNQhk4PYgTKvHtOjAQDQNYI9AEC3BlrjBxlCM4ioGQBiizF7AIC8Q4bQDEmeHDJEzUykCACxQMseACDvMD1amrprtUueHDL8zvfJIQEAkmjZAwDkITKEpiGdVrvBMq9GTY00f770xhv2m5ZLAIMEwR6AjAv1qssvp16F7GB6tDSk09d1METNiUHv0KF0VQUwqBDsAcgohgChPzA9WhrSabWLQ9Tc3d2lxKDXOQZ4AhhUGLMHIKMYApRf8jkR40DLEDrgTJtmgVv4EkodW+0GwrwafTkI00nLygBPAIMYLXsAMmqwDAGKA1phYy7dVruKCmsR+9Wv7Hd/B3p9OQjpqgoAXSLYA5BR1KvyB9MXxFw+9HXt60HY066q3udnV1UA6CW6cQLIqMpKuzEvWZ2rocHqVVdckdtyoSN6tw0CA72va18Pwp52VW1utuf2d1dVAMgRWvYAZFQ+NCbA0AqLnOvrQdjTrqqHHNL/XVUBIIf6vWXPOTdV0m8kTZDkJd3svf9v59xBkv4g6VBJayVd5L2v6+/yAei7gd6YAEMr7CA3ELLz9PUgHAgJZgBgAMtFN84WSdd675c450ZKWuyce0TSpZIe897/wDn3VUlflfSVHJQPQD8aCPXNXMrl56eePIilk8WyP2TiIOTuEgB0qt+DPe/9Rkkb2/7e6ZxbIWmypPMlzWl72q2SqkWwB8TaQKlv5spA+PzUkwcp5kgBgEHBee9z9+bOHSrpSUkzJdV670e3LXeS6sL/Sa+5UtKVkjRhwoSTFi5c2F/FHfB27dqlESNG5LoYyIDBsi83bpRaWqTChNtO4f9Jk3JXrkzpbj/G/fPHRSy/j2+8IQ0dapOMB95bApNDDum/cuzdK23aJA0ZYj/799vPhAnSsGEZf7tY7stBiP0YD+zHzDnrrLMWe+9np3osZ9k4nXMjJP1R0he89ztcwgXHe++dcymjUO/9zZJulqTZs2f7OXPm9ENp80N1dbXYHvEwWPbl5Zdbi1ZBQqqo1lZL7PKrX+WuXJnS3X6M++ePi1h+H+fP75jFMvz/qU8NjHLMn5/xt4vlvhyE2I/xwH7sHznJxumcGyoL9G7z3ofJdDY55ya1PT5J0uZclA1A/xns2SAH++dHhtTUWGB0+eX2O50JydPNYplt6cyTBwDotX4P9tq6aP5S0grv/X8lPHSfpHA78VOS7u3vsgHoXwOlvpkrg/3zIwPCwM+6uvYDP7sL+AbKHCnc8QCArMpFN853SPqEpJecc0vbln1d0g8k3eGcu0LSG5IuykHZAPSjwZ4NcqB9/sGeGTUv9SXRykDIzsP8HwCQVbnIxvmUJNfJw3P7sywAcm8g1DdzaaB8/oGQGRS9UFtrOyxRPnWDHGh3PAAgZnKWoAUAMHCQiT9PTZvWMcFJvnWDHCh3PAAghgj2AABZbSBK1T1Uostop9LtT1tTI731lvToo9KYMdKsWVJJCd0gAQAH5CQbJwBgYMlWnoxU+UO+/nXpuut6nlNkUEg34Up4XnGxNLdtBMRjj0lNTfS9BQAcQLAHAMhaZtDE7qEFBfZ7yxZp8+b2y8rL7bmDXqoNlmrjJD5v0iTpfe+TPvABm4ycQA8A0IZunEgbmfqA+MpWnoxU3UP37ev4vHzKKZJV6fanzffELACAfkGwF1OZDszI1AfEXzbyZKTKH1Jc3PF5+ZZTJGvSTbgSh8QsAICsoxtnDPV2jt2upNuzCEB7NTXS/PnS5Zfb795+D8N6PvQhac4c+92X9fWXVN1Dx42Txo9nMvmU0u1Pm61+twCAWCHYi6FsBGa1tdZDKBE9hoCu7d2bmRsv4QbOqlXSmjVSfb39Xr164Cc2Cd1Dy8ul9evt9/e/L33ve+2X0UugTaoNlmrjpPs8AMCgRjfOGMrGUA56DAE9V1+fmbnrwg2cF1+Uhg2zn717pQ0bpOOPH/hz4XXWPXQglzmn0u1Pm/y80PzLwGoAQBta9mIoGynU6TEE9FxTU2ZaxEPLekODTaMm2e+GBlrY0SYb/fcBAHmPlr0Yqqy0a7wUVRD7OsdutjL1DWRkH0U6ujpOiors+9fXFvHQsl5WZi16w4ZJjY3R95sWdrTrvy/1vhkZABArBHsxlK3ALBuZ+gYqso8ObHfdJd1wg3VjnDxZuvpq6cIL+78c3R0no0fbMqlvN17CDZyDD5aWLbOpC1pbpSOP7PuNnHzAjZc0MBVD7mQj/TUHPIAMIdiLqcEUmGUDN8kHrrvukv71X6VRo2wu6fp6+1/q/4Cvu+Nk2LDM3HhJvIGzZ4995tGjpaOOin89kBsvaWJgdXv9FTBl+gDlgAeQYQR7QArcJB+4brjBAr3Ro+3/8PuGG/o/2EvnOMnUjZfBegOHGy9p6k3//bi2IHUVMGVapg9QDngMVHE9XwwCJGgBUshGkhtkxoYNFuwlGjXKlvc3jpPsqqmR7r1XeuIJqbpa2rTJlnPjJYWeTsUQ54Qu/TkxbF/nJUqeiHPpUuY56m+Zmgw1zuJ8vhgEaNkDUshGkhtkxuTJUTfGYMcOW97fOE6yJ9Qtiook7y0xzdNPS6efbssIqFPoSfNvtlqQBsLd//7smtGX7rOpWiBff10qLZWmT+/5+tBzdJtNDy3OeY2WPSAF5iseuK6+2oK7+npLUlJfb/9ffXX/lyWXx0ncb0aHusUJJ1hCGkkqLpaWLGHal4zoa4tUKgPl7n9/Nrn3ZV6iVC2QM2dKy5czz1F/6c9W4HyWjfMF+g0te0AnBusYqYEujMtLzMb5jW9kZrxebxolcnGcpHMzeiA0sPRFaJwpKJDe/nbplVcssJe48ZIR2Ujo0tXd//C7Pw7Irprct2/P7Hv1Jf11qhbII46Qdu+2bZfpeY7y/aSQDQzQTw8JoPIawR6AvHPhhZlNxlJTI914o/Too9KYMdKsWQO7N093PWri0DMpsW4xcaL9hP/z5TMMaD3tg5xOoNBZxXnpUmnNmv47ILsKwKqrs/N+vfkcnVWgZ82y5vpMisNJIRsIYtLDmIW8RrAHIOdyecM51IFWrpQOOshuqt9zj137y8osCPyf/+n/cne17u5uRodgsKlJevJJuy4XFUk33WQ/+YC6RQ/19GDsSYtUuoFCZxXn+nrpkEP6d7xPPnTN6M+DnDFXqXGiSU+2JnBGvyDYA5BTub7hnBgYFRRIW7dKLS3Sxo3Stm3SqlXSu9/dsSWxL+Xurl7e3bq7uxldWysNHSotWiSVlFi20r17pUcesXUnv9dA7NlF3aIHenswphsQpRsodFZxHj2a8T6p9OdBTnfF1DjRpC8fbqAgJYI9IGZyXXnv6fvn4oZzYhmXLJFOPdXqPatXW06ExkbLADlihAWA3/2uJcdLLE9vy51OvTzVurdulT73Oenww62VbsMG+zvVzehp06SHHrJAb9gwW+acdVFNLF+qsnz969LUqZYUJXH/5eK4CnWL8N4//nH3753r47/f1dTYgbF5szR+vHTMMdbnVcrclyjdQKGzinNVFV3lOtNfFWi6K3aOIAYxRzZOIEZynQyvN+/f30m+kstYXGxdHceNs9avPXvseUOGWAvflClSc3PH5Gy9LXc6yd+S171pk/TSS1afD2X23lojU2UBray0Vsnduy2T+/Ll0htvWBC3dGmUxfNzn7PPGMqyb5/02msWACfuv7vuyt1x1ZNjKtfHf78LH3jzZmnsWDuAn3lGeuutzH6JepLdsqLCDrBf/cp+V1T0LWNlf4l7ett82AcAsoJgD4iRXGeR7s379yZLel/qZcllPPFEW15bKx12mAU/+/dLI0dacFRYaIFgcr25t9nd0wkSk9e9YoWVdfz4aLsecYQ0YUL7OnVQUWFTFrz1lgWvw4dbLLB4sfTyy1EwtHmztGyZPU+yjJcjR0ZdWsN2uuGG3B1XPTmmbrzRxl4++aT97NsX8yzqYeOMH29ZJjdtkt58U3r4YenVVzPXatPXQKG7OUr6M9Dau7fjew2GuwTMJwQMWnTjBGIk18MyevP+ycN8Xn3VWqIOO8zqYj0dz9bTMk6YIJ15pvTss5ZDYsMGK8fEidads7HRAqvkenNvx/Wn05sqed2bN1vrYmGhdO+9tuzoo7vermPGWJA6apR152xstKBu4sTovcePt9wZr7xiyxsabKxfYjBaVmbb5Mwz268/W8dVcjfMpUs77tdU711TY9lUDzooGqP4zDPSaafFeFhSOJjHjZNefNH69w4bZhNPLlqUuVabTIxr6qyrXH8O2q2psYA4OagbPnxwJC+huyIwKNGyB8RIf84lnKn3T7zhXFNjgd7MmbY81Q32vrZepipjSYl0/vmWhfOGG+z/LVvs94wZFmQl15t7e6M8nUaS5HWXllqL45AhUSDz5JNWt+9MU5MFaKHuP2yYBUKFCbf4jj3WyrB5s/0uKpJ27rTlktWL//QnW/anP9n/QTaOq1QNLK+/bl1LE6V676oqC3Cds59hw2z/LV0a42FJ4WDessU22LBhdnCMGmWTEy5blrn3StU9MxP6sztCVZV9iZLfa9GizPQlj3tXUAB5iZY9IEZylUU6tMYsXWqV8xkzpCOPTP/9ww3n+fO7z9De19bL7rbRhRdaMpZ0knz05kZ5uo0kiev+zGdSTw/mXOfvE1oQ58yJlt11l8UFoXXwmGOk446zlrv1663r54YNFvRt3GgBpST9wz9YEF5dbQFkSUn6x1VPEqYk1vs3bbLuqzt2SI89Zo8fcUTnx1RtrU1PtmiR/V9SYuMat2/P02FJ6Wy4cDCHMXulpdaEe/rpqfseZ+I9M72u/uyOUFtrX+7k9/LeDqy+JC/JVAvloMswlEVsS0ASwR7yVE/P4YPlnJ+LLNKJdZyKCusRtWyZjRWbNatn759Ova+vSeXS2Ua9yQLZEz0NEvftsyBr5Ur7rGVltm337ev43M4C71dftS6bRUXWVXPPHmut27/fYoSGBuvy+KlP2f67915rIDrhBOviOXas9MIL1t31/PO73q/JZZg50wK17uq/Yf9v2iQ9/bQFbFOnSuvWWZl27+78mArHxemnW5AY5hY8++w8/K7fdZelgG1utqCtsTH1hgsHc2I2zhNPtL7JdXXdD3xN/BLMnCndd19mulP2JPDpzyyR06bZAZ8oHPh1dfZ/unfJkrffpk197wqa63lo4iSu23KwVGaQUQR7yDs9PYfH9Zzfmf4elpE8TcBRR1lgUF5uLXVS+ten3oxn603rZTrbqK/HTU2NtY5dfnnXnzmdbZOqla6uTpo0qfMyJwfe9fXSO99pAdyKFTZ+b/t2ax0Mmfqrq62F73vfiwKvgrbO/hMnSu99rz0e9muqz5EY4NXV2fqXLbP3nTDBnttZ/Td8zhUromkj9u611t7jj29/TCULx0V5uY1nXLrUMpKGsuXNd72mxgI956JAb/lyi9pTbbiKCuknP4k+fFlZ1De4sy9FqoP7u9+19+htsJJ4IK9ZI02enN66+rM7QmWlpZqtq2v/XvPmReVL5y5Zqu33yCPS3Lntn9fTFkomPs+cOG7LwVaZQcYwZg95p6dDPHKdoTLuussu2ZNEd70Zz5atpHJ9OW7CZw5TN3T2mdPdNukmQ0wu81FHWYA4a5bNyRcyeM6ZY8HbiBHWyldaaj+jRlkDUVVVz8ZfJn6OxABv0yY7FkpKLICTuq7/hs+5ebNNL7F3r8U6xxxjv++5p/PhUOG42Lcv6vY5d6617uVFYsUw3uuTn7SMmkOGtB98uGFD5xuup1+KVAd3c7O9R6J0g5XkA3nzZpsrJHGQZ2fr6s8skRUV9gVI9V49GZOYavuNGWN3GBL1tIWyv+ehibM4bksqM+glWvaQd3o6xCNbQ0Iy3ZsicX1nn50/rRHdtcb15AZrb8azZUtfjpvwmQsLo2tyWN6bidnT3S5dlTnsp337LPvmSy9FuTyCkhLbd7W10vvfbxOs19VZ3DFunLWwff/7nX/e8nIbY1dWZsFZQ4P9DuuVuq7/puqVeMIJ9tif/2xJZ5YssYau55+3siRvp4kTpQ98oP3xmGqbDiiJd+wli8DfeEM69FCLyEPGoLPO6nwdPel7nOpAGTfO3iPRq69a4Nld83SY86KpyXZ+yCi0YkXUnNvdju+vnTNsWOfNw+lKtf1mzbK7DMmthj1poWTi88yJ47bMdbpt5C1a9pB3eprxMRsZKjM9LVPy+lpa8qQ1Qt23OvX0Bmu2kv71VG+Pm5oaG+/2xBOWxTI0bqT6zD3ZNulsl1DmTZusS+a999q4vOJi2x+vvWbl2rPHlrW2WjC2a5e9vrHRlhcXS7fcYo094XOvWmWtdatWdXzfxM8RAr2SEltPCPpGjUpverbQK3H2bOu6OX689NRT1uW0tNRev2aNJWL57ne7Lkt323TASIyWR4+2ViLJCv366xaZb9tmfWO7ku6JKdXBPWWKBZnhi7xqlW3kyZO7b55+9FFLchJSxe7YYQMsQ5rXgTyBd28yaHaW0vfd7+5bCyUTn2dOHLdlrtNtI28R7CHv9PQcno1zfqZ7UySvr7Awf3pndNcLK1+vTz09bmpqLGtmZaXVcVta7HVPP23BV6rPnOltU1lpPcnuvNMCs82b7WfdOnt86lSrjzc3SwcfbIFUa6s1yixbZsFgyGK5ZYs1KhUU2JQNY8daPf673+06dghdLhsarJVtxgxbXzi+06n/Jh9TW7daud9808rV3GzlfvTR9OKYAX+8JUaoxxxjG330aCv4zp0WhJ14oiVQ6SoYSXdW+VQH95Ah0je/GW30N9+06RuOOsrK09Rk6/7kJ9sHRanmvCgvtwNp/PiBPYF3b+/adXZy+Oxn+3anionPMyeO2zKOASz6Bd04kXd6mnEyGxkqM92bYqD0zuht19SuemGlyr/w2msWeHTXO6y/yp9K4nGzdKklOBk9Oqo3J6431BlXrrTAaM8eG/7U2mqtW0uWWNKQ5B5d2chNsXFj+7n09u+X1q61LJuSdMopUWKXl1+2gGnPHkvmMm6cNHKkJW/Zt88aaAoLLdbw3gLY5uaOXSITP8f48RbgLV9uMcKbb0qHHWa93HqyPxKPqTCBfEGBlWX/fitfSUnXZenP6Uf6JLHL2cSJFmTdf79FuDNmWAA4caI9p7P+qF3NKr90qQUfiV+Mzk6KF15o67v88uikFNKjFhfb/yEo+uAHrfk4ZAAaP97e33t7/5/8pP8q17358vc2iUc20x73d4atOIvbtsxFum3EAsEe8kry9fwLX+h7MNIbmR4OMBCGF2Qr0Vfy9amoyBoAioosuMjU+6RT/p7WB8Nja9bYeLWQ6DB5vaHO2NRk9ezSUlu+f78tk1J/vkxfu6uqLDg7+mjbxrt2WaC3f79t6+Jia/CZM8eGUm3ebO85enSU6bOuTnrxRXvunj3RZ2lpsXWnmr4t+XNMn27bNmTyT95uoazp7oeRI+0zJAax3luw111Z8qI+lByhFhdbC9m557ZPudrVHaDQwiZFLWySBWkFBXYAJ38x5s/vvEyJJ6WQHlWynfDii1aOP/3JAryyMvtCb95sdwPKyvp3zotUX/6vf93uKO3b17Oxi+neZevNRSUf0+bnY5njLG4BLPoFwR4yKpvXhYGUdTjTrQfJ62tp6f/WiHRvcvdmHyden+bPt7pspjNid1f+3h4/6WyXUGcsK7MGjWHDrN5dWGjTHYSpEFLJ5LW7tjbK1j9smHV/LCqygHP0aGsgeuIJa2l873utbl5YaMuDsjJ77ogR1gLb2Gg9/JqarNFm8uTUNyESP0dNTfskK8ceG+XpuPFGCyJ7sh+mTLGha42N1lo6ZIi1RJaUdF+WASnVlyg5Qn33u6OWtKCrO0CdzSq/YYP0vvelPoDD71Rf5sSTUpigsb7e/h8yJGrm3bcv6i56yCEWaB59tPVpTvez93VnJX9J9+2zg3fLFjvQk+80BP15l20gXcDSlY9lHsgInJEjBHvImGxfFzIxbU6mzrWZbj1IXt+xx/b/9TSdm9y9meMweXtnq8tqd+vt7fGzdKl9zpBl8phjLICprY0+X8gQecgh1pVTsnq2c/0TtIdyvPCC1cHDZOuNjVaOgoIo4DrzTOnvf7dhLOPHW8vd88/b/5IFi//wD1ZX/+53rWdgYaF9tqOPtr+7GiISjpHNm6Nxfk8/bZOdjxtnrX0hAJbS2w+zZlmwuXixBXvDh1vsIVkPw0x3B86amhrp+uttow4ZYoXety91S1vYkFJ6d5SmTZNWr44yeUq2wcePtzk3EpWV2YG9Zk3nX+bEk1IYjzdqlJU7TIA4apS9fv9+WxaCwVQnhJoa6aabbD66MWNsp2bqIpH85X/lFWsObmpqnw73xhvt4Lv8cjuAtm+3L28oT0lJ9r6w+TjvWz6WeaAicEYOEewhY3pzXehJ8NXXICHT59pMtx4krq+6uv/P/+nc5O7JPu5sew8fbuvtzc30ro6X7srfm+OnpsZalJyLWu2eecaGUY0aFX2+U0+1qQFee80+X0uL1TFLS/t2LU/8vMXF1lJXU2Ofq6xMete7bC65W26x+n1trXXdLCiwemzoennWWVHLWkmJ9KEPWVxx113WFXr3bqur799v61i2zJ77zW9aPXjRIguyysosCEynJXT8eNte+/dbue++2wLGvXt7ni2zstLikne/24LSdessOWVhodXVMxk3ZE1NjXTddXYADR1qhX/jDWt9KiuzxCcf+lB0UPf0jtLMmdLPf24RfmgC3b3bxuyl+sLV19sO6erLHMoRWvmefz6K4IcMiaaF2LHD+gGHL2BnJ4MwqFWyg+rtb48SyPRlpyV/+RsabBsnHmiNjTY1wjveYY898YQtP+EE276PPWYHWE8PoHQvYsl3jY49NnWf6IFkoAwmjwMCZ+QQwR4ypqfXhZ4GX33tcZPrc22qOkF4/4HQqyOdrqk92ceJ2/utt+xm++bNFgBNmmSTfPekC2x3x0t35e/s+Cku7pi7InEs3syZFvyE6QT27bNWvNNPb//5Qv193z6rQ48bZz99CfTC5x06VHr4YYsLhgyxcmzdasv+7/+sXr9zp/WqGzrUfjc0WJfLqVMt82Zra8dtsmyZddn0PmqcGTfOGkRuuslihfJyy8MRXtudcIyMGyf95S+2jqFDrdw7dth7vfaaJXlM3A+J3+Ouejnu3m1xSpgIXrK4IeyPAVt3qqqyL4BzFo3v3m1BU0OD/T7oIOmhh6Tf/c6Cjs9+Ngq2wgb51reiTEHJGW8efdR+79ljv4cOtd8rV9pBfsQR7b8Yo0f3LOouLbU7CFu22AF+xhk2PUO48xAyA6b6IicPanXOlr/yijU19zV4SP7yFxXZwXbSSdFzli6N+lavXBlNMLlli3Vz7SxQ7Uq6F7FUd42eftpOLolfhIFmIAwmjwsCZ+QQwR4yJnnS5oYGu+aGSZGT9TT46us4uVyea1PVCa67ziq+RxzRsZ7QH+VJFWR215DQk2t/2N5vvWWNGSUl1iiwdat97qYma6VJtwtsd8dLYvmfeMLqVq2tVo6rr059/KxZY7+XLImCuMWLpe99z9ZXW2v7Z9Qoy1ER6rUFBfaZJKsz79pl5Zk0yeqYoe64ZUvngWR3Ej/vAw/YdmtsjFoMS0psG4as+VI0iXvIWDlhgtUny8uj5DjDh0fzbS9daq858sgoocuWLZZB8403rOWwpzdHQm/CVausXEVFtm1bW62OO2KE9Le/2Xzd+/ZZHDJ+vGULnT/fyvT661buI47omE9k/nyLNZ580oLTEDesWJGZuCFramvtAxcW2oEUdpr39n9rqx1MBx1k/XETTwYLFlgz7Zo1UbPt8OHtn3PPPVH61JEjbcM3N9tGnzrVdmBiWtn6+u6jbqn9yev8823D795t5W5ttTGB48fbZ+us++a999rzN22y5xcURBMx9iR46OzEFb78N95o/YT37o2267hx9nvbNmsGD58zBHthno7eXAzSvYhVVUUpahPvGi1bJn3lKz17z2xK3r4zZ1q3gS1boi9rUZEtz5u+0wMEgTNyiGAPGVNZaQnQXnvN6hpDh1rdY8MGu4b0NRFaX8fJ5fJcm6pOsHmz/T17drQsPHfOnOyVpbub0V1tz54E3GF7//Wv1u2uudnqodOmWSW+vNzWV1UVBR/J9YbEuseSJdZdMlFjo9VxE+t+M2dKv/mN1WdHjbI67Re+YD3Gtm61IUN79ligUFpqdcKQUHDbNgviLrpIuvhiq9c0NFjQFLpBrlpldbaQ/2LbNgtuSkttnSF5YmOjHf/JU3il20ssfD82bbI6/v79ttx7a8UbMSL6f98+K2tBQfT6goIod8b8+e33e8iCGgLixkZbf5iPb9gwCySXLbNtOHFitM+7qw9XVkqf+ETUgFVcHDXoeG/P2bzZzhEh3mlosDplCO6ci947bPdQf06VDKekxNYxoOtO06bZgRN2pGR/O2c7oaAg6vO6c6cdpJdfbsuKiqKm15B5529/s4Pu8sttB+3fH23gcFdCso28b1/UFzaklX311eiOxRFHdP5lrqqyQPPFF+05w4fb8x57zNKufuQj0Vi3ZOGgKyqyOwl790Zj/fbutWWvvSZ9//vdb790WtH27LExeY2N1nL28MN2h+ld77LsoGGgZzh4wt9hm/X04En3IlZba3dURo2K7oSWlfW8JTGbUm3fW2+1k1iwe7dd0MNJfMD3nR5A8nJOGMQFwR4ypqLCbiBv2WKVu7Iy60VTVJS6NaA3wVdfxsll+lzb1/GGIYlGomy0NCaX8623+ja2cvhwK3t3rXKVldI111hwFLo4NjfbOt5809Z59922LGSQTKw3JHdj3LJFWrjQGiJOPtnqtU8+afWnxLrfyy/bstGjrRzNzVY3vu++aDxdUZHVAVevjjJn7t5tzxsyJOqRtmFD1Poajpnlyy2gHDXK6rvbt0f16YICC2QeeMBaxmbMiJZ3N74x+VgK348VK2zbtbREcUJra5Rhc+RIqzOH5ZI9r6TEttnTT9vNgzfftDKceKKV8ZVXLKYIjRzheNy3z95LskDz+edtBoC33rIGp6YmCx4Tj/fk8h90kJXlrbes/p0YeIa6/pgxVq5Nm2zfNDXZ53jrLYtdGhvts0+Y0P57EbbLMcdEsYr3tk8HdN2pstKajV95xQ7oEGGHVq7W1vYpR1991Q6sgw+2569ZYxMWhubX1lbbGCtW2ME7bpw9FgLHnTujtLdr1ljTaXOzvdf+/bZRjz7aDvLi4ujLLLVvjn7iCbujMWyY7bjGRnuf0aOtCTtRqhat8nLr3nH33VHL0N699plCP+Pk90x1Mu2uFS2xq+iiRRYIhybrPXusL/J999nBPXasDYxubrbnPPecndAOO6zjwd2VdC9iRUU2TUW4MJ56avuUxANBV3ckw36urraT0YYNdiKO47izbGXMzMs5YRAXBHvIqH37LNN1YgtD6EqXrL9vdGXyXHvXXZapsLNAJVmqOkFyVnUp8y0TqW7WPvpo1Jsp6MnYyrCfurqZG66Xy5dbXSs0TowcacdDdbXVYydOjLbf8uUWHHVWdzvooCjIbGqKGjFOPDEKprZutfWUlUXjukJiwjCOzTmrr+zaZWUIwaAUtTTt32/rO/xwm6T8xRetfjN5spXjiCPsPUeNsnp2yIWxf78FL9u22eOFhRaQvP3t9llTZTe98UbbJ8kJCkPdtLbWyhg+r2Tv57295yGHWLm3bo1ihzABeujOWV9v8cHQoRagStH+lKyc27bZ4wUF1jpZXGxz9L36qrWyLV9uzz3zzI5djhOD8ocesvijuNjW29gYrTf0rDvssChwfvLJaL8sX277ZfPmqJuqZPHO9On2dzhvlJdH84Vv324NN90lj8mpigrrH3zxxRZ8DRliO72lJQqgQnPokCG2k1tboz7Cw4ZZ02tTU9QVMPThDV+s8eNtR4dovazMAp2DD7ZoP2TtGTbMvkgFBdLb3ib96lf2/FQnjJdfjrqXSvba5AMyvFeqFq0pU+x9Ro+OPqf39uXZscOa5p9+2r78Rx4Z9XGfPNk+a7ppfMPjTz5p22bYMHufHTvs8yxbZgfs0qX2M3GiHaR1dXbXZ/x4O+A3bmzfl7sr6VzEamrs5LFjh+2nPXssgD7iiPRaNDOts2AmnTuS4c5Q6PoqxWvcWbYzZg74OWEQVwR7yKietNbl4kZXJs61NTUW6DmXOlBZtUq64YYoOOhsvNj48VYXqavrWE/Yvr3vn1VKfbN2zBir6yTO1dxVkNnTsZWJ18vQgLBjh9Xbmput3tfYaDf0y8qi+Z9377Y633PP2XqWLrX1J9bdiopsu7a02DY6//yom9+mTdJLL9n6vI9aEZuaohavEHDs3Gl/h66bu3dbnbmgIJpI/N57o5a6ykoLchoaLFANQ53277c6Y+j+uHt39P6hx1hJiTXmTJzYfjt3lqAwJBoJddMnn7R1lpTYZwotfEOG2D5sabFsmy+8YIFZa6sFV8OHRw05o0dbDBAy40tW/9+718pz/PEW0IaebcOGRcHw3r3WG+6ww2xKhrC9wzEQjokQlCc2LIXYpbnZlocbHOvWWZD6179G+6WpKdoPO3ZEc/HV19t+POmkqPEntC43N1ujQ0ii01l34AGjosK6GYZgp6TEDuR16+xDl5REJ4OwIbdvtx3e1BTtQOdsQ61da68J/XHDOLSQqWfUKLuDMH26fbmam23D7t5tB7lzFsyFfvapvuwjRkQJXUpK7MtbWBjdGQlSnUQSLwiTJkVfttJSO7BaW608I0faSTQkb1m2zPptjxtny59/3k4YqbKKhgxLYf6T0O9asrKWlUUBSUWFPecDH4jW88ADVsa9e+2E3dhoX6SbbrKf7vZn4kUseVBs6Kd++OEWPISBv6NG2efJ5kHaWUawzoKZdO5IhrtooduENMD7TvdQrrO4AVlCsIeM6mlrXV+Cr2xP4J48Tn3ZMvt/zRqryE6dGgUqkgUhGzfaeLFRo6xuU18v/eu/Sv/xHx0D2+99z16XKtitrs7M50h1s3bWLGvdSRVkpruOrm7mJl4vy8psW0kWlITJuUOyjm3brEdV6JnW3Cwdd1w0nixM0xDqsGEC8KOPtmDsr3+NJu1escLqxocdZo0YoUUxBHrFxVGLWGurlWfSJCtHGBK1b5/9HjPG1vXKK/b/3/5mYysnTrSgftkyK3dBgR0P+/ZFrXxh+4wdGzWAbNxoLV7btlkLVDi+UiUoTEw0UlFh+2vNGltHCKBbWqy+NXGibbfp0+0mw/jxtr69e6VDD7XXrVtnr927Nxo3WVJi/zc2WstomEh9/Xp7rLY26p4auv6G8qU6BhIbVDZtsjijqSlKzBL2RUlJNL7uzTfttcOH2zYaMsSeF8oYWl1Hj7Y6+G9+Y90+k1uXpTybvmrfPtvBK1fahm1stOCksdEOxJ07o6brkMa0qCjqUhd2RBiw6JytM7SaFRfbxt6/31qsQmApRa2HzkV3DUaMaD8gcuhQOwGFcWUhXeuwYdGyk0+2g6W7k0jiBeHoo6U//tHec+hQOzDCTt+8OUo5W1oa3SkoK7PP9dpr0XhFqX2GpXBn5dRT7SDcutUOoOHD7bUnnNA+INmzx+4khSkQ1q6N5h0JJ3Tvo8npu5OYMTV5UOyCBfY+FRV2sghBaGtrNLFlNnQ1701nwUxndyTr6+3kFU6O27fbSTBVet++lHcgpKUmYyZiimAPGdVfrXXZ7G2RvO7Vq62iedpp1sto0SKrM23fbkGBFI2P2rPH6kbhxmf4fcMNnc+dl2rZ3r29z+KY+DnWrLHyhoBowgQr69lnRxkau9tH6bTWJl6rX