dataesr/scientific-paragraphs-categorization huggingface dataset:
METHODS First, we reported a case of a 52-year-old woman, who was suspected of ACS after the first administration of a PD-L1i and a fatal recurrence in another coronary area immediately after readministration. Subsequently, the French pharmacovigilance database was queried for all ICI-induced adverse drug reactions reported until March 30, 2019 (NCT03492242). Immune checkpoint inhibitor drugs studied were nivolumab, pembrolizumab, ipilimumab, atezolizumab, and avelumab. We selected all cases with at least one adverse effect listed in the MedDRA Standard Medical Query "ischemic heart disease". Finally, all the "ischemic heart diseases" cases were analyzed to identify those related to ACS with an evidence of coronary artery thrombosis on angiogram. Drug exposition was defined by the presence in the case safety reports of an ICI drug coded as "suspect".
DISCUSSION We report for the first time a case of recurrent ACS following ICI administration, leading to a fatal outcome and certain ADR causality. This presentation strongly suggests an ICI-related coronary toxicity by destabilization of previous nonsignificant atherosclerotic lesions after analysis of coronary angiograms before and after ICI administration. Four other cases of ACS associated with ICI were found in the French pharmacovigilance database, further supporting this concept. In the case we reported, after the first ACS episode we could not conclude that the ICI was the cause of the event because it was associated with a MEKi and the patient had risk factors. On the other hand, ACS recurrence after readministration of the ICI and discontinuation of the MEKi strongly suggested the involvement of the former.
The four previously reported cases of ACS were with nivolumab. At this stage of knowledge, we cannot explain why no ACS cases were reported with pembrolizumab, another PD-1i. Moreover, no previous cases of ACS were reported with PD-L1i. This might be related to the recent use of this drugs and the relative fewer number of treated patients. Our study had some limitations. First, data on coronary angiogram preceding ICI administration were lacking for other cases found in the analysis of the pharmacovigilance database. Nevertheless, the case described in this study is the only one for which we had previous angiogram, allowing us to strengthen our hypothesis. Moreover, the causality assessment according to the ADR guidelines was certain. Second, we cannot exclude the role of MEK inhibition in the coronary toxicity of the case we reported.
Limitations of this study The small number of participating laboratories in this study is a significant limitation. However, the five participating laboratories represent three countries and laboratories in academic, governmental, and industrial environments, arguing that the findings have broad implications. The lack of a commercially available certified reference material for total oxylipins also limits future direct comparisons to the data reported here. Future efforts in this area should expand the number of laboratories to no less than 7 and include a commercially available reference plasma (e. g. , NIST SRM 1950-Metabolites in Human Plasma) allowing the development of consensus values for the total oxylipin content of this material.
, 2016 Dancik et al. , 2013 Kasting and Miller, 2006 Miller and Kasting, 2010 Polak et al. , 2012). The empirical relationships are generally based on datasets obtained from several different publication sources, thus resulting in large heterogeneity. This makes it much more difficult to correlate skin penetration with chemical characteristics, which is needed for the development of in silico skin penetration models. Predictions from the in silico skin penetration models are anticipated to improve by replacing the empirical relationships for K and D with mechanistically based determinations. Our group has previously reviewed several different protocols for evaluating K and D  (Rothe et al. , 2017). In the current work the K assay tests each compound in dermatomed skin, dermis, whole epidermis (VE + SC), intact SC, delipidized SC and in the isolated SC lipids (obtained from the delipidization process).
Diffusion Coefficient (D) The cumulative amount profiles were analyzed as follows: Using the software, JMP Pro 11, a non-linear regression of the solution to Fick's 2nd law was performed against the cumulative amount profile Q(t)  (Moser et al. , 2001 Frasch and Barbero, 2008 Zhang et al. , 2009)  through the linear portion of the curve: = = Q t J t where J ss, total is identical to C v (the donor concentration) divided by R total (the diffusive resistance through the ABL + skin layer + Supor® filter in the case of SC, whole epidermis and FT skin, and through the ABL + dialysis membrane + skin layer + Supor® filter in the case of dermis). The parameter t lag is the lag time. The number of terms (n) in the sum in eq. 7 was set to 11. The area of penetration is fixed (A = 0. 64 cm 2 ). Regression yielded values for J ss, total and t lag after initial guesses had been specified.
Outlier analysis A statistically based outlier analysis was carried out on all of the K and D data as a way to help visualize potential data that should be further examined and determined if removal from the overall dataset is justified (Supplemental Figs. 2  and 3 ). Results from the outlier analysis identified 31 out of 1200 (dermatomed skin), 23 out of 1199 (whole epidermis), 16 out of 1194 (SC), 34 out of 1200 (dermis), 29 out of 1195 (delipidized SC) and 7 out of 1171 (SC lipids) as possible outliers for K and 25 out of 1476 (dermatomed skin), 38 out of 1672 (whole epidermis), 17 out of 1379 (SC) and 28 out of 1358 (dermis) for D. After reviewing the outliers identified by the statistical approach to determine which data points should be excluded or retained, the total number of data points excluded as statistical outliers were: 11 (dermatomed skin), 2 (whole epidermis), 8 (SC), 8 (dermis), 5 (delipidized SC) and 6 (SC lipids) for K and 10 (dermatomed skin), 23 (whole epidermis), 8 (SC) and 12 (dermis) for D.
Discussion The current work measured K and D values for dermatomed skin and isolated human skin layers for 50 compounds were specifically selected to be relevant to the cosmetics industry, including positive and negative reference chemicals for genotoxicity and/or skin sensitization. K values were measured in dermatomed skin, isolated dermis, whole epidermis, intact SC, delipidized SC and SC lipids by direct measurements of the radiolabeled chemicals in the tissue layers/lipid component vs. buffer samples. D determinations were made in dermatomed skin, dermis, whole epidermis and intact SC using a non-linear regression of the cumulative receptor fluid content of radiolabeled compound, fit to the solution of Fick's 2nd Law. The data was generated as part of a larger Cosmetics Europe project on dermal bioavailability and will be utilized to better understand the mechanistic behavior for how chemicals move into and through skin.
Several mechanistic skin penetration models currently exist which utilize empirical relationships for predicting the partitioning and diffusion of chemicals into and through the skin  (Chen et al. , 2016 Kasting and Miller, 2006 Miller and Kasting, 2010 Polak et al. , 2012). The empirical relationships are generally based on datasets obtained from several different publication sources, thus resulting in large heterogeneity. Predictions from the in silico skin penetration models are anticipated to improve by replacing the empirical relationships for K and D with mechanistically based determinations. Relatively similar D were measured for dermatomed skin, whole epidermis and intact SC. Each of these skin preparations contained the SC, the outermost layer of the skin, which generally provides the ratelimiting step for skin penetration.
The agreement between the new and existing K data helps to build confidence in the overall dataset. Several of the chemicals included in the current work also have existing D values in the literature, thus allowing us to compare to them. Comparisons (current work SC D vs literature SC D) of SC D values for caffeine (4. 7 E-08 vs 1. 8 to 1. 6 E-06 cm2/h), resorcinol (2. 9 E-09 vs 9. 1 E-07 cm2/h), 7-ethoxycoumarin (8. 1 E-08 vs 2. 45 E-06 cm2/h), testosterone (5. 4 E-09 vs 1. 8 E-07 cm2/h) and triclosan (1. 3 E-09 vs 1. 8 E-07 cm2/h) show that the D values vary more between studies than the K values did  (Surber et al. , 1990; Kruse et al. , 2007; Hansen et al. , 2008; Rothe et al. , 2017. This is not unique to the current dataset, as illustrated by the existing literature dataset for caffeine, where the SC D values range from 1.
Rheological studies All the rheological analyses were performed on an Anton Parr MCR102 Rheometer and all the data were analyzed using the Anton Parr RheoCompass software. The geometry was a stainless steel cone/plate (diameter 50 mm, angle 1 ° and gap 100 μm), which provided an homogeneous shear of the sample. The cone was equipped with a solvent trap to avoid evaporation during measurement. Thanks to Peltier diodes which were placed in the lower plate, it was possible to control the temperature with a precision of 0. 1 °C.
Tensile strength test The mucoadhesive capacity of the formulations was evaluated by measuring the force required to detach the formulations from a mucin film in a tensile test  [28, 36]. The measurements were performed on an Anton Parr MCR102 Rheometer and all the data were analyzed using Anton Parr RheoCompass software. The geometry was a stainless steel plate/plate (diameter 50 mm and gap 100 μm). A 5 % (weight/weight) porcine gastric mucin dispersion was previously prepared in sterile water. Mucin films were prepared directly on the lower plate: The mucin dispersion (300 µL) was placed and spread homogeneously on the plate. The plate was then heated for 3 min at 70 °C allowing the mucin dispersion to dry. Then, the hydrogel (400 µL) was carefully loaded on the lower plate, covering the mucin film.
After a contact of 1 min with the hydrogel with a force of 0. 3 N, the upper plate was moved upward at a constant speed of 10 mm/min until the complete break-up of the hydrogel. Six replicate analyses were performed for each formulation at 35 °C, using a fresh sample and a fresh mucin film in each case. The force of detachment as a function of displacement was recorded by the software. Then, the maximum detachment force (Fmax) was analyzed and compared with the data obtained from the gel strength study without mucin film. The difference between the two forces, ∆Fmax, indicates the mucoadhesion capacity of the hydrogel  [28]. The results are the mean ± SD of n=3 experiments.
Tensile strength test The maximum detachment forces of hydrogels (Fmax) were measured and compared in the presence and in the absence of mucin film. The results are presented in figure  5. When comparing Fmax values with mucin to those without mucin, here expressed as ∆Fmax, a significant increase was observed for all the formulations (p < 0. 001). This indicated a positive interaction between hydrogels and mucin which was coherent with data found on the mucoadhesive properties of gellan gum and HEC in the literature  [45]. Therefore, hydrogels could be retained by interacting with the mucin associated to the ocular surface, leading to enhanced ocular retention time.
Acknowledgements The authors would like to thank  Unither Pharmaceuticals  for financial support on this project, and  Pr. Moji C. Adeyeye  for early stage research training at  Roosevelt University, College of Pharmacy , and for her knowledge on ophthalmic in situ gelling delivery systems. In vivo imaging was performed at the  Life Imaging Facility of Université de Paris (Plateforme Imageries du Vivant, LIOPA) , supported by  France Life Imaging  (grant  ANR-11-INBS-0006 ) and  Infrastructures Biologie-Santé. The authors would also like to thank  White Rabbit  association for their help on rabbit's rehabilitation.
Acknowledgments: We thank all members of the SCAHU and PACEM platforms and  Lore Dubesset  for technical assistance. Funding: This work was supported by  INSERM ,  Atip Avenir program ,  Fondation Raoul Follereau-France ,  Agence Nationale de la Recherche (ANR) BU_   SPONT_HEAL  project ( InfectEra project)  and  MYCOPARADOX ANR  project , Angers University  ( REPAIR  project),  Société Française de Dermatologie , and the  Pays de la Loire region  ( STARTER  project). J. M. , L. A. , and A. A. acknowledge support from the  Laboratoire d'Excellence Integrative Biology of Emerging Infectious Diseases  (grant no. ANR-10-LABX-62-IBEID ) and the  ANR  under the  "Investments for the Future" program  (grant no. ANR-10-IAHU-01 ). A. A. acknowledges support from the  Fondation pour la Recherche Médicale  (grant no.