3hBOuUUW37KKdYVMbSohQm+R4yw+uyGDfa8MNRo6FDrVdbUZPWKBx+0ul9zc1R3mzbN6nQh2Kqvt2Bs1y5b7xln2L559tmoISRkr9y50+q4ob7c0mL7VLIgZdcuW29RkXX/DHXhjRuj7pgjR9rn/8MfbLt4b/Xr0lIrX3Ozrf+gg6x8a9fa88eOtVa7oqKoDjhhgq1j/Xr7fGPGtE80UlNjnyFMq9DSEuV1GDvW6sjjxlnMsGZNVB/evduCv5CUsKAg6h0Yun/u2mXPXbTIynTiibb9H3/cjuOiomiKh9JSe30YQ5d8DKxebYGxZJ8rtDwWFEQtna2tFuw6F8UjIRBPDraHDbOgvrXVArzHH7fPn6qOGv7Pm5vx4Qt17LFRsBSCqd277QCT7O+QAXHTJls2fHjUjS40bXofRdOhBTCkZG1psZ0TBp8mtsYNHWo7uLDQvrg1Nfbz6qv2PhMn2gG0fbs99/jj2wd2l10W3QHr7CSSmCXz73+38paWRs22IbBrbIzulmzaFI0PTAy+wok48eIS7kxs2GBlmznTDsQwpuy006KumldcYZ9v374oaN27N8osGrZ7kNxNtTudtQrV1vZ+YtFU0gmMOivLk09K551nf4f5cMJJMnF+k7DuT33KkrSEGw2lpXZiGzmyZ6mUu/s8A+VuTT5mzBwogTIGNII9ZFx/dEtPvpY1NdmN8uR5iTOx7g0brN7x5pvWgjJ+vNWpwpxxLS3ROLLW1qhLXjBqVBTUSN2fm2tqrL7T2yyOYR0LFlhXve3bo4DouOOsvpdqGqjOgsuuWmtDl9ZHH7U6Y5ha4fXXLYgKrWHbt0fdI6dMsfrb66/btty1y8pXVGQ93CTrcVZaaoFMGDt26KEWLC1das854wyrj4X5+1pbra43caLVEQ891Oq8mzbZPtm1K+pOKEWJQ4YNi1q3fvtbW/7GG1EPpn37rD66c6eVIwSK06fbcRB6xm3aFD22a5f1jmtutr+nTLFt8+KL9rn37LFtWFISDePZu9fed+JEe2zaNMtqGbo0hmz5xcVW7oMPjo6RV16JGj1CsFRfHwXYUhSUhvFzoeW0qMh+1q2LtnlIVLN7dzTmbvdue97RR0fDxa64woLK3/wmiiXCNh4yxI6JsI2kqFtnYjfRnTvtsxcW2vpDrLJunf2urra/Qx4Pyeqpzz1n+8N7a8095ZQoEB3QN+PDFyrMfyfZQTZpUnQiCf2dpahPsmQHQmJgt3+/7aCWlo6DOvftiw7I8NzQvB5SpEpR/+jrrrONtmePvTYMZHXO7kL87W/R5I1XXy1deKH9pCNkyRwyxD7jvn22rKkp6hra1GQHYMjaOXGi7eAw38jIkdHF5a67pB/8wL5QJSX2JWhutrsRo0fbieWEE+xACd0Oq6rs/1NPtW0Vxj2Gg/Hgg6P0um+9ZXd55sxJPadhKp21CjkXJYMZN86eM2RI71rD0g2MOitLGDS7b180H05RkZUxrCd0Pa2ttXJPntx+vsIQDM2f3/PypzKQuk4mX+xee81uaPQ0cU9/GUiBMgY0gj3kpcRr2aZNFhyEelO657t0x6mHjIJhXNa4cdE0Vy0tdiN86FC7Hv797zYkZMwYqzuMGGGV9cmTo/fs7txcVWXjmUKm87Iye31Prn2J18+Q7XvzZgs6f/KTjoFed1MxJN/wPeMMu1l/991WNwqtOGEITaisNzXZNhg2zOqfIRDbu9eGsQwfbkH0rFn2GUeNsh5DIbCYNMkyQa5aZc9rbrZ1nnlmVLGfONHWXVNjAcPq1TZ2L7QgTp5s+RaamqJ5pkPPr1CnDRkzDznE6oYhI+TatVEQ39ISTZMWPs/w4dH4v5AkZufOKEgKPfBCYsQQeJeURK1cU6faT+jRV1JijSj/8R/2eUeNirKRhuP9yCNtexx0kNVFQnKWkK9j+PCoNTWULbFVNbSk7t8fdbNduzbqRvvww7aPwmtDnNDYKN15pyX4+eY3o2Fep51m+2j16iivSHh9YmNSmAewuNi+F2EbhpwjBQVRlv4Q4L7yim3PY46Jvo9/+Yttj5ArZPlye+/p0+0GQ0guOSCFL9QnP2n/jx5tgcnf/x7tWO9tY4XgJ6Rj9T5KvRqEFr5UQqvfvn3RARDWE1r1du+O+vuGLp91dbZzCgvtgNm82XbMe95jr7vvPtvYnaVjTawUJ/ZX3rnTDqbQohcOgIMPtgNi2LDorsbmzbYjwyDOffvsTl5RkQWeLS1RE/Grr0bNyLt3W+V8/Xr7P7RIhuxUp59uTfRhCoTwXO/tyxS+EGGaiLIyK29nk6WHz11TY3cgCgvtNcccY+vasMHKHCak37jRErP0pjKebmDUWQvVaafZ8uQbDaHLwY03Rq2uU6ZYk/qKFXYCnTjRPtP48V3fSelpS1O2uk72tBzh+Tt22Guci7qshsRBAy2QGkiBMgY0gj3kpcRr2YoVVv+QrJ6Q6nz3wx9KP/2pnbsPOki64AK77nY3Tj20sLW22o3lvXutUjt5sr1+/Xq7Ro4YYd0Xw5ijbdui5CQtLdI3vhGVqbtz89Kldj1NnI7qpZei8WDpSHX9LCpqn6AjSKdMia21ITh84QWry4Queonj4UaMiDJClpZGY8v27rVt1NhoLURvvGF1vWOOsf24eXM07GjfPrvuvvWWXWtLSixp4Pz57af0Wr7cgv0QQO7ZY8vHj7f1TpxodZsQXIShUGGO6uHDraFg5swokN+/3943tIKFgCUEPyFIlaKyFBbae4ScECGj/ujRVrY337T1heAwtKht3GivPeoo2yabNllLWXOzbcc9e2y9paX2/5AhVrZhw2x56IlWXGzH9v79Vq8bOtSeH7rBFhZGDUPh/9CiVlho+6Kx0d4/dKtsaYkSOw4dGt08ePJJ6VvfsiA9JNJpbrbWwsQuuyGoDvFF6Lq5aVPUbdd7256S/R0anFpbowQzo0ZZff6oo+w42bYtil/CuMD9+21bJic6zGkvp64mAf/Qh9pXyEPyi2OOsch7y5bo4EoeMBmEndmVxJ0hRTs8HAylpTb49YgjbEeEuzdhbF9oIdy0yaYPOOccK3M4QXR3tyiMA/zTn+xADXdcwsHQ2GifNbS2HXGE7eQhQ+zACYM+x461db/6atQsP3KkbbPQNzvcOdi3zw7o0lI7WYeUr2Fuv4kTo8kj6+rsy7lqVdQknbjdtm+3x97xjvYnxeS5YbZts59p06KMmyHjUMiMFSbffOyx9FtFE6UbGHXWHSMMcv3Up+yzjR5t3RomTLDPft991gJbXm4nmb17bVtu3Gif7ZVX7Pmhn36y3rQ0ZaPrZGflOP546f7722dPu/DC9s+vqIgycc2YEaUBHoiBFGMMkSaCPeSlxGtZ6AK4b5/dIJfan+9++EPp29+Obhrv3m2B36mnWnAgdT5OPWQd37zZKs4lJfY+r71mFfM9e6Jsi62ttp5Q6Q83o084wW4YT5+e3rk5BGShW96wYbaeVIFaZ8L1s7PeOh/8YDTcJoyxe+stS3oX8gaUlXU9HCS0NBUWRsFeSMDS0mLXyTPPtOvopEnW8rJ5s33+Y46xAFay5xQXW30ipOuXrBvmkCG2bXfssPrY5ZdHGTkPP9x+P/JI1OvtpZfsvWfPbj8xfWNjVFbnota6EJCGAD10iywqsvrdyJHROLiQv6GhwfZ7CNpCHTqxi2hRUTScqrTU6skhOHHO3qeoyD5fCIbnzLHl9fX22pEjrWxNTfa8/fuj6cnCmNHy8uj9Q46OsrKo197o0bZNN2ywx0JwFIK8EHiH3n1DhkTPC/Xo8JlCfBCCwDAX4euv2zpfeMGeM2pUlE025AEJ4/YSp4YIU1OEcYTDhkWNPVKUtTUMLWtqss/7+uv2vNAoFd4ntCZOmRIlOgx1uP377bhetMhao7/5zd7VtXuku4pvcoV82DBrzt+40TZgOEBDoBc2YpA4T0h3wpczBFghkCsqsh22caPdEZGi6D6su7k5agXavdtOKKedFp20OrtbdOONdmD/5S/2BQj9i8POOuooO0Crqy1IC2MWly61E9i4cfbFr6uz9Rx0UHR3o7Q0muskBLthMGroc7xtmwVYzzxj77djR9Tk/ve/W5KZv/wlmjwyjA0sL7fnhhNFaakdPMkn6sTPXV1td5dGjLCDM2RCffNNW/bGG3YQjx1rX6x0k78kSzcw6m7w/Pnnd1zPa6/ZReCJJ2y/hEHQ9fW2LUaPthPAM89Y0/78+R1vYvSmpSl8D7Zute0c5oj55jd7t43C+yWXY8kS6Y47ortmr78uffrT0s9+Zvt98uT2z29uthNnCPakgRdI5eMYQ+QEwR7yUuK1LMyZW1Ji1/Cysvbzcv30p3YdD60HoetdTU37+ebKyqLxYKEnx+uvWy+figq7BoVulQ0NFrhs3hwl/RgyxK6Po0ZFAcvYsdblMdTzGhvbz6177LFW30o8N4ekLnv3Rt39tmyx+t/8+en1Rtm0yYKgvXuj9YXeOjt22Di7OXOsDrp8ufTnP0c9xEKrzrZt0te/3rHHUbhRH8bASe0bD0JF/thjrf4QxoZNnGife/Nmu66HLoUrV1q9bvPmqBEh5JmYOtU+e22t9bwKmRhDC1B1te37UD8tLLTHnn/etumMGVH5Qp05tPwFoYUpTMe1YYPVM771LWtgCYF2aOELQZfUsZ4dgpn6+qiRIWTrDELZi4vtsxQWRtMelJREw4xCYBamJZPsNSGrqGTlDkGk9/bZwti54mKrz77ySpQ4JsxnHRpRksue+Hm2bWv/eAishg+3+mpia+mzz0ateGHbhG2bPFVXcXEUGBcVRYlbEjNyhhbiME1aCB7vuCPqChp67YWyFxbaDYCWFkues2iRrW/8eNunJSV2nDU02PGf2AsxK7qr+CaexJ54wlqzhg61DxsO2DDfXmFhFHSFL0mIpNOVuHNbWqL+uWGHJWYuSpY4/m/rVmtKv+AC+z9Vv/fnnrO7O4ccEu3ccDCEA1Gyz1xWFmVfGj7cgro9e6zZuLzc0u6GOwghmUsIxnbsaB8Ahy9XaHbfudNOgCEV7PDhtk2XLLEvRm1tFDQnZlcqLIz6FocELnffbXOPBEuX2ol9xw4LlsMknIWFFkyFKSOamqKTamOjBaBDh/auybm7dNfprjPV2LTqattHGzbYSXfnTttew4dH2ZPCIPXE1LiJNzF609JUURH1W9+3z/bX1Kn2Jf3d79IbL5ksuRybNllXVCkaJL1zpx0ToXvH9u12nIXxAePGRXc0g3QCqbvu6jj3UrbuLPX3ZMXIWwR7WdSf3/l0xC1pU6gvzZxp0xuEVoUwL1eYVmjdOruGhXwDpaVWZ0qslEp2vXv5ZbtuhyR4Yez/UUdFz1u92rbl8OHRWK7QahHS+IeWxvHjo7FcW7ZY/SDMgbxnT3RD+NhjrdWquNhubra0WGU61D3GjLGWrsQJtxMT4YXPmjhB9wkn2PrffNN6Rp1+ul3HXn7Z6idhTOCQIRZohTFtoV4XgufPfc6O3WXLrE66aFEUYCQLdcjSUnvu9u227ZYts/UddJC9Z329lXHECKsLLlli2y7UySS7Fq9cafv0Xe+y9YQEcrW19ppQL5ba1329t+2weXPUQyyxS2KiEKC99Zb9P3So9M//HE11FtYfugqG9wrvkyx0Pwwtc531sgtDg0J2yu3bozKEYDfx/SUrZ5jEPUyknrj+0JoYAqH77ovqqkOGRC3TPU00mGj3bttXBx0UdTMOAXQIUruS+L1raLAyhdckB89hv4XPvmZN1LVz//72+7652fZ16PI6dKgdP6+/bi3LiVn7a2vtuD788CyeCzur+C5d2r5VZOZMCyRCZp7QZByi4pCVU4oi9BA89VY4YYWBnonLk1sQEx9rbLSDbvNm+4LPn28nu5BtMgyo3LDByvfGG1b2kSPtrkXIvlNQYF/8nTutj3aYGkKy161dG5Vr1Chb7+bNdvcs3A1KR3OzHRDhTk1ozSwtjQZhh22ZmF0pTAAfvkxDh9r+Wr7cLux33GF37SR7bviskybZiW3TJhvvFoLacPIJmVXPOqvn3R2Tx5SFxDGhxS6dLpSJlYAwWeX69fZFKSqyL/WmTVHf8HAshnUOGWKPd5YatzctTTU1FjxOmmTfj+3b7YIxfnzUfaCzbVNTYxe9RYts/512mt3Bramx7ppDhkTfwXBxCSeL0KVg5067o1tf3z7d8JQptizdOYokOzb+9V87zr0kZafyl4vJipGXCPaypL+/893JVtKmgRDQLltmrT4bNtj5ePToaC600IMn0Z49UcU38Tz+3HNRxTlM79TcbMu3b7frQH29XR+HDLHrfqKQUyEEek1NdmP3b3+LKu0lJTb0I6Tb37fPrp1Tpljg8/DDVpcK3fdC60fI9BjqOVddZXWKMKn7ddfZ+rdsibKBrlplN9ZbWmzd4RoWMoGvXt0+oV94r7AsJFqprbVjd/Jk+zzJLULJCgqsHFOn2nOfe86+B2PHWv0udLnctcsCt5BsMOynxGAg1LOWL49akoqLbT+kuqmfqL7ehuoUFUVZIVMJ036F5DFh3FlyoJUYREldN6wUFdl+TKdO3tJidY2aGtsOoZUq+f2l9q2SIdFNssTgL7GbaZgiS4rW31utrdEk6Ikti6GxqCdCHNNZjBEeS5ycvbHRjoMQs4THE8eQ1tba7127oik1JDsG9+yx2OHMM7OYd2HatGjag9AloLTUxpyFO0rLl0u//70dBCFLT/gShqycvYnMu9qY3enqdaHlrKjIvoCrVtlJITRRh26S+/fbBg93TEKSmTBR/N690eSNL74YHTzl5XYCO/zwqBJbXm5f/vHjo3k9hg7tfqxiEFpEm5ossAx9l8N6EudhCV/80PwdPnM4sJ9/3lLkJt5V2r496ua6caPdVXv00ShYDQF0WEdxsfU3Lyuz8W/hc0sduzuG4GzpUjsxzZgRjSmrq0udCKez+UkS7wTOmmX7MIzj++Qn7WIS+uOH/SZF/bdXr7aLSEGBPVfqOH3DN74RTdUQ7piOHx9NKptKVVWUqdS5KJtVaMFN3jY1NTbZ/cyZ0le/GgV0paV2Aa2qigK5kL0p+VhJnPR1wgQbV/D001Fa53AX9Jvf7H56kUQ33GAXu1RzL2WrYtQf6c+R9wj2siQX3/lUwrXi3nvt3H7CCV1fW3pioAS0tbXWchVa3zZtstarZ5+1m7ep6j3791vrzciR0v/9n13HGhqicW3TpllLwLhx1ppQWxsl9ghBWGf27rVryZ49URK9YN8+ay0bPdoCsTfesPd96KEoUV5Yd0g8ETJA1tdbHSFU2DdvtjpQ4lQPQ4ZYUBamjApDdBKvYdu323U0jIcK18HCwmhMY3NzNPVVa6stf+GF9OpXoTvhK69E9da9e+1mfeLnCkFNVz3RQplfeSV677Cd0tXUFCX96Exjo9VbEpODpBKG9YTcFl2tL12trdZKmZhpPzmTfiqpgqrk13T2OXqy/TrT2mp123DDPMwJnioA7UqoC4fvVmfPSSxzCPgSeztKURbX0Oss7PutW+37FnKBhOz8mToXdlBTY5XMMEn45Mn2BX72WStsyPoUWrskOxmESmpis2Vv9OW16RgyxO5+rVwZRfslJbYDwh2RkBBFioLXEPSEzDyJX5T9+21Hbd1qAfJFF1lXwf/3/6TPf95OAmG+wTDYs6s7C+HumGTbeudOuzDs22flDGMHEweOhmb+UNbQMhf2Rzj5J04gGQ6y0HVh8eJosncpuhMSWpSmTIkmVV+92gLb2bPtgExs9U0cnFxXZ9vqL3+x9Y8caf8nzjWU3JL88st2sdm61cYyhOb9vXttfXPnRol2wliILVuiC074zCEADncDw5iIBx6IJvcsL7dlt95qx/nu3XZyCIODv/vdKH1vstra6K5lOAYkC/RCUNnYKN1zj22fMKfNscdG3Sg2brQ7Zk1NUcvs+PH2nQrHa2JGqiFDortMp5xid/pmzrRuMMnzB/akMrNhg1WGEiXPvSTFr5sVBjyCvSxJ9zvfmUycCxJb88L1JkwMPXFi38caD5SANrHnyMsvW++ikLI+XLek9vWnggJL3PLDH1qdZdiwKPAI14dp0yxASRy71F0luaXFbpqGm4mJw2BCi0OYsy30Iho5MpqPLUiuq7W0tE9eESR2Tw1CK2C4po0da+u/7z4LiPfti4LK0Lso1HXq6qKeVqGC3dgYbZOw7boKdpO7Ou7fH322xJam8LnSkTjuqy8tUl3pLiCU2mdEzWR9OjSWJI6BHOj27LHyJs6rFxIt9lSoZ/f0NYk3co46yo7TMNRKigLBvXutDjdlitVJi4utrhj0Oe9C4gk7VNI3b7aTSH29nUgmTYruoiRPnyBFFep8EIK4xJNbaHYOJ7pUUX8Ijrprmdu9O0oY8uyzdhIOc3OEJvXuviThoidFJ5DQdB5S/oYDJYyNlGzAd0hpG06G4f0S+3QnC3cSNm5s/9wgBEthGobQcvvyy3YxOPJIK9chh9iB+qc/WcAzZYqd1EPr2Y4dUReJwkLrAnnTTXYRmzDBWgu3bbNB2yEL044dUTfSMOD6ueek97/fjtnTTrM5VcKg5/BlLCmxL0dIIzx0qHTJJZbUZM+eKHPVhg3S2WfbFzDMn5gYON9zj93RDF/aUaNsH+/Y0fU+fPll26a7d0fdnBsaoot54vjKMP9M+AlztxQWRn3mx4yJWoclG9R8zDG23sLCjvMS9VS4qRMqQ2F/hbmXJObGQ04Q7GVJOt/5zmTqXJDYqyOkfw+ZIydOTK8rfVcBZ38EtOk8JzGZV3V11AI3YkR0nS4ujhK0NDbaeX3+fHtduLlbUhLVT8Kcbsnjs9Opi40ebdfycOO4O+E6lCxVYJeu0IOludkquRMm2PXtwQejek/i+lpaom1WVxfVw1IFYz0tR2KFPF/qsrmQTrA5kISunMnzdvdWb14b6v5hOFbojpzqecXF1gNg92479919tz02ZYpl1k0cl9sjySfsP/0pytwUTizl5XZSyEST6kCSqh91Oncq0uki0NJiO+qFF3pXts7WGQKx0L0zTN4pRa1WIZNUTw/K5OxPiUIGozBeLgQ6YXqLxYttcHIY+/jaa3YMvfZa+ykhkk8UIVnNtm1Rd4/Q7J2YwSlkFw13lV5/PaoEVFZaS13ocpGYsTVkyho1ygK/3/42Kn9iVqUHH4xa5zrb9iEF78aN6W3PvXvtuWGfhEHNoQ934s2GMLFpYlrh4mJ732HDrLIS5sz5+MetJa8nXTTTcfXVUfemUaOitNxh7iWJufGQEwR7WZLOd74zmToXJPbqOPbYaOLxMOa4q7HG6QScvQ1ow5jqxOEDnY0lTyfoDWOUP/c5ux6MHm2tWSNGWC+QMJ68qCjqcRN6xoSsiGGYQEgrv2+fXTcTpTsM5qmnBl5Qs2lT1AOqM6mC274I9SnEV6oGjP6UOL4xzAmeSsifccUVNmRu9WqLwYYMiRqQPvzhXhYi+YRdX28nlMSK9dq1PZsoE9kV7sSFE1Q4ue/dG01BkQ3DhtnxsXVr1IoWWkWbm+1GwZNPRq1RiReSroIoKRoTuHt3NMHpsGHRnIRh3sIQJIXunGecYRfkHTusxS+8p/e2rt27o27HpaXtW8+SM1Wl09raVf/3VJqb7YJdWGgXsdBVJ/T9DoPww+SlI0ZEF/sQfB50kHWVLS625Djz59s6Mt0FKawvMZHBN77R/n2YGw85QLCXJel85zuTqXNBYvfGCRNs3PiSJfZYeXnXN7LSCTh7E9CGAC5xrNmiRVa2xHl60y1DcNVVFmRJdrNyyxbroXH00VFCu7177cbeCSfYe997b9SNcehQu4aEZC2hjhbGUaVKlpHKkCHd90wBkDmhrhsy16YSvsc//amdC6dOtXptY6PVX0ePtpv8var7JZ6w33ormlRRsjcImWwwcPSkFTKTEu8gpmrKDwFWb4SuqGEArWQXvLq69oFimJ+loMCyiv7+9923YIaWvuSWy95sv57cCQ1BeGghLy62i/OWLfbYwQdHd3lGjbJAbu5cu5NcXW3be/Jkq2AUF6c3LUFfs85deGHXz582zbrQLl9ulYVRoyzxzsknp/8eQA8R7GVRd9/5zmRqnszkKViKiiz4Sac7aDoBZ28C2hDANTXZOS5UzlassMx4ietPN+g944wo0Av27LHz6VFH2Tp++9voM591lmXILCmxMoShGZIFfQUF0ne+YxXDjRuja1PiNWrEiPZTVAW97coWspH3ZuwSANNVy97+/VFyluLiaNhcSDRz2232f9rjo0Mf87/8xbrQhQpx4mBd7ztONAh0pi/BZ2ilDAd24gD0ROFC1tOLVSYC49AC15P3HDbMgtd9+2z8SUgLHZKufOxj0mc/2/5LGyohieNAJk2KAr2QBCd5bEh/ZJ0bOdISCwwbZhWJ3bvt/3e9KzPrB1Ig2BuAMjVPZl+mYEk34OxpQBsCuLIyC5SGDbOgq6Gh4/rTLUNyoBeEG5jJScBef92CupKSaNqdEPANH27ZnK+91p77jW9EwyXCtAdlZdLFF9t1R7Iy3nZbx0mo05XYehiSv0lRwpi+ZFEHYPbts+/9xo3t5zhsaop60z30kA2d+t73ujlPhi4K27bZT1NT+1S6AXdugEjIBNYT73iHZdZctswqA9On20V6+3bLztmV5GkJuhsb0h9Z5+6/31okQzfT4cOtPPffH1U8gAwj2BuAMjlPZm+nYMlUwJksBHDHHGOZQaVo3Hry+kMZtmyxlsMtW6yy9s1vpl53qqAosUUvCEnCQjKz0tIoSVqYHkCKzrsLFth1JbQEnndeFOhJ0TywvfXCC5Y47Yc/tHpjUZF9zo98xMZZ7t7dMeMmgJ5rabGeXUOHWuve3r3RuL8wVdqrr1pyw5tu6mJFoYvCo49GLXoDbaAuMNCEgfFjxnTMxjlyZDR+0Xv7fp10kgVl06ZJX/lK+4t5dXXP37+7sSF9zTqXjvAeiS2cra2ZfQ8gCcHeAJXreTIzGXAmCgFceblle1661AKls8+WPvOZjolXPvhBm6InzLk6ZYpNITB9euqyJI8XT/WcI46whFzhxlpJid1cO/jgjs+99tr2N9vmz4/myg0aGjpmnUzXBz4Q7evE93noIdsuTz3V8/HsALoXZggYMiRK7hfmUVy0qJsX19ZaxBjuztCCB0SJUVL50pfsjmaixJa2xLvK2ZqGoLuxIX1Jo56u/ngPIInzedw/bORhI/1J3z6p3bKLZlykz578We1p3qP33/b+Dq+5dNalunTWpdq6Z6suvKNjs/xnZn9GH535Ua1rWKdP3P2JDo9f+/Zrdd7R52nl1pX65wf+ucPj3zjzG3r34e/W0reW6gsPf6HD49+f+32dPvV0Pb3uaX39sa93ePzH5/xYsybO0qNrHtX1T17f4fH/Pfd/dfTYo3X/yvv1w2fanzjr6+t1/2X3a2rZVP1h2R900/Mdb03fddFdGls6VrcsvUW3LL2lw+MPfvxBlQ4t1Y3P3ag7lt/R4fHqS6slSQueXqAHVj3Q7rFhQ4fpoY8/JEn67hPf1WOvP9bu8TGlY/THi/6omhrps1Vf02v7nlFxsQVxw4dLU0ZN0e8qfydJ+sLDX9DSt5Zq7Vq7G19YKI3RdJ2nm1VXJz1VfqVGHrpKkvTCUmlHg6S3ZkkP/9jerPISjZq6XifMit7/7VPern9797/prrukTz14gVzptgNdJffvl84/bq4WftaaDd932/u0t7n9gLxzp5+r94yYpwULpL9Mm3PgtaG1QMsv0pAln1WL2yN9vOOxp6WX2k/pVrmPXqjx462FM0g89s779XkaPXq01q611ofNmyU9fa206jxpzErpvI7Hnp78hrTm3dLEpdI5X+j4+GPfl9adLk19Wprb8djTwz+2bXj4o9KZHY893f+/0rajpen3S6f/sOPjVb+VdkyVZvxBOjlFs8gdd0l7xkqzbrGfZLc9KDWXSiffKM3oeOzplmr7ffoCaXr7Y0/Nw6Tb7NjTmd+VDm9/7GnPGOmOP9rfc78mTX2m/eM7pkhVduzpnC/YNky0bbp0/83293lXSmNWtX886djTqPWSpMMPr9eaNaOldW+XHvs3e/yiC6TSpD6/a+ZKT7Y1WX/8fdLQpMGgq86Vnp5nf186Rx0sv0h67rPS0O6PPV2UojvSc5+Rln9UGrVOqux43ovdseekslFt3TmdVHTHg5pwUKmGnn6jNo29Q62tlrwvmH/ofM2ZMyc6761da3dj9uzWsGbpoduHSM3N+u6Z0mOHt3/rMXukP7Ydzl+bKz0ztf3jU3ZIv6uyv79wjrR0YvvHp2+Tbr7f/r7yPGnVmPaPz3pL+vHD9vclldL6Ue0ff/s66d/avg4XXCRtK23/+Nw10jeftL/f93Fpb9JcieeukuY9bX/PuVQdXLRc+uxz0p6h0vs/3vHxS5faz9ZS6cKLOj7+meekjy6X1o2SPlHZ8fFrn5bOWyWtHCP983kdH//Gk9K719h2+8I5HR///mPS6eukp6dKX58r1R9+uEavWXPg8R8/bNvw0cOl68/s+Pr/vV86ept0/3Tph6d3fPy3VdLUHdIfZkg3pcixcdcd0tg90i2z7CfZg7dJpc3SjSdLd8zo+Hj1LfZ7wenSA9PbPzasWXqobaxpTo+9ggJd8i/jtf5tbYHUqpXSzl16+zqvf1s0QrrySl1w6lpt29P+vDf3sLn6Zvn5UlWV3tf4C+0tKYwqBLJr7rzT7bw355Y57V5bX1+vK99xZc/qe4kVCkmf0Wx9tG6y1pUX6BOHvmDdh9asOTAe8NpXynXeKqeV3/28/rn57g7r71V9L+E9frxmuma92apHh2/S9R86KJpEvk1X9T1J+u2Hfzvg63uS9LVHv6Zn1re/5ob6XnV1te5pvEdL31ra7vHpY6br5vPsmnvl/Vdq1bb219xZE2fpx+f8WJJ0SdUlWr9jfbvHQ31Pki6444LUx947u67vdXbsSQMz1njisicWe+9nd3iypB6MlAUyo6JCOuMfLFnMoYdG89+lkjgpelBW1n5O4hNmSaPK2j9n3Di1C/QSXXihdNxMW+++Jvt9+OGWECudss+b1/batrJNnWpDCkJLQXJLnGtLvnL0MdK3v229T848s32g15lx42x9XW2jTHnhBbv52h/vBeRCmDpMoVWvUPJtvS/HjGnLrbLfem51adw4e/LQouiLD8RVaal07DF2hyTZpEnSf/yHZcEcPrwt5fWJdpH7ylftjkpyi16iigrrMnPccd1XCPpq3LjoDq0UTStxzvuix8M0D037rCz/8R/Se1PcRehLGcJ7bN1qLXyf/nSHQA/IpLxu2Zs9e7Z//vnnc12MAaO6ulpz5szJdTEyKnSbTEzSEv4PU+UMFJ/8pGWybmqyG4NlZVbO3mRvTtyXIaHYHXdY1tJEpaWW0Ct5TkDJlre2dj3Pb5Cqh83RR0urVqV+PtKzYEG15s2bk+tioM3IkVFOhIoKy9q+ZYtNxzBihA70NPj+95OHB6U4t/7Lv9gdkk2brDtn4h0oDFjVCxZozrx5uStAQUF+3BwYMsS6Zb7jHXZxGGATfve6vpOYoTM5Gyf6XRzrrbninOu0ZW/Ajdlzzp0j6b8lDZH0C+/9D3JcJORQthLFZMNvfmM/mRbG9HUW3NbUSOeeaxXXYNQoa22cPNm2WU2N1S8KCmzIQkOD3dQcPty2ZaobrytX2jjCX/4yeu6ECZbNu6mtRTQk3NmxI7pZ6pyNfTrkEAtGFy/O+CbpVFdDRkaNkr71Lem668iGPxgNGxb9nHmmBX5nnSXNnGmJ/npc9/vsZ+3kdNppdnJ69VXp8cftC9HcbJXlMIEnk28ObJkIwA46qH2mruJim/snDAotKbGD7ogjpJdfti4eITtlZyetbBkzJhonN3SopahuarIWp69+NfOTjQ8kuU6IAOTAgAr2nHNDJP1M0tmS1kt6zjl3n/f+5dyWDLmSrUQxcVJR0XHuwUz54Q+77oEjdX2jNDz205/2LWNpQUE09UVnj593njRrlg2H+O1v2z8+YYLNdy21T4Rzwgkds3eXltr7eG89lK65Rrrnns6n+CgutkB4586ovhhaiEpLpfPPj8p1553RfMedmTJF+tGPpP/9X8tYGwLr1laLIbqrkxYW9u98jcOGWR22vt7K19POIkOHWr0zTHIekh0NHWr15xkzLFHdypUdP/vQodLYsVE23+ZmOxaGDrXnFhZafbakxB7rrJW9V3Xb5JNTSAlfUREd+EuX2oZxzloAN2yI5ng56ywLFO+/354XDqDCQuvaNWZMNKF0S4vdodi8uePknomcs50xfHh00GzaFN2JCRsnTFIdJnxPzDCVvAOLi+11TU0dD6phw2wDT55sO3Hduij5xNSptlM2bbKgZsQI+xI2NNhrQwpU761M48fbFzVxOxUX20FQVxdlwkpUWCjNmWNdEDo7CYa+u6m+OMXFFvmvXWsBeX1913eCZs2yL/T//Z+0erWVcfx4O0kkp81PdWKUbNnUqbb/X3rJtn9hYfsuGKNHW5fGI4+0z/3449E+TP5sYZuEyVqTn5eq2waAQWVABXuSTpH0qvd+jSQ55xZKOl8Swd4gxo24ga2r/dNdq2S2pNvC+sIL6T2vt9MfVVdbltmgJy2/cb65HhudHfw9OWkxt1b/qq7u+8Sl6ZzQujo2AKAfDagxe865CyWd473/p7b/PyHpVO/91QnPuVLSlZI0YcKEkxYuXJiTsg5Eu3bt0ogRI3JdDGQA+zIe2I/xwH6MD/ZlPLAf44H9mDlnnXVW/ozZ6473/mZJN0uWoIWBnREGusYH+zIe2I/xwH6MD/ZlPLAf44H92D8G2tQLGyQlzgQzpW0ZAAAAAKAHBlqw95yko5xzhznniiRdLOm+HJcJAAAAAPLOgOrG6b1vcc5dLelPsqkXfuW9X57jYgEAAABA3hlQwZ4kee8flPRgrssBAAAAAPlsoHXjBAAAAABkAMEeAAAAAMQQwR4AAAAAxBDBHgAAAADEEMEeAAAAAMQQwR4AAAAAxBDBHgAAAADEEMEeAAAAAMQQwR4AAAAAxBDBHgAAAADEEMEeAAAAAMQQwR4AAAAAxBDBHgAAAADEEMEeAAAAAMSQ897nugy95pzbIumNXJdjABkraWuuC4GMYF/GA/sxHtiP8cG+jAf2YzywHzPnEO/9uFQP5HWwh/acc89772fnuhzoO/ZlPLAf44H9GB/sy3hgP8YD+7F/0I0TAAAAAGKIYA8AAAAAYohgL15uznUBkDHsy3hgP8YD+zE+2JfxwH6MB/ZjP2DMHgAAAADEEC17AAAAABBDBHsAAAAAEEMEezHknLvGOfeKc265c+4/cl0e9I1z7lrnnHfOjc11WdBzzrn/bPs+1jjn7nbOjc51mZA+59w5zrmVzrlXnXNfzXV50HPOuanOucedcy+3XRc/n+syofecc0Occy845x7IdVnQe8650c65u9qujyucc2/PdZniimAvZpxzZ0k6X9Lx3vsZkhbkuEjoA+fcVEnvkVSb67Kg1x6RNNN7XyFplaSv5bg8SJNzboikn0l6n6S3SfqYc+5tuS0VeqFF0rXe+7dJOk3SVezHvPZ5SStyXQj02X9Leth7f4yk48U+zRqCvfj5jKQfeO/3SZL3fnOOy4O++ZGkf5VEJqU85b3/s/e+pe3fRZKm5LI86JFTJL3qvV/jvW+StFB2Mw15xHu/0Xu/pO3vnbJK5eTclgq94ZybIukDkn6R67Kg95xzZZLOlPRLSfLeN3nv63NaqBgj2Iuf6ZLOcM793Tn3hHPu5FwXCL3jnDtf0gbv/Yu5Lgsy5nJJD+W6EEjbZEnrEv5fL4KEvOacO1TSCZL+nuOioHd+LLsB2prjcqBvDpO0RdKv27rk/sI5NzzXhYqrwlwXAD3nnHtU0sQUD10n26cHybqqnCzpDufc4Z45Ngakbvbl12VdODHAdbUfvff3tj3nOll3stv6s2wAjHNuhKQ/SvqC935HrsuDnnHOnStps/d+sXNuTo6Lg74plHSipGu89393zv23pK9K+mZuixVPBHt5yHv/7s4ec859RlJVW3D3rHOuVdJY2R0UDDCd7Uvn3HGyO18vOuck6/q3xDl3ivf+rX4sItLQ1XdSkpxzl0o6V9JcbrzklQ2Spib8P6VtGfKMc26oLNC7zXtflevyoFfeIemDzrn3SyqRNMo59zvv/SU5Lhd6br2k9d770MJ+lyzYQxbQjTN+7pF0liQ556ZLKpK0NZcFQs9571/y3o/33h/qvT9UdmI8kUAv/zjnzpF1O/qg935PrsuDHnlO0lHOucOcc0WSLpZ0X47LhB5ydsfsl5JWeO//K9flQe9477/mvZ/Sdk28WNJfCPTyU1tdZp1z7ui2RXMlvZzDIsUaLXvx8ytJv3LOLZPUJOlTtCQAOXWDpGJJj7S10i7y3v9LbouEdHjvW5xzV0v6k6Qhkn7lvV+e42Kh594h6ROSXnLOLW1b9nXv/YO5KxIw6F0j6ba2G2lrJF2W4/LEliMOAAAAAID4oRsnAAAAAMQQwR4AAAAAxBDBHgAAAADEEMEeAAAAAMQQwR4AAAAAxBDBHgBgQHPO7crAOs52zi12zr3U9vtdCY8559xfnHOj2v6f4py71zm32jn3mnPuv9vSg6dab7VzbnaK5Zc6527oZPkW59xS59wrzrkvplH2S51zByf8/wvn3Nu6ec2jzrny7tYNAIg3gj0AwGCwVdJ53vvjJH1K0m8THnu/pBe99zvaJuCuknSP9/4oSdMljZD0vQyW5Q/e+1my+d+uc85N7eb5l0o6EOx57//Je9/dBMS/lfTZvhQSAJD/CPYAAHnHOTfLObfIOVfjnLs7tGI5505uW7bUOfefzrllkuS9f8F7/2bby5dLGuacK277/+OS7m37+12SGr33v2573X5JX5R0uXOu1Dk3zDm30Dm3wjl3t6RhCWW6zDm3yjn3rCyQ65L3fpukVyVNanv9t5xzzznnljnnbm5rcbxQ0mzZ5MNL297/QGuic+5jba2Vy5xz/56w+vskfayn2xUAEC8EewCAfPQbSV/x3ldIeknSt9uW/1rSP7e1nO3v5LUXSFrivd/X9v87JC1u+3tGwt+SJO/9Dkm1ko6U9BlJe7z3x7a950mS5JybJOk7bev6B0lddrNse800SSWSatoW3eC9P9l7P1MWRJ7rvb9L0vOSPu69n+W935vw+oMl/bssQJ0l6WTn3Ifaylwnqdg5N6a7cgAA4otgDwCQV5xzZZJGe++faFt0q6QznXOjJY303j/Ttvz3KV47QxYg/XPC4oO89zvTfPszJf1Okrz3NYoCtVMlVXvvt3jvmyT9oYt1fNQ5VyNr1bvRe9/Ytvws59zfnXMvyQK4Gd2U5eSE92yRdFtb+YLNSuj+CQAYfAj2AACDgnNuiqS7JX3Se/9awkMtzrlwPXxZba11Ca8bJWmaLDjLhD+0tUieLukHzrmJzrkSSTdKurBtXOHPZa1+fVEiaW+3zwIAxBbBHgAgr3jvGyTVOefOaFv0CUlPeO/rJe10zp3atvzi8Jq2Vr//k/RV7/3fkla5UtLhbX8/JqnUOffJttcNkfRDSbd47/dIelLSP7Y9NlNSRdvr/i7pnc65Mc65oZI+ksbneF6WSOXzigK7rc65EZIuTHjqTkkjU6zi2bb3HNtWzo9JeqKtbE7SRElruysHACC+CPYAAANdqXNufcLPl2QZNf+zrTvkLEn/r+25V0j6uXNuqaThkhrall8tG3P3rbZEJ0udc+PbHvs/SXMkyXvvJX1Y0kecc6slrZLUKOnrbc+9SdII59yKtvdc3Pa6jZLmS3pG0t8krUjzs/27pMtk4wt/LmmZpD9Jei7hObdI+p+QoCUsbHvPr0p6XNKLkhZ770OimZMkLWrr3gkAGKScXdcAAMh/zrkR3vtdbX9/VdIk7/3nu3nNJEm/8d6f3R9l7A/Ouf+WdJ/3/rFclwUAkDuFuS4AAAAZ9AHn3Ndk17c3ZHPUdcl7v9E593Pn3Ki2zJtxsIxADwBAyx4AAAAAxBBj9gAAAAAghgj2AAAAACCGCPYAAAAAIIYI9gAAAAAghgj2AAAAACCG/j/Ya+K6ZRtxcAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "# Cálculo del logaritmo negativo base 10 de los p-values\n",
    "df_drug_adr_name_filter['log_p_values'] = -np.log10(df_drug_adr_name_filter['P-value'])\n",
    "\n",
    "# Cálculo del logaritmo base 2 del odd ratio\n",
    "df_drug_adr_name_filter['log_odd_ratios'] = np.log2(df_drug_adr_name_filter['odds_ratio'])\n",
    "\n",
    "# Separar los puntos en dos grupos según el signo del odd ratio\n",
    "odd_ratio_pos = df_drug_adr_name_filter['log_odd_ratios'][df_drug_adr_name_filter['log_odd_ratios'] > 0]\n",
    "odd_ratio_neg = df_drug_adr_name_filter['log_odd_ratios'][df_drug_adr_name_filter['log_odd_ratios'] <= 0]\n",
    "\n",
    "# Plot\n",
    "plt.figure(figsize=(15, 10))\n",
    "plt.scatter(odd_ratio_pos,df_drug_adr_name_filter['log_p_values'][df_drug_adr_name_filter['log_odd_ratios'] > 0], color='red', alpha=0.5, label='Sex-biased ADR Female')\n",
    "plt.scatter(odd_ratio_neg, df_drug_adr_name_filter['log_p_values'][df_drug_adr_name_filter['log_odd_ratios'] <= 0], color='blue', alpha=0.5, label='Sex-biased ADR Male')\n",
    "plt.axhline(y=-np.log10(0.05), color='green', linestyle='--')  # Línea para indicar el nivel de significancia\n",
    "plt.xlabel('Log2(Odd Ratio)')\n",
    "plt.ylabel('-log10(p-value)')\n",
    "plt.title('Volcano Plot')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.savefig(\"volcano_plot_adrs.svg\")\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    3963.000000\n",
       "mean        3.230774\n",
       "std         5.573887\n",
       "min         1.301405\n",
       "25%         1.605907\n",
       "50%         2.081328\n",
       "75%         3.091855\n",
       "max       135.988808\n",
       "Name: log_p_values, dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_drug_adr_name_filter[\"log_p_values\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.3010299956639813"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "-np.log10(0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     3.963000e+03\n",
       "mean      1.424071e-02\n",
       "std       1.509667e-02\n",
       "min      1.026107e-136\n",
       "25%       8.093685e-04\n",
       "50%       8.292245e-03\n",
       "75%       2.477953e-02\n",
       "max       4.995681e-02\n",
       "Name: P-value, dtype: float64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_drug_adr_name_filter[\"P-value\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### hacer uno para un farmaco concreto SIROLIMUS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_sirolimus = df_drug_adr_name_filter[df_drug_adr_name_filter[\"Drug_name \"]==\"sirolimus\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-67-f2cf59eb667e>:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_sirolimus['log_p_values'] = -np.log10(df_sirolimus['P-value'])\n",
      "<ipython-input-67-f2cf59eb667e>:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_sirolimus['log_odd_ratios'] = np.log2(df_sirolimus['odds_ratio'])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "# Cálculo del logaritmo negativo base 10 de los p-values\n",
    "df_sirolimus['log_p_values'] = -np.log10(df_sirolimus['P-value'])\n",
    "\n",
    "# Cálculo del logaritmo base 2 del odd ratio\n",
    "df_sirolimus['log_odd_ratios'] = np.log2(df_sirolimus['odds_ratio'])\n",
    "\n",
    "# Separar los puntos en dos grupos según el signo del odd ratio\n",
    "odd_ratio_pos = df_sirolimus['log_odd_ratios'][df_sirolimus['log_odd_ratios'] > 0]\n",
    "odd_ratio_neg = df_sirolimus['log_odd_ratios'][df_sirolimus['log_odd_ratios'] <= 0]\n",
    "\n",
    "# Plot\n",
    "plt.figure(figsize=(15, 10))\n",
    "plt.scatter(odd_ratio_pos,df_sirolimus['log_p_values'][df_sirolimus['log_odd_ratios'] > 0], color='red', alpha=0.5, label='Sex-biased ADR Female')\n",
    "plt.scatter(odd_ratio_neg, df_sirolimus['log_p_values'][df_sirolimus['log_odd_ratios'] <= 0], color='blue', alpha=0.5, label='Sex-biased ADR Male')\n",
    "plt.axhline(y=-np.log10(0.05), color='green', linestyle='--')  # Línea para indicar el nivel de significancia\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "plt.xlabel('Log2(Odd Ratio)')\n",
    "plt.ylabel('-log10(p-value)')\n",
    "plt.title('Volcano Plot')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.savefig(\"volcano_plot_adrs_sirolimus.svg\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-66-3a1bac24cb39>:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_sirolimus['log_p_values'] = -np.log10(df_sirolimus['P-value'])\n",
      "<ipython-input-66-3a1bac24cb39>:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_sirolimus['log_odd_ratios'] = np.log2(df_sirolimus['odds_ratio'])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "# Cálculo del logaritmo negativo base 10 de los p-values\n",
    "df_sirolimus['log_p_values'] = -np.log10(df_sirolimus['P-value'])\n",
    "\n",
    "# Cálculo del logaritmo base 2 del odd ratio\n",
    "df_sirolimus['log_odd_ratios'] = np.log2(df_sirolimus['odds_ratio'])\n",
    "\n",
    "# Separar los puntos en dos grupos según el signo del odd ratio\n",
    "odd_ratio_pos = df_sirolimus['log_odd_ratios'][df_sirolimus['log_odd_ratios'] > 0]\n",
    "odd_ratio_neg = df_sirolimus['log_odd_ratios'][df_sirolimus['log_odd_ratios'] <= 0]\n",
    "\n",
    "# Plot\n",
    "plt.figure(figsize=(15, 10))\n",
    "plt.scatter(odd_ratio_pos,df_sirolimus['log_p_values'][df_sirolimus['log_odd_ratios'] > 0], color='red', alpha=0.5, label='Sex-biased ADR Female')\n",
    "plt.scatter(odd_ratio_neg, df_sirolimus['log_p_values'][df_sirolimus['log_odd_ratios'] <= 0], color='blue', alpha=0.5, label='Sex-biased ADR Male')\n",
    "plt.axhline(y=-np.log10(0.05), color='green', linestyle='--')  # Línea para indicar el nivel de significancia\n",
    "\n",
    "\n",
    "for x, y, nombre in zip(df_sirolimus['log_odd_ratios'], df_sirolimus['log_p_values'], df_sirolimus['PT_name']):\n",
    "    plt.text(x, y, nombre, fontsize=8, ha='center', va='top')\n",
    "\n",
    "\n",
    "plt.xlabel('Log2(Odd Ratio)')\n",
    "plt.ylabel('-log10(p-value)')\n",
    "plt.title('Volcano Plot')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.savefig(\"volcano_plot_adrs_sirolimus_names.svg\")\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_sirolimus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Drug</th>\n",
       "      <th>Drug_name</th>\n",
       "      <th>ADR</th>\n",
       "      <th>PT_name</th>\n",
       "      <th>SOC_name</th>\n",
       "      <th>Number adrs in Male</th>\n",
       "      <th>Number adrs in Female</th>\n",
       "      <th>P-value</th>\n",
       "      <th>p_value_FDR</th>\n",
       "      <th>Significance</th>\n",
       "      <th>Sex-biased</th>\n",
       "      <th>odds_ratio</th>\n",
       "      <th>Log2_Odds_ratio</th>\n",
       "      <th>sex_biased_log_oddratio</th>\n",
       "      <th>Sum_ADRS</th>\n",
       "      <th>log_p_values</th>\n",
       "      <th>log_odd_ratios</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2850</th>\n",
       "      <td>DB00877</td>\n",
       "      <td>sirolimus</td>\n",
       "      <td>10012378</td>\n",
       "      <td>depression</td>\n",
       "      <td>psychiatric disorders</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>0.035147</td>\n",
       "      <td>(array([ True]), array([0.03514673]), 0.050000...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>sex-biased female</td>\n",
       "      <td>3.415398</td>\n",
       "      <td>1.772054</td>\n",
       "      <td>sex-biased female</td>\n",
       "      <td>15</td>\n",
       "      <td>1.454115</td>\n",
       "      <td>1.772054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2851</th>\n",
       "      <td>DB00877</td>\n",
       "      <td>sirolimus</td>\n",
       "      <td>10035528</td>\n",
       "      <td>platelet count decreased</td>\n",
       "      <td>investigations</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>0.007180</td>\n",
       "      <td>(array([ True]), array([0.0071797]), 0.0500000...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>sex-biased female</td>\n",
       "      <td>4.350024</td>\n",
       "      <td>2.121023</td>\n",
       "      <td>sex-biased female</td>\n",
       "      <td>18</td>\n",
       "      <td>2.143894</td>\n",
       "      <td>2.121023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2852</th>\n",
       "      <td>DB00877</td>\n",
       "      <td>sirolimus</td>\n",
       "      <td>10028813</td>\n",
       "      <td>nausea</td>\n",
       "      <td>gastrointestinal disorders</td>\n",
       "      <td>37</td>\n",
       "      <td>50</td>\n",
       "      <td>0.017054</td>\n",
       "      <td>(array([ True]), array([0.01705352]), 0.050000...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>sex-biased female</td>\n",
       "      <td>1.683418</td>\n",
       "      <td>0.751394</td>\n",
       "      <td>sex-biased female</td>\n",
       "      <td>87</td>\n",
       "      <td>1.768186</td>\n",
       "      <td>0.751394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2853</th>\n",
       "      <td>DB00877</td>\n",
       "      <td>sirolimus</td>\n",
       "      <td>10040070</td>\n",
       "      <td>septic shock</td>\n",
       "      <td>infections and infestations</td>\n",
       "      <td>14</td>\n",
       "      <td>29</td>\n",
       "      <td>0.003144</td>\n",