Drug-induced Mood-and Cognition-related adverse events (MCAEs) are often only detected during the clinical trial phases of drug development, or even after marketing, thus posing a major safety concern and a challenge for both pharmaceutical companies and clinicians. To fill some gaps in the understanding and elucidate potential biological mechanisms of action frequently associated with MCAEs, we present a unique workflow linking observational population data with the available knowledge at molecular, cellular, and psychopharmacology levels. It is based on statistical analysis of pharmacovigilance reports and subsequent signaling pathway analyses, followed by evidence-based expert manual curation of the outcomes. Our analysis: (a) ranked pharmaceuticals with high occurrence of such adverse events (AEs), based on disproportionality analysis of the FDA Adverse Event Reporting System (FAERS) database, and (b) identified 120 associated genes and common pathway nodes possibly underlying MCAEs.
2018). Antiepileptic drugs such as perampanel have also been associated with adverse mood changes (e. g. depression) and effects on cognition  (Afzal et al. 2017; Goji and Kanemoto 2019). In patients with lower urinary tract symptoms  (Muderrisoglu et al. 2019) , muscarinic antagonists and 5-alpha-reductase inhibitors were associated with impaired cognition and depression, respectively. Recently, both the United States and European regulators issued a warning about the potential of neuropsychiatric AEs of fluoroquinolones, including suicidal ideation  (Bennett et al. 2019). These AEs were largely revealed by PharmacoEpidemiology (PE) and PharmacoVigilance (PV) data. Such databases include the FDA Adverse Event Reporting System (FAERS) database  (FDA 2018 ) that retrieves information on adverse event and medication error reports submitted to the FDA; the Global Individual Case Safety (ICSRs) Reports Database System (VigiBase)  (Lindquist 2008) , that collects reports of suspected adverse effects of medicines worldwide, and Eudravigilance  (Postigo et al.
2018) , the European Union data processing network and management system for reporting and evaluation of suspected adverse drug reactions. As a concept complementary to PV databases, Adverse Outcome Pathways (AOPs) help to understand the mechanisms leading to adverse outcomes (AOs). In essence, AOPs describe how the interaction of a substance with a biological/ toxicological target, also termed Molecular Initiating Event (MIE), triggers a sequential cascade of linked events (Key Events-KEs), at molecular, cellular or tissue levels, ultimately resulting in an AO in the organism  (Bal-Price et al. 2017; Bal-Price and Meek 2017). Validated AOPs become valuable prediction tools to estimate a drug's potential to elicit an AO  (Pletz et al. 2018). This work comprises a comprehensive joint effort led by the NeuroDeRisk consortium, formed through the Innovative Medicines Initiative (IMI) to collect, organize, and expand existing knowledge on the biological pathway elements implicated in the onset of MCAEs, with a particular focus on the role of immune system modulation.
Here, we employed an original methodology with a unique workflow that represents, to the best of our knowledge, the first attempt to provide a new mechanistic interpretation of neurotoxic effects involved in mood and cognition. Contrasting with the more traditional approaches (e. g. systematic reviews, meta-analysis of clinical trials), we mined the selected PV database (FAERS) for pharmaceuticals that demonstrate a high occurrence of MCAEs based on disproportionality analysis. We then performed a functional enrichment and cross-talk analysis with the aim to elucidate the molecular mechanisms underlying these druginduced changes. Our integrated approach has particularly highlighted the key role played by immune function mediators in the mechanisms leading to MCAEs. At the same time, our parallel top-down and bottom-up analysis revealed distinct differences both in the sets of drugs and in the biological pathways associated with MCAEs.
Data sources FAERS is a publicly available database that contains Adverse Event reports that were submitted to FDA by healthcare professionals and patients (https ://www. fda. gov)  (FDA 2018). As of this writing, it contains over 9 million reports from 2004 to date. Individual reports in FAERS are patient-based. Reports document the patient-experienced AEs as a result of the administration of one or more drugs. Each report lists the drugs taken, with information regarding the putative role of the drug in the AEs of the report, i. e. whether a drug has been a suspect for an AE or is simply a concomitant drug. However, there is no direct association between each AE and respective drug(s) in a report. Other information contained in FAERS reports include the age of the patient and the indication for which the drug has been prescribed.