       "      <td>(array([ True]), array([0.00314356]), 0.050000...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>sex-biased female</td>\n",
       "      <td>2.578851</td>\n",
       "      <td>1.366728</td>\n",
       "      <td>sex-biased female</td>\n",
       "      <td>43</td>\n",
       "      <td>2.502579</td>\n",
       "      <td>1.366728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2854</th>\n",
       "      <td>DB00877</td>\n",
       "      <td>sirolimus</td>\n",
       "      <td>10070863</td>\n",
       "      <td>toxicity to various agents</td>\n",
       "      <td>injury, poisoning and procedural complications</td>\n",
       "      <td>13</td>\n",
       "      <td>26</td>\n",
       "      <td>0.006002</td>\n",
       "      <td>(array([ True]), array([0.00600241]), 0.050000...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>sex-biased female</td>\n",
       "      <td>2.488598</td>\n",
       "      <td>1.315333</td>\n",
       "      <td>sex-biased female</td>\n",
       "      <td>39</td>\n",
       "      <td>2.221675</td>\n",
       "      <td>1.315333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2855</th>\n",
       "      <td>DB00877</td>\n",
       "      <td>sirolimus</td>\n",
       "      <td>10075160</td>\n",
       "      <td>graft versus host disease in gastrointestinal ...</td>\n",
       "      <td>injury, poisoning and procedural complications</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>0.024279</td>\n",
       "      <td>(array([ True]), array([0.02427924]), 0.050000...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>sex-biased female</td>\n",
       "      <td>4.139681</td>\n",
       "      <td>2.049520</td>\n",
       "      <td>sex-biased female</td>\n",
       "      <td>13</td>\n",
       "      <td>1.614765</td>\n",
       "      <td>2.049520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2856</th>\n",
       "      <td>DB00877</td>\n",
       "      <td>sirolimus</td>\n",
       "      <td>10008479</td>\n",
       "      <td>chest pain</td>\n",
       "      <td>general disorders and administration site cond...</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>0.003608</td>\n",
       "      <td>(array([ True]), array([0.00360844]), 0.050000...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>sex-biased male</td>\n",
       "      <td>0.154547</td>\n",
       "      <td>-2.693887</td>\n",
       "      <td>sex-biased male</td>\n",
       "      <td>18</td>\n",
       "      <td>2.442681</td>\n",
       "      <td>-2.693887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2857</th>\n",
       "      <td>DB00877</td>\n",
       "      <td>sirolimus</td>\n",
       "      <td>10062237</td>\n",
       "      <td>renal impairment</td>\n",
       "      <td>renal and urinary disorders</td>\n",
       "      <td>29</td>\n",
       "      <td>10</td>\n",
       "      <td>0.022597</td>\n",
       "      <td>(array([ True]), array([0.02259651]), 0.050000...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>sex-biased male</td>\n",
       "      <td>0.426076</td>\n",
       "      <td>-1.230817</td>\n",
       "      <td>sex-biased male</td>\n",
       "      <td>39</td>\n",
       "      <td>1.645959</td>\n",
       "      <td>-1.230817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2858</th>\n",
       "      <td>DB00877</td>\n",
       "      <td>sirolimus</td>\n",
       "      <td>10012174</td>\n",
       "      <td>dehydration</td>\n",
       "      <td>metabolism and nutrition disorders</td>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "      <td>0.003977</td>\n",
       "      <td>(array([ True]), array([0.00397712]), 0.050000...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>sex-biased female</td>\n",
       "      <td>3.978316</td>\n",
       "      <td>1.992158</td>\n",
       "      <td>sex-biased female</td>\n",
       "      <td>21</td>\n",
       "      <td>2.400431</td>\n",
       "      <td>1.992158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2859</th>\n",
       "      <td>DB00877</td>\n",
       "      <td>sirolimus</td>\n",
       "      <td>10018964</td>\n",
       "      <td>haemoptysis</td>\n",
       "      <td>vascular disorders</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0.048753</td>\n",
       "      <td>(array([ True]), array([0.04875321]), 0.050000...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>sex-biased male</td>\n",
       "      <td>0.154750</td>\n",
       "      <td>-2.691985</td>\n",
       "      <td>sex-biased male</td>\n",
       "      <td>9</td>\n",
       "      <td>1.311997</td>\n",
       "      <td>-2.691985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2860</th>\n",
       "      <td>DB00877</td>\n",
       "      <td>sirolimus</td>\n",
       "      <td>10057030</td>\n",
       "      <td>pneumatosis intestinalis</td>\n",
       "      <td>gastrointestinal disorders</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>0.028602</td>\n",
       "      <td>(array([ True]), array([0.02860178]), 0.050000...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>sex-biased male</td>\n",
       "      <td>0.123752</td>\n",
       "      <td>-3.014475</td>\n",
       "      <td>sex-biased male</td>\n",
       "      <td>11</td>\n",
       "      <td>1.543607</td>\n",
       "      <td>-3.014475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2861</th>\n",
       "      <td>DB00877</td>\n",
       "      <td>sirolimus</td>\n",
       "      <td>10010305</td>\n",
       "      <td>confusional state</td>\n",
       "      <td>nervous system disorders</td>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "      <td>0.006770</td>\n",
       "      <td>(array([ True]), array([0.00676959]), 0.050000...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>sex-biased male</td>\n",
       "      <td>0.206031</td>\n",
       "      <td>-2.279064</td>\n",
       "      <td>sex-biased male</td>\n",
       "      <td>21</td>\n",
       "      <td>2.169438</td>\n",
       "      <td>-2.279064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2862</th>\n",
       "      <td>DB00877</td>\n",
       "      <td>sirolimus</td>\n",
       "      <td>10011906</td>\n",
       "      <td>death</td>\n",
       "      <td>general disorders and administration site cond...</td>\n",
       "      <td>58</td>\n",
       "      <td>25</td>\n",
       "      <td>0.007606</td>\n",
       "      <td>(array([ True]), array([0.00760574]), 0.050000...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>sex-biased male</td>\n",
       "      <td>0.531501</td>\n",
       "      <td>-0.911855</td>\n",
       "      <td>sex-biased male</td>\n",
       "      <td>83</td>\n",
       "      <td>2.118858</td>\n",
       "      <td>-0.911855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2863</th>\n",
       "      <td>DB00877</td>\n",
       "      <td>sirolimus</td>\n",
       "      <td>10021143</td>\n",
       "      <td>hypoxia</td>\n",
       "      <td>respiratory, thoracic and mediastinal disorders</td>\n",
       "      <td>25</td>\n",
       "      <td>7</td>\n",
       "      <td>0.011528</td>\n",
       "      <td>(array([ True]), array([0.01152798]), 0.050000...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>sex-biased male</td>\n",
       "      <td>0.345994</td>\n",
       "      <td>-1.531182</td>\n",
       "      <td>sex-biased male</td>\n",
       "      <td>32</td>\n",
       "      <td>1.938247</td>\n",
       "      <td>-1.531182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2864</th>\n",
       "      <td>DB00877</td>\n",
       "      <td>sirolimus</td>\n",
       "      <td>10003445</td>\n",
       "      <td>ascites</td>\n",
       "      <td>gastrointestinal disorders</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>0.013708</td>\n",
       "      <td>(array([ True]), array([0.01370824]), 0.050000...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>sex-biased female</td>\n",
       "      <td>4.554750</td>\n",
       "      <td>2.187372</td>\n",
       "      <td>sex-biased female</td>\n",
       "      <td>14</td>\n",
       "      <td>1.863018</td>\n",
       "      <td>2.187372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2865</th>\n",
       "      <td>DB00877</td>\n",
       "      <td>sirolimus</td>\n",
       "      <td>10014625</td>\n",
       "      <td>encephalopathy</td>\n",
       "      <td>nervous system disorders</td>\n",
       "      <td>6</td>\n",
       "      <td>14</td>\n",
       "      <td>0.025073</td>\n",
       "      <td>(array([ True]), array([0.02507261]), 0.050000...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>sex-biased female</td>\n",
       "      <td>2.898887</td>\n",
       "      <td>1.535499</td>\n",
       "      <td>sex-biased female</td>\n",
       "      <td>20</td>\n",
       "      <td>1.600801</td>\n",
       "      <td>1.535499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2866</th>\n",
       "      <td>DB00877</td>\n",
       "      <td>sirolimus</td>\n",
       "      <td>10012601</td>\n",
       "      <td>diabetes mellitus</td>\n",
       "      <td>metabolism and nutrition disorders</td>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>0.000410</td>\n",
       "      <td>(array([ True]), array([0.00040994]), 0.050000...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>sex-biased female</td>\n",
       "      <td>4.977713</td>\n",
       "      <td>2.315483</td>\n",
       "      <td>sex-biased female</td>\n",
       "      <td>25</td>\n",
       "      <td>3.387284</td>\n",
       "      <td>2.315483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2867</th>\n",
       "      <td>DB00877</td>\n",
       "      <td>sirolimus</td>\n",
       "      <td>10023421</td>\n",
       "      <td>kidney fibrosis</td>\n",
       "      <td>renal and urinary disorders</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>0.012320</td>\n",
       "      <td>(array([ True]), array([0.01232045]), 0.050000...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>sex-biased female</td>\n",
       "      <td>4.038331</td>\n",
       "      <td>2.013759</td>\n",
       "      <td>sex-biased female</td>\n",
       "      <td>17</td>\n",
       "      <td>1.909373</td>\n",
       "      <td>2.013759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2868</th>\n",
       "      <td>DB00877</td>\n",
       "      <td>sirolimus</td>\n",
       "      <td>10058854</td>\n",
       "      <td>cytomegalovirus viraemia</td>\n",
       "      <td>infections and infestations</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>0.006091</td>\n",
       "      <td>(array([ True]), array([0.0060914]), 0.0500000...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>sex-biased male</td>\n",
       "      <td>0.164882</td>\n",
       "      <td>-2.600496</td>\n",
       "      <td>sex-biased male</td>\n",
       "      <td>17</td>\n",
       "      <td>2.215283</td>\n",
       "      <td>-2.600496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2869</th>\n",
       "      <td>DB00877</td>\n",
       "      <td>sirolimus</td>\n",
       "      <td>10028034</td>\n",
       "      <td>mouth ulceration</td>\n",
       "      <td>gastrointestinal disorders</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>0.002383</td>\n",
       "      <td>(array([ True]), array([0.00238264]), 0.050000...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>sex-biased female</td>\n",
       "      <td>4.973863</td>\n",
       "      <td>2.314367</td>\n",
       "      <td>sex-biased female</td>\n",
       "      <td>20</td>\n",
       "      <td>2.622941</td>\n",
       "      <td>2.314367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2870</th>\n",
       "      <td>DB00877</td>\n",
       "      <td>sirolimus</td>\n",
       "      <td>10022004</td>\n",
       "      <td>influenza like illness</td>\n",
       "      <td>general disorders and administration site cond...</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0.028614</td>\n",
       "      <td>(array([ True]), array([0.02861407]), 0.050000...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>sex-biased male</td>\n",
       "      <td>0.137529</td>\n",
       "      <td>-2.862191</td>\n",
       "      <td>sex-biased male</td>\n",
       "      <td>10</td>\n",
       "      <td>1.543420</td>\n",
       "      <td>-2.862191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2871</th>\n",
       "      <td>DB00877</td>\n",
       "      <td>sirolimus</td>\n",
       "      <td>10028735</td>\n",
       "      <td>nasal congestion</td>\n",
       "      <td>respiratory, thoracic and mediastinal disorders</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0.048753</td>\n",
       "      <td>(array([ True]), array([0.04875321]), 0.050000...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>sex-biased male</td>\n",
       "      <td>0.154750</td>\n",
       "      <td>-2.691985</td>\n",
       "      <td>sex-biased male</td>\n",
       "      <td>9</td>\n",
       "      <td>1.311997</td>\n",
       "      <td>-2.691985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2872</th>\n",
       "      <td>DB00877</td>\n",
       "      <td>sirolimus</td>\n",
       "      <td>10046306</td>\n",
       "      <td>upper respiratory tract infection</td>\n",
       "      <td>infections and infestations</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>0.035147</td>\n",
       "      <td>(array([ True]), array([0.03514673]), 0.050000...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>sex-biased female</td>\n",
       "      <td>3.415398</td>\n",
       "      <td>1.772054</td>\n",
       "      <td>sex-biased female</td>\n",
       "      <td>15</td>\n",
       "      <td>1.454115</td>\n",
       "      <td>1.772054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2873</th>\n",
       "      <td>DB00877</td>\n",
       "      <td>sirolimus</td>\n",
       "      <td>10043645</td>\n",
       "      <td>thrombotic microangiopathy</td>\n",
       "      <td>blood and lymphatic system disorders</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>0.029741</td>\n",
       "      <td>(array([ True]), array([0.02974085]), 0.050000...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>sex-biased female</td>\n",
       "      <td>3.230036</td>\n",
       "      <td>1.691550</td>\n",
       "      <td>sex-biased female</td>\n",
       "      <td>18</td>\n",
       "      <td>1.526647</td>\n",
       "      <td>1.691550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2874</th>\n",
       "      <td>DB00877</td>\n",
       "      <td>sirolimus</td>\n",
       "      <td>10021015</td>\n",
       "      <td>hypokalaemia</td>\n",
       "      <td>metabolism and nutrition disorders</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>0.008156</td>\n",
       "      <td>(array([ True]), array([0.00815556]), 0.050000...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>sex-biased female</td>\n",
       "      <td>6.210730</td>\n",
       "      <td>2.634763</td>\n",
       "      <td>sex-biased female</td>\n",
       "      <td>12</td>\n",
       "      <td>2.088546</td>\n",
       "      <td>2.634763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2875</th>\n",
       "      <td>DB00877</td>\n",
       "      <td>sirolimus</td>\n",
       "      <td>10060931</td>\n",
       "      <td>adenovirus infection</td>\n",
       "      <td>infections and infestations</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000881</td>\n",
       "      <td>(array([ True]), array([0.00088095]), 0.050000...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>sex-biased male</td>\n",
       "      <td>0.077255</td>\n",
       "      <td>-3.694235</td>\n",
       "      <td>sex-biased male</td>\n",
       "      <td>17</td>\n",
       "      <td>3.055047</td>\n",
       "      <td>-3.694235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2876</th>\n",
       "      <td>DB00877</td>\n",
       "      <td>sirolimus</td>\n",
       "      <td>10014909</td>\n",
       "      <td>enterovirus infection</td>\n",
       "      <td>infections and infestations</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>0.013409</td>\n",
       "      <td>(array([ True]), array([0.01340937]), 0.050000...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>sex-biased female</td>\n",
       "      <td>9.934300</td>\n",
       "      <td>3.312418</td>\n",
       "      <td>sex-biased female</td>\n",
       "      <td>9</td>\n",
       "      <td>1.872592</td>\n",
       "      <td>3.312418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2877</th>\n",
       "      <td>DB00877</td>\n",
       "      <td>sirolimus</td>\n",
       "      <td>10019663</td>\n",
       "      <td>hepatic failure</td>\n",
       "      <td>hepatobiliary disorders</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>0.015384</td>\n",
       "      <td>(array([ True]), array([0.01538431]), 0.050000...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>sex-biased female</td>\n",
       "      <td>5.588306</td>\n",
       "      <td>2.482411</td>\n",
       "      <td>sex-biased female</td>\n",
       "      <td>11</td>\n",
       "      <td>1.812922</td>\n",
       "      <td>2.482411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2878</th>\n",
       "      <td>DB00877</td>\n",
       "      <td>sirolimus</td>\n",
       "      <td>10065042</td>\n",
       "      <td>immune reconstitution inflammatory syndrome</td>\n",
       "      <td>general disorders and administration site cond...</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0.048753</td>\n",
       "      <td>(array([ True]), array([0.04875321]), 0.050000...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>sex-biased male</td>\n",
       "      <td>0.154750</td>\n",
       "      <td>-2.691985</td>\n",
       "      <td>sex-biased male</td>\n",
       "      <td>9</td>\n",
       "      <td>1.311997</td>\n",
       "      <td>-2.691985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2879</th>\n",
       "      <td>DB00877</td>\n",
       "      <td>sirolimus</td>\n",
       "      <td>10076476</td>\n",
       "      <td>product use in unapproved indication</td>\n",
       "      <td>injury, poisoning and procedural complications</td>\n",
       "      <td>231</td>\n",
       "      <td>124</td>\n",
       "      <td>0.000167</td>\n",
       "      <td>(array([ True]), array([0.00016682]), 0.050000...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>sex-biased male</td>\n",
       "      <td>0.655121</td>\n",
       "      <td>-0.610166</td>\n",
       "      <td>sex-biased male</td>\n",
       "      <td>355</td>\n",
       "      <td>3.777754</td>\n",
       "      <td>-0.610166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2880</th>\n",
       "      <td>DB00877</td>\n",
       "      <td>sirolimus</td>\n",
       "      <td>10037394</td>\n",
       "      <td>pulmonary haemorrhage</td>\n",
       "      <td>respiratory, thoracic and mediastinal disorders</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0.025996</td>\n",
       "      <td>(array([ True]), array([0.02599577]), 0.050000...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>sex-biased female</td>\n",
       "      <td>8.690413</td>\n",
       "      <td>3.119425</td>\n",
       "      <td>sex-biased female</td>\n",
       "      <td>8</td>\n",
       "      <td>1.585097</td>\n",
       "      <td>3.119425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2881</th>\n",
       "      <td>DB00877</td>\n",
       "      <td>sirolimus</td>\n",
       "      <td>10074248</td>\n",
       "      <td>herpes zoster meningoencephalitis</td>\n",
       "      <td>nervous system disorders</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0.028614</td>\n",
       "      <td>(array([ True]), array([0.02861407]), 0.050000...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>sex-biased male</td>\n",
       "      <td>0.137529</td>\n",
       "      <td>-2.862191</td>\n",
       "      <td>sex-biased male</td>\n",
       "      <td>10</td>\n",
       "      <td>1.543420</td>\n",
       "      <td>-2.862191</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Drug Drug_name        ADR  \\\n",
       "2850  DB00877  sirolimus  10012378   \n",
       "2851  DB00877  sirolimus  10035528   \n",
       "2852  DB00877  sirolimus  10028813   \n",
       "2853  DB00877  sirolimus  10040070   \n",
       "2854  DB00877  sirolimus  10070863   \n",
       "2855  DB00877  sirolimus  10075160   \n",
       "2856  DB00877  sirolimus  10008479   \n",
       "2857  DB00877  sirolimus  10062237   \n",
       "2858  DB00877  sirolimus  10012174   \n",
       "2859  DB00877  sirolimus  10018964   \n",
       "2860  DB00877  sirolimus  10057030   \n",
       "2861  DB00877  sirolimus  10010305   \n",
       "2862  DB00877  sirolimus  10011906   \n",
       "2863  DB00877  sirolimus  10021143   \n",
       "2864  DB00877  sirolimus  10003445   \n",
       "2865  DB00877  sirolimus  10014625   \n",
       "2866  DB00877  sirolimus  10012601   \n",
       "2867  DB00877  sirolimus  10023421   \n",
       "2868  DB00877  sirolimus  10058854   \n",
       "2869  DB00877  sirolimus  10028034   \n",
       "2870  DB00877  sirolimus  10022004   \n",
       "2871  DB00877  sirolimus  10028735   \n",
       "2872  DB00877  sirolimus  10046306   \n",
       "2873  DB00877  sirolimus  10043645   \n",
       "2874  DB00877  sirolimus  10021015   \n",
       "2875  DB00877  sirolimus  10060931   \n",
       "2876  DB00877  sirolimus  10014909   \n",
       "2877  DB00877  sirolimus  10019663   \n",
       "2878  DB00877  sirolimus  10065042   \n",
       "2879  DB00877  sirolimus  10076476   \n",
       "2880  DB00877  sirolimus  10037394   \n",
       "2881  DB00877  sirolimus  10074248   \n",
       "\n",
       "                                                PT_name  \\\n",
       "2850                                         depression   \n",
       "2851                           platelet count decreased   \n",
       "2852                                             nausea   \n",
       "2853                                       septic shock   \n",
       "2854                         toxicity to various agents   \n",
       "2855  graft versus host disease in gastrointestinal ...   \n",
       "2856                                         chest pain   \n",
       "2857                                   renal impairment   \n",
       "2858                                        dehydration   \n",
       "2859                                        haemoptysis   \n",
       "2860                           pneumatosis intestinalis   \n",
       "2861                                  confusional state   \n",
       "2862                                              death   \n",
       "2863                                            hypoxia   \n",
       "2864                                            ascites   \n",
       "2865                                     encephalopathy   \n",
       "2866                                  diabetes mellitus   \n",
       "2867                                    kidney fibrosis   \n",
       "2868                           cytomegalovirus viraemia   \n",
       "2869                                   mouth ulceration   \n",
       "2870                             influenza like illness   \n",
       "2871                                   nasal congestion   \n",
       "2872                  upper respiratory tract infection   \n",
       "2873                         thrombotic microangiopathy   \n",
       "2874                                       hypokalaemia   \n",
       "2875                               adenovirus infection   \n",
       "2876                              enterovirus infection   \n",
       "2877                                    hepatic failure   \n",
       "2878        immune reconstitution inflammatory syndrome   \n",
       "2879               product use in unapproved indication   \n",
       "2880                              pulmonary haemorrhage   \n",
       "2881                  herpes zoster meningoencephalitis   \n",
       "\n",
       "                                               SOC_name  Number adrs in Male  \\\n",
       "2850                              psychiatric disorders                    4   \n",
       "2851                                     investigations                    4   \n",
       "2852                         gastrointestinal disorders                   37   \n",
       "2853                        infections and infestations                   14   \n",
       "2854     injury, poisoning and procedural complications                   13   \n",
       "2855     injury, poisoning and procedural complications                    3   \n",
       "2856  general disorders and administration site cond...                   16   \n",
       "2857                        renal and urinary disorders                   29   \n",
       "2858                 metabolism and nutrition disorders                    5   \n",
       "2859                                 vascular disorders                    8   \n",
       "2860                         gastrointestinal disorders                   10   \n",
       "2861                           nervous system disorders                   18   \n",
       "2862  general disorders and administration site cond...                   58   \n",
       "2863    respiratory, thoracic and mediastinal disorders                   25   \n",
       "2864                         gastrointestinal disorders                    3   \n",
       "2865                           nervous system disorders                    6   \n",
       "2866                 metabolism and nutrition disorders                    5   \n",
       "2867                        renal and urinary disorders                    4   \n",
       "2868                        infections and infestations                   15   \n",
       "2869                         gastrointestinal disorders                    4   \n",
       "2870  general disorders and administration site cond...                    9   \n",
       "2871    respiratory, thoracic and mediastinal disorders                    8   \n",
       "2872                        infections and infestations                    4   \n",
       "2873               blood and lymphatic system disorders                    5   \n",
       "2874                 metabolism and nutrition disorders                    2   \n",
       "2875                        infections and infestations                   16   \n",
       "2876                        infections and infestations                    1   \n",
       "2877                            hepatobiliary disorders                    2   \n",
       "2878  general disorders and administration site cond...                    8   \n",
       "2879     injury, poisoning and procedural complications                  231   \n",
       "2880    respiratory, thoracic and mediastinal disorders                    1   \n",
       "2881                           nervous system disorders                    9   \n",
       "\n",
       "      Number adrs in Female   P-value  \\\n",
       "2850                     11  0.035147   \n",
       "2851                     14  0.007180   \n",