FAERS data (4th quarter of 2018) were obtained using the openFDA drug adverse event Application Programming Interface (API, https ://open. fda. gov/ apis/downl oads/). The drugs of interest were used as queries to collect data from DGIdb (Drug Gene Interaction Database)  (Cotto et al. 2018). Medical Dictionary for Regulatory Activities (MedDRA) classes corresponding to mood and cognitive AEs, Anatomical Therapeutic Chemical (ATC) codes mapping drugs to therapeutic classes and MeSH pharmacological actions, were taken from the NIH UMLS (Unified Medical Language System) version 2018AB database  (Bodenreider 2004). Bibliographic links to Drugs (e. g. from Genes, Pathways, Physiology terms, etc. ) were taken from Biovista's COSS (Clinical Outcome Search Space) database, specifically using the Vizit tool  (Andronis et al.
2012). Biovista Vizit is a visual bibliographic PubMed-based search tool that helps researchers and medical practitioners explore the existing knowledge of a biomedical domain, discover non-obvious links between entities (e. g. genes, pathways, drugs, etc. ), visualize biological interconnections and communicate findings with peers (https ://www. biovi sta. com/vizit /). Data on immune system genes were obtained from two publicly available databases: a) InnateDB  (Breuer et al. 2013)  developed by the Department of Molecular Biology and Biochemistry, Simon Fraser University, Canada, and b) The Immunology Database and Analysis Portal (ImmPort), developed by the National Institutes of Health (NIH), National Institute of Allergy and Infectious Diseases (NIAID), Division of Allergy, Immunology, and Transplantation (DAIT)  (Bhattacharya et al.
FAERS drug entries / Drug name unification A selection of fields encompassing Indications, AEs, Patient age, Date received and Adverse Drug Reaction outcome, were used to populate an internal database. AE names in FAERS are standardized on the MedDRA terminology. On the other hand, drug names entered in FAERS are not based on any standard terminology. Drug entries in FAERS may contain any of the following variations of terms: Drug names appended with the pharmaceutical salt, Active ingredients, Brand names, Drug combinations (usually, but not always, separated by ';'). Drug names also frequently contain typographic errors, making any aggregation attempt quite difficult. Finally, drugs appearing in FAERS also include Nutritional supplements used in alternative therapies and Investigational compounds.
Lack of data standardization in drug naming in FAERS is a known issue and efforts to tame this problem have been carried out in the past  (Banda et al. 2016; Maciejewski et al. 2017 ). An example drug presented with this issue is isotretinoin which exists in FAERS with at least 4 synonyms (number of rows in FAERS database in parenthesis): Accutane (10 038), isotretinoin (8 832), Roaccutane (894), Roacutan (240). Clearly, any effort to find a meaningful Drug-AEs combination would have to address drug name synonymization. The initial P/P subset of AEs contained 4,370,408 Drug-AE combinations,  1,356,883 reports, 38,424  Drug entries and 717 AEs. As a result of the synonymization process, drug names were normalized to 88. 4% of the reports in the P/P subset of FAERS based on their canonical form (usually the active ingredient of the drug) (Fig.
Selection of pharmaceuticals related to MCAEs Drug-MCAE combinations were generated from the FAERS database (FDA Adverse Event Reporting System, https ://www. fda. gov) (FDA 2018). A subset of this database was then filtered with AEs that were descendants of the MedDRA Psychiatric/Psychological (P/P) disorders class. Drug names in FAERS (as opposed to AEs) are not based on a controlled vocabulary. This requires significant consolidation and normalization before they can be used in any downstream analysis such as the one presented here. To address the problem of drug name incoherence in FAERS we employed Biovista's internal COSS database  (Deftereos et al. 2011 ) that contains synonyms for medicines