       "2852                     50  0.017054   \n",
       "2853                     29  0.003144   \n",
       "2854                     26  0.006002   \n",
       "2855                     10  0.024279   \n",
       "2856                      2  0.003608   \n",
       "2857                     10  0.022597   \n",
       "2858                     16  0.003977   \n",
       "2859                      1  0.048753   \n",
       "2860                      1  0.028602   \n",
       "2861                      3  0.006770   \n",
       "2862                     25  0.007606   \n",
       "2863                      7  0.011528   \n",
       "2864                     11  0.013708   \n",
       "2865                     14  0.025073   \n",
       "2866                     20  0.000410   \n",
       "2867                     13  0.012320   \n",
       "2868                      2  0.006091   \n",
       "2869                     16  0.002383   \n",
       "2870                      1  0.028614   \n",
       "2871                      1  0.048753   \n",
       "2872                     11  0.035147   \n",
       "2873                     13  0.029741   \n",
       "2874                     10  0.008156   \n",
       "2875                      1  0.000881   \n",
       "2876                      8  0.013409   \n",
       "2877                      9  0.015384   \n",
       "2878                      1  0.048753   \n",
       "2879                    124  0.000167   \n",
       "2880                      7  0.025996   \n",
       "2881                      1  0.028614   \n",
       "\n",
       "                                            p_value_FDR Significance  \\\n",
       "2850  (array([ True]), array([0.03514673]), 0.050000...          Yes   \n",
       "2851  (array([ True]), array([0.0071797]), 0.0500000...          Yes   \n",
       "2852  (array([ True]), array([0.01705352]), 0.050000...          Yes   \n",
       "2853  (array([ True]), array([0.00314356]), 0.050000...          Yes   \n",
       "2854  (array([ True]), array([0.00600241]), 0.050000...          Yes   \n",
       "2855  (array([ True]), array([0.02427924]), 0.050000...          Yes   \n",
       "2856  (array([ True]), array([0.00360844]), 0.050000...          Yes   \n",
       "2857  (array([ True]), array([0.02259651]), 0.050000...          Yes   \n",
       "2858  (array([ True]), array([0.00397712]), 0.050000...          Yes   \n",
       "2859  (array([ True]), array([0.04875321]), 0.050000...          Yes   \n",
       "2860  (array([ True]), array([0.02860178]), 0.050000...          Yes   \n",
       "2861  (array([ True]), array([0.00676959]), 0.050000...          Yes   \n",
       "2862  (array([ True]), array([0.00760574]), 0.050000...          Yes   \n",
       "2863  (array([ True]), array([0.01152798]), 0.050000...          Yes   \n",
       "2864  (array([ True]), array([0.01370824]), 0.050000...          Yes   \n",
       "2865  (array([ True]), array([0.02507261]), 0.050000...          Yes   \n",
       "2866  (array([ True]), array([0.00040994]), 0.050000...          Yes   \n",
       "2867  (array([ True]), array([0.01232045]), 0.050000...          Yes   \n",
       "2868  (array([ True]), array([0.0060914]), 0.0500000...          Yes   \n",
       "2869  (array([ True]), array([0.00238264]), 0.050000...          Yes   \n",
       "2870  (array([ True]), array([0.02861407]), 0.050000...          Yes   \n",
       "2871  (array([ True]), array([0.04875321]), 0.050000...          Yes   \n",
       "2872  (array([ True]), array([0.03514673]), 0.050000...          Yes   \n",
       "2873  (array([ True]), array([0.02974085]), 0.050000...          Yes   \n",
       "2874  (array([ True]), array([0.00815556]), 0.050000...          Yes   \n",
       "2875  (array([ True]), array([0.00088095]), 0.050000...          Yes   \n",
       "2876  (array([ True]), array([0.01340937]), 0.050000...          Yes   \n",
       "2877  (array([ True]), array([0.01538431]), 0.050000...          Yes   \n",
       "2878  (array([ True]), array([0.04875321]), 0.050000...          Yes   \n",
       "2879  (array([ True]), array([0.00016682]), 0.050000...          Yes   \n",
       "2880  (array([ True]), array([0.02599577]), 0.050000...          Yes   \n",
       "2881  (array([ True]), array([0.02861407]), 0.050000...          Yes   \n",
       "\n",
       "             Sex-biased  odds_ratio  Log2_Odds_ratio sex_biased_log_oddratio  \\\n",
       "2850  sex-biased female    3.415398         1.772054       sex-biased female   \n",
       "2851  sex-biased female    4.350024         2.121023       sex-biased female   \n",
       "2852  sex-biased female    1.683418         0.751394       sex-biased female   \n",
       "2853  sex-biased female    2.578851         1.366728       sex-biased female   \n",
       "2854  sex-biased female    2.488598         1.315333       sex-biased female   \n",
       "2855  sex-biased female    4.139681         2.049520       sex-biased female   \n",
       "2856    sex-biased male    0.154547        -2.693887         sex-biased male   \n",
       "2857    sex-biased male    0.426076        -1.230817         sex-biased male   \n",
       "2858  sex-biased female    3.978316         1.992158       sex-biased female   \n",
       "2859    sex-biased male    0.154750        -2.691985         sex-biased male   \n",
       "2860    sex-biased male    0.123752        -3.014475         sex-biased male   \n",
       "2861    sex-biased male    0.206031        -2.279064         sex-biased male   \n",
       "2862    sex-biased male    0.531501        -0.911855         sex-biased male   \n",
       "2863    sex-biased male    0.345994        -1.531182         sex-biased male   \n",
       "2864  sex-biased female    4.554750         2.187372       sex-biased female   \n",
       "2865  sex-biased female    2.898887         1.535499       sex-biased female   \n",
       "2866  sex-biased female    4.977713         2.315483       sex-biased female   \n",
       "2867  sex-biased female    4.038331         2.013759       sex-biased female   \n",
       "2868    sex-biased male    0.164882        -2.600496         sex-biased male   \n",
       "2869  sex-biased female    4.973863         2.314367       sex-biased female   \n",
       "2870    sex-biased male    0.137529        -2.862191         sex-biased male   \n",
       "2871    sex-biased male    0.154750        -2.691985         sex-biased male   \n",
       "2872  sex-biased female    3.415398         1.772054       sex-biased female   \n",
       "2873  sex-biased female    3.230036         1.691550       sex-biased female   \n",
       "2874  sex-biased female    6.210730         2.634763       sex-biased female   \n",
       "2875    sex-biased male    0.077255        -3.694235         sex-biased male   \n",
       "2876  sex-biased female    9.934300         3.312418       sex-biased female   \n",
       "2877  sex-biased female    5.588306         2.482411       sex-biased female   \n",
       "2878    sex-biased male    0.154750        -2.691985         sex-biased male   \n",
       "2879    sex-biased male    0.655121        -0.610166         sex-biased male   \n",
       "2880  sex-biased female    8.690413         3.119425       sex-biased female   \n",
       "2881    sex-biased male    0.137529        -2.862191         sex-biased male   \n",
       "\n",
       "      Sum_ADRS  log_p_values  log_odd_ratios  \n",
       "2850        15      1.454115        1.772054  \n",
       "2851        18      2.143894        2.121023  \n",
       "2852        87      1.768186        0.751394  \n",
       "2853        43      2.502579        1.366728  \n",
       "2854        39      2.221675        1.315333  \n",
       "2855        13      1.614765        2.049520  \n",
       "2856        18      2.442681       -2.693887  \n",
       "2857        39      1.645959       -1.230817  \n",
       "2858        21      2.400431        1.992158  \n",
       "2859         9      1.311997       -2.691985  \n",
       "2860        11      1.543607       -3.014475  \n",
       "2861        21      2.169438       -2.279064  \n",
       "2862        83      2.118858       -0.911855  \n",
       "2863        32      1.938247       -1.531182  \n",
       "2864        14      1.863018        2.187372  \n",
       "2865        20      1.600801        1.535499  \n",
       "2866        25      3.387284        2.315483  \n",
       "2867        17      1.909373        2.013759  \n",
       "2868        17      2.215283       -2.600496  \n",
       "2869        20      2.622941        2.314367  \n",
       "2870        10      1.543420       -2.862191  \n",
       "2871         9      1.311997       -2.691985  \n",
       "2872        15      1.454115        1.772054  \n",
       "2873        18      1.526647        1.691550  \n",
       "2874        12      2.088546        2.634763  \n",
       "2875        17      3.055047       -3.694235  \n",
       "2876         9      1.872592        3.312418  \n",
       "2877        11      1.812922        2.482411  \n",
       "2878         9      1.311997       -2.691985  \n",
       "2879       355      3.777754       -0.610166  \n",
       "2880         8      1.585097        3.119425  \n",
       "2881        10      1.543420       -2.862191  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sirolimus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sex_biased_log_oddratio\n",
       "sex-biased female    18\n",
       "sex-biased male      14\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " pd.value_counts(df_sirolimus[\"sex_biased_log_oddratio\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "18+14"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SOC_name</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>infections and infestations</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>gastrointestinal disorders</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>general disorders and administration site cond...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>injury, poisoning and procedural complications</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>metabolism and nutrition disorders</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>nervous system disorders</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>respiratory, thoracic and mediastinal disorders</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>renal and urinary disorders</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>psychiatric disorders</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>investigations</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>vascular disorders</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>blood and lymphatic system disorders</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>hepatobiliary disorders</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             SOC_name  count\n",
       "0                         infections and infestations      5\n",
       "1                          gastrointestinal disorders      4\n",
       "2   general disorders and administration site cond...      4\n",
       "3      injury, poisoning and procedural complications      3\n",
       "4                  metabolism and nutrition disorders      3\n",
       "5                            nervous system disorders      3\n",
       "6     respiratory, thoracic and mediastinal disorders      3\n",
       "7                         renal and urinary disorders      2\n",
       "8                               psychiatric disorders      1\n",
       "9                                      investigations      1\n",
       "10                                 vascular disorders      1\n",
       "11               blood and lymphatic system disorders      1\n",
       "12                            hepatobiliary disorders      1"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " pd.value_counts(df_sirolimus[\"SOC_name\"]).reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tamoxifen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tamoxifen = df_drug_adr_name_filter[df_drug_adr_name_filter[\"Drug_name \"]==\"tamoxifen\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-20-7a7aaf20bed2>:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_tamoxifen['log_p_values'] = -np.log10(df_tamoxifen['P-value'])\n",
      "<ipython-input-20-7a7aaf20bed2>:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_tamoxifen['log_odd_ratios'] = np.log2(df_tamoxifen['odds_ratio'])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "# Cálculo del logaritmo negativo base 10 de los p-values\n",
    "df_tamoxifen['log_p_values'] = -np.log10(df_tamoxifen['P-value'])\n",
    "\n",
    "# Cálculo del logaritmo base 2 del odd ratio\n",
    "df_tamoxifen['log_odd_ratios'] = np.log2(df_tamoxifen['odds_ratio'])\n",
    "\n",
    "# Separar los puntos en dos grupos según el signo del odd ratio\n",
    "odd_ratio_pos = df_tamoxifen['log_odd_ratios'][df_tamoxifen['log_odd_ratios'] > 0]\n",
    "odd_ratio_neg = df_tamoxifen['log_odd_ratios'][df_tamoxifen['log_odd_ratios'] <= 0]\n",
    "\n",
    "# Plot\n",
    "plt.figure(figsize=(15, 10))\n",
    "plt.scatter(odd_ratio_pos,df_tamoxifen['log_p_values'][df_tamoxifen['log_odd_ratios'] > 0], color='red', alpha=0.5, label='Sex-biased ADR Female')\n",
    "plt.scatter(odd_ratio_neg, df_tamoxifen['log_p_values'][df_tamoxifen['log_odd_ratios'] <= 0], color='blue', alpha=0.5, label='Sex-biased ADR Male')\n",
    "plt.axhline(y=-np.log10(0.05), color='green', linestyle='--')  # Línea para indicar el nivel de significancia\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "plt.xlabel('Log2(Odd Ratio)')\n",
    "plt.ylabel('-log10(p-value)')\n",
    "plt.title('Volcano Plot')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.savefig(\"volcano_plot_adrs_tamoxifen.svg\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-24-75e289a5e087>:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_tamoxifen['log_p_values'] = -np.log10(df_tamoxifen['P-value'])\n",
      "<ipython-input-24-75e289a5e087>:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_tamoxifen['log_odd_ratios'] = np.log2(df_tamoxifen['odds_ratio'])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "# Cálculo del logaritmo negativo base 10 de los p-values\n",
    "df_tamoxifen['log_p_values'] = -np.log10(df_tamoxifen['P-value'])\n",
    "\n",
    "# Cálculo del logaritmo base 2 del odd ratio\n",
    "df_tamoxifen['log_odd_ratios'] = np.log2(df_tamoxifen['odds_ratio'])\n",
    "\n",
    "# Separar los puntos en dos grupos según el signo del odd ratio\n",
    "odd_ratio_pos = df_tamoxifen['log_odd_ratios'][df_tamoxifen['log_odd_ratios'] > 0]\n",
    "odd_ratio_neg = df_tamoxifen['log_odd_ratios'][df_tamoxifen['log_odd_ratios'] <= 0]\n",
    "\n",
    "# Plot\n",
    "plt.figure(figsize=(15, 10))\n",
    "plt.scatter(odd_ratio_pos,df_tamoxifen['log_p_values'][df_tamoxifen['log_odd_ratios'] > 0], color='red', alpha=0.5, label='Sex-biased ADR Female')\n",
    "plt.scatter(odd_ratio_neg, df_tamoxifen['log_p_values'][df_tamoxifen['log_odd_ratios'] <= 0], color='blue', alpha=0.5, label='Sex-biased ADR Male')\n",
    "plt.axhline(y=-np.log10(0.05), color='green', linestyle='--')  # Línea para indicar el nivel de significancia\n",
    "\n",
    "\n",
    "for x, y, nombre in zip(df_tamoxifen['log_odd_ratios'], df_tamoxifen['log_p_values'], df_tamoxifen['PT_name']):\n",
    "    plt.text(x, y, nombre, fontsize=8, ha='center', va='top')\n",
    "\n",
    "\n",
    "plt.xlabel('Log2(Odd Ratio)')\n",
    "plt.ylabel('-log10(p-value)')\n",
    "plt.title('Volcano Plot')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.savefig(\"volcano_plot_adrs_tamoxifen_names.svg\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Drug</th>\n",
       "      <th>Drug_name</th>\n",
       "      <th>ADR</th>\n",
       "      <th>PT_name</th>\n",
       "      <th>SOC_name</th>\n",
       "      <th>Number adrs in Male</th>\n",
       "      <th>Number adrs in Female</th>\n",
       "      <th>P-value</th>\n",
       "      <th>p_value_FDR</th>\n",
       "      <th>Significance</th>\n",
       "      <th>Sex-biased</th>\n",
       "      <th>odds_ratio</th>\n",
       "      <th>Log2_Odds_ratio</th>\n",
       "      <th>sex_biased_log_oddratio</th>\n",
       "      <th>Sum_ADRS</th>\n",
       "      <th>log_p_values</th>\n",
       "      <th>log_odd_ratios</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3648</th>\n",
       "      <td>DB00675</td>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>10053762</td>\n",
       "      <td>off label use</td>\n",
       "      <td>injury, poisoning and procedural complications</td>\n",
       "      <td>7</td>\n",
       "      <td>61</td>\n",
       "      <td>0.047586</td>\n",
       "      <td>(array([ True]), array([0.04758603]), 0.050000...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>sex-biased female</td>\n",
       "      <td>0.442636</td>\n",
       "      <td>-1.175808</td>\n",
       "      <td>sex-biased male</td>\n",
       "      <td>68</td>\n",
       "      <td>1.322521</td>\n",
       "      <td>-1.175808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3649</th>\n",
       "      <td>DB00675</td>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>10062194</td>\n",
       "      <td>metastasis</td>\n",
       "      <td>neoplasms benign, malignant and unspecified (i...</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0.042700</td>\n",
       "      <td>(array([ True]), array([0.0427005]), 0.0500000...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>sex-biased female</td>\n",
       "      <td>0.128031</td>\n",
       "      <td>-2.965434</td>\n",
       "      <td>sex-biased male</td>\n",
       "      <td>7</td>\n",
       "      <td>1.369567</td>\n",
       "      <td>-2.965434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3650</th>\n",
       "      <td>DB00675</td>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>10010774</td>\n",
       "      <td>constipation</td>\n",
       "      <td>gastrointestinal disorders</td>\n",
       "      <td>6</td>\n",
       "      <td>34</td>\n",
       "      <td>0.012351</td>\n",
       "      <td>(array([ True]), array([0.01235088]), 0.050000...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>sex-biased female</td>\n",
       "      <td>0.287424</td>\n",
       "      <td>-1.798750</td>\n",
       "      <td>sex-biased male</td>\n",
       "      <td>40</td>\n",
       "      <td>1.908302</td>\n",
       "      <td>-1.798750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3651</th>\n",
       "      <td>DB00675</td>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>10035528</td>\n",
       "      <td>platelet count decreased</td>\n",
       "      <td>investigations</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>0.014687</td>\n",
       "      <td>(array([ True]), array([0.01468721]), 0.050000...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>sex-biased female</td>\n",
       "      <td>0.203733</td>\n",
       "      <td>-2.295252</td>\n",
       "      <td>sex-biased male</td>\n",
       "      <td>20</td>\n",
       "      <td>1.833061</td>\n",
       "      <td>-2.295252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3652</th>\n",
       "      <td>DB00675</td>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>10058202</td>\n",
       "      <td>cystoid macular oedema</td>\n",
       "      <td>eye disorders</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000449</td>\n",
       "      <td>(array([ True]), array([0.00044926]), 0.050000...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>sex-biased male</td>\n",
       "      <td>0.016993</td>\n",
       "      <td>-5.878919</td>\n",
       "      <td>sex-biased male</td>\n",
       "      <td>4</td>\n",
       "      <td>3.347499</td>\n",
       "      <td>-5.878919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3653</th>\n",
       "      <td>DB00675</td>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>10039203</td>\n",
       "      <td>road traffic accident</td>\n",
       "      <td>injury, poisoning and procedural complications</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.013453</td>\n",
       "      <td>(array([ True]), array([0.01345261]), 0.050000...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>no sex-biased</td>\n",
       "      <td>0.051183</td>\n",
       "      <td>-4.288202</td>\n",
       "      <td>sex-biased male</td>\n",
       "      <td>4</td>\n",
       "      <td>1.871193</td>\n",
       "      <td>-4.288202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3654</th>\n",
       "      <td>DB00675</td>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>10076309</td>\n",
       "      <td>product use issue</td>\n",
       "      <td>injury, poisoning and procedural complications</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>0.004429</td>\n",
       "      <td>(array([ True]), array([0.00442893]), 0.050000...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>sex-biased female</td>\n",
       "      <td>0.182730</td>\n",
       "      <td>-2.452211</td>\n",
       "      <td>sex-biased male</td>\n",
       "      <td>23</td>\n",
       "      <td>2.353702</td>\n",
       "      <td>-2.452211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3655</th>\n",
       "      <td>DB00675</td>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>10010305</td>\n",
       "      <td>confusional state</td>\n",
       "      <td>nervous system disorders</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>0.007850</td>\n",
       "      <td>(array([ True]), array([0.00784975]), 0.050000...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>sex-biased female</td>\n",
       "      <td>0.102057</td>\n",
       "      <td>-3.292558</td>\n",
       "      <td>sex-biased male</td>\n",
       "      <td>9</td>\n",
       "      <td>2.105144</td>\n",
       "      <td>-3.292558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3656</th>\n",
       "      <td>DB00675</td>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>10061218</td>\n",
       "      <td>inflammation</td>\n",
       "      <td>general disorders and administration site cond...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.013453</td>\n",
       "      <td>(array([ True]), array([0.01345261]), 0.050000...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>no sex-biased</td>\n",
       "      <td>0.051183</td>\n",
       "      <td>-4.288202</td>\n",
       "      <td>sex-biased male</td>\n",
       "      <td>4</td>\n",
       "      <td>1.871193</td>\n",
       "      <td>-4.288202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3657</th>\n",
       "      <td>DB00675</td>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>10043607</td>\n",
       "      <td>thrombosis</td>\n",
       "      <td>vascular disorders</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>0.003826</td>\n",
       "      <td>(array([ True]), array([0.00382555]), 0.050000...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>sex-biased female</td>\n",
       "      <td>0.127184</td>\n",
       "      <td>-2.975006</td>\n",
       "      <td>sex-biased male</td>\n",
       "      <td>14</td>\n",
       "      <td>2.417306</td>\n",
       "      <td>-2.975006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3658</th>\n",
       "      <td>DB00675</td>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>10066901</td>\n",
       "      <td>treatment failure</td>\n",
       "      <td>general disorders and administration site cond...</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0.021699</td>\n",
       "      <td>(array([ True]), array([0.02169942]), 0.050000...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>sex-biased female</td>\n",
       "      <td>0.076789</td>\n",
       "      <td>-3.702960</td>\n",
       "      <td>sex-biased male</td>\n",
       "      <td>5</td>\n",
       "      <td>1.663552</td>\n",
       "      <td>-3.702960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3659</th>\n",
       "      <td>DB00675</td>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>10008531</td>\n",
       "      <td>chills</td>\n",
       "      <td>general disorders and administration site cond...</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>0.007850</td>\n",
       "      <td>(array([ True]), array([0.00784975]), 0.050000...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>sex-biased female</td>\n",
       "      <td>0.102057</td>\n",
       "      <td>-3.292558</td>\n",
       "      <td>sex-biased male</td>\n",
       "      <td>9</td>\n",
       "      <td>2.105144</td>\n",
       "      <td>-3.292558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3660</th>\n",
       "      <td>DB00675</td>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>10000125</td>\n",
       "      <td>abnormal dreams</td>\n",
       "      <td>psychiatric disorders</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0.002086</td>\n",
       "      <td>(array([ True]), array([0.00208644]), 0.050000...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>no sex-biased</td>\n",
       "      <td>0.050999</td>\n",
       "      <td>-4.293397</td>\n",
       "      <td>sex-biased male</td>\n",
       "      <td>6</td>\n",
       "      <td>2.680594</td>\n",
       "      <td>-4.293397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3661</th>\n",
       "      <td>DB00675</td>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>10019063</td>\n",
       "      <td>hallucination</td>\n",
       "      <td>psychiatric disorders</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>0.005428</td>\n",
       "      <td>(array([ True]), array([0.00542822]), 0.050000...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>sex-biased female</td>\n",
       "      <td>0.085031</td>\n",
       "      <td>-3.555872</td>\n",
       "      <td>sex-biased male</td>\n",
       "      <td>8</td>\n",
       "      <td>2.265342</td>\n",
       "      <td>-3.555872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3662</th>\n",
       "      <td>DB00675</td>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>10055798</td>\n",
       "      <td>haemorrhage</td>\n",
       "      <td>vascular disorders</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0.042700</td>\n",
       "      <td>(array([ True]), array([0.0427005]), 0.0500000...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>sex-biased female</td>\n",
       "      <td>0.128031</td>\n",
       "      <td>-2.965434</td>\n",
       "      <td>sex-biased male</td>\n",
       "      <td>7</td>\n",
       "      <td>1.369567</td>\n",
       "      <td>-2.965434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3663</th>\n",
       "      <td>DB00675</td>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>10003553</td>\n",
       "      <td>asthma</td>\n",
       "      <td>immune system disorders</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0.042700</td>\n",
       "      <td>(array([ True]), array([0.0427005]), 0.0500000...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>sex-biased female</td>\n",
       "      <td>0.128031</td>\n",
       "      <td>-2.965434</td>\n",
       "      <td>sex-biased male</td>\n",
       "      <td>7</td>\n",
       "      <td>1.369567</td>\n",
       "      <td>-2.965434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3664</th>\n",
       "      <td>DB00675</td>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>10051055</td>\n",
       "      <td>deep vein thrombosis</td>\n",
       "      <td>vascular disorders</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>0.010812</td>\n",
       "      <td>(array([ True]), array([0.01081199]), 0.050000...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>sex-biased female</td>\n",
       "      <td>0.119089</td>\n",
       "      <td>-3.069885</td>\n",
       "      <td>sex-biased male</td>\n",
       "      <td>10</td>\n",
       "      <td>1.966095</td>\n",
       "      <td>-3.069885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3665</th>\n",
       "      <td>DB00675</td>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>10038435</td>\n",
       "      <td>renal failure</td>\n",
       "      <td>renal and urinary disorders</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0.031506</td>\n",
       "      <td>(array([ True]), array([0.0315059]), 0.0500000...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>sex-biased female</td>\n",
       "      <td>0.102405</td>\n",
       "      <td>-3.287642</td>\n",
       "      <td>sex-biased male</td>\n",
       "      <td>6</td>\n",
       "      <td>1.501608</td>\n",
       "      <td>-3.287642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3666</th>\n",
       "      <td>DB00675</td>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>10074903</td>\n",
       "      <td>intentional product misuse</td>\n",
       "      <td>injury, poisoning and procedural complications</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0.021699</td>\n",
       "      <td>(array([ True]), array([0.02169942]), 0.050000...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>sex-biased female</td>\n",
       "      <td>0.076789</td>\n",
       "      <td>-3.702960</td>\n",
       "      <td>sex-biased male</td>\n",
       "      <td>5</td>\n",
       "      <td>1.663552</td>\n",
       "      <td>-3.702960</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Drug Drug_name        ADR                     PT_name  \\\n",
       "3648  DB00675  tamoxifen  10053762               off label use   \n",
       "3649  DB00675  tamoxifen  10062194                  metastasis   \n",
       "3650  DB00675  tamoxifen  10010774                constipation   \n",
       "3651  DB00675  tamoxifen  10035528    platelet count decreased   \n",
       "3652  DB00675  tamoxifen  10058202      cystoid macular oedema   \n",
       "3653  DB00675  tamoxifen  10039203       road traffic accident   \n",
       "3654  DB00675  tamoxifen  10076309           product use issue   \n",
       "3655  DB00675  tamoxifen  10010305           confusional state   \n",
       "3656  DB00675  tamoxifen  10061218                inflammation   \n",
       "3657  DB00675  tamoxifen  10043607                  thrombosis   \n",
       "3658  DB00675  tamoxifen  10066901           treatment failure   \n",
       "3659  DB00675  tamoxifen  10008531                      chills   \n",
       "3660  DB00675  tamoxifen  10000125             abnormal dreams   \n",
       "3661  DB00675  tamoxifen  10019063               hallucination   \n",
       "3662  DB00675  tamoxifen  10055798                 haemorrhage   \n",
       "3663  DB00675  tamoxifen  10003553                      asthma   \n",
       "3664  DB00675  tamoxifen  10051055        deep vein thrombosis   \n",
       "3665  DB00675  tamoxifen  10038435               renal failure   \n",
       "3666  DB00675  tamoxifen  10074903  intentional product misuse   \n",
       "\n",
       "                                               SOC_name  Number adrs in Male  \\\n",
       "3648     injury, poisoning and procedural complications                    7   \n",
       "3649  neoplasms benign, malignant and unspecified (i...                    2   \n",
       "3650                         gastrointestinal disorders                    6   \n",
       "3651                                     investigations                    4   \n",
       "3652                                      eye disorders                    3   \n",
       "3653     injury, poisoning and procedural complications                    2   \n",
       "3654     injury, poisoning and procedural complications                    5   \n",
       "3655                           nervous system disorders                    3   \n",
       "3656  general disorders and administration site cond...                    2   \n",
       "3657                                 vascular disorders                    4   \n",
       "3658  general disorders and administration site cond...                    2   \n",
       "3659  general disorders and administration site cond...                    3   \n",
       "3660                              psychiatric disorders                    3   \n",
       "3661                              psychiatric disorders                    3   \n",
       "3662                                 vascular disorders                    2   \n",
       "3663                            immune system disorders                    2   \n",
       "3664                                 vascular disorders                    3   \n",
       "3665                        renal and urinary disorders                    2   \n",
       "3666     injury, poisoning and procedural complications                    2   \n",
       "\n",
       "      Number adrs in Female   P-value  \\\n",
       "3648                     61  0.047586   \n",
       "3649                      5  0.042700   \n",
       "3650                     34  0.012351   \n",
       "3651                     16  0.014687   \n",
       "3652                      1  0.000449   \n",
       "3653                      2  0.013453   \n",
       "3654                     18  0.004429   \n",
       "3655                      6  0.007850   \n",
       "3656                      2  0.013453   \n",
       "3657                     10  0.003826   \n",
       "3658                      3  0.021699   \n",
       "3659                      6  0.007850   \n",
       "3660                      3  0.002086   \n",
       "3661                      5  0.005428   \n",
       "3662                      5  0.042700   \n",
       "3663                      5  0.042700   \n",
       "3664                      7  0.010812   \n",
       "3665                      4  0.031506   \n",
       "3666                      3  0.021699   \n",
       "\n",
       "                                            p_value_FDR Significance  \\\n",
       "3648  (array([ True]), array([0.04758603]), 0.050000...          Yes   \n",
       "3649  (array([ True]), array([0.0427005]), 0.0500000...          Yes   \n",
       "3650  (array([ True]), array([0.01235088]), 0.050000...          Yes   \n",
       "3651  (array([ True]), array([0.01468721]), 0.050000...          Yes   \n",
       "3652  (array([ True]), array([0.00044926]), 0.050000...          Yes   \n",
       "3653  (array([ True]), array([0.01345261]), 0.050000...          Yes   \n",
       "3654  (array([ True]), array([0.00442893]), 0.050000...          Yes   \n",
       "3655  (array([ True]), array([0.00784975]), 0.050000...          Yes   \n",
       "3656  (array([ True]), array([0.01345261]), 0.050000...          Yes   \n",
       "3657  (array([ True]), array([0.00382555]), 0.050000...          Yes   \n",
       "3658  (array([ True]), array([0.02169942]), 0.050000...          Yes   \n",
       "3659  (array([ True]), array([0.00784975]), 0.050000...          Yes   \n",
       "3660  (array([ True]), array([0.00208644]), 0.050000...          Yes   \n",
       "3661  (array([ True]), array([0.00542822]), 0.050000...          Yes   \n",
       "3662  (array([ True]), array([0.0427005]), 0.0500000...          Yes   \n",
       "3663  (array([ True]), array([0.0427005]), 0.0500000...          Yes   \n",
       "3664  (array([ True]), array([0.01081199]), 0.050000...          Yes   \n",
       "3665  (array([ True]), array([0.0315059]), 0.0500000...          Yes   \n",
       "3666  (array([ True]), array([0.02169942]), 0.050000...          Yes   \n",
       "\n",
       "             Sex-biased  odds_ratio  Log2_Odds_ratio sex_biased_log_oddratio  \\\n",
       "3648  sex-biased female    0.442636        -1.175808         sex-biased male   \n",
       "3649  sex-biased female    0.128031        -2.965434         sex-biased male   \n",
       "3650  sex-biased female    0.287424        -1.798750         sex-biased male   \n",
       "3651  sex-biased female    0.203733        -2.295252         sex-biased male   \n",
       "3652    sex-biased male    0.016993        -5.878919         sex-biased male   \n",
       "3653      no sex-biased    0.051183        -4.288202         sex-biased male   \n",
       "3654  sex-biased female    0.182730        -2.452211         sex-biased male   \n",
       "3655  sex-biased female    0.102057        -3.292558         sex-biased male   \n",
       "3656      no sex-biased    0.051183        -4.288202         sex-biased male   \n",
       "3657  sex-biased female    0.127184        -2.975006         sex-biased male   \n",
       "3658  sex-biased female    0.076789        -3.702960         sex-biased male   \n",
       "3659  sex-biased female    0.102057        -3.292558         sex-biased male   \n",
       "3660      no sex-biased    0.050999        -4.293397         sex-biased male   \n",
       "3661  sex-biased female    0.085031        -3.555872         sex-biased male   \n",
       "3662  sex-biased female    0.128031        -2.965434         sex-biased male   \n",
       "3663  sex-biased female    0.128031        -2.965434         sex-biased male   \n",
       "3664  sex-biased female    0.119089        -3.069885         sex-biased male   \n",
       "3665  sex-biased female    0.102405        -3.287642         sex-biased male   \n",
       "3666  sex-biased female    0.076789        -3.702960         sex-biased male   \n",
       "\n",
       "      Sum_ADRS  log_p_values  log_odd_ratios  \n",
       "3648        68      1.322521       -1.175808  \n",
       "3649         7      1.369567       -2.965434  \n",
       "3650        40      1.908302       -1.798750  \n",
       "3651        20      1.833061       -2.295252  \n",
       "3652         4      3.347499       -5.878919  \n",
       "3653         4      1.871193       -4.288202  \n",
       "3654        23      2.353702       -2.452211  \n",
       "3655         9      2.105144       -3.292558  \n",
       "3656         4      1.871193       -4.288202  \n",
       "3657        14      2.417306       -2.975006  \n",
       "3658         5      1.663552       -3.702960  \n",
       "3659         9      2.105144       -3.292558  \n",
       "3660         6      2.680594       -4.293397  \n",
       "3661         8      2.265342       -3.555872  \n",
       "3662         7      1.369567       -2.965434  \n",
       "3663         7      1.369567       -2.965434  \n",
       "3664        10      1.966095       -3.069885  \n",
       "3665         6      1.501608       -3.287642  \n",
       "3666         5      1.663552       -3.702960  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tamoxifen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sex_biased_log_oddratio\n",
       "sex-biased male    19\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " pd.value_counts(df_tamoxifen[\"sex_biased_log_oddratio\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SOC_name</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>injury, poisoning and procedural complications</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>general disorders and administration site cond...</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>vascular disorders</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>psychiatric disorders</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>neoplasms benign, malignant and unspecified (i...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>gastrointestinal disorders</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>investigations</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>eye disorders</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>nervous system disorders</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>immune system disorders</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>renal and urinary disorders</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             SOC_name  count\n",
       "0      injury, poisoning and procedural complications      4\n",
       "1   general disorders and administration site cond...      3\n",
       "2                                  vascular disorders      3\n",
       "3                               psychiatric disorders      2\n",
       "4   neoplasms benign, malignant and unspecified (i...      1\n",
       "5                          gastrointestinal disorders      1\n",
       "6                                      investigations      1\n",
       "7                                       eye disorders      1\n",
       "8                            nervous system disorders      1\n",
       "9                             immune system disorders      1\n",
       "10                        renal and urinary disorders      1"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " pd.value_counts(df_tamoxifen[\"SOC_name\"]).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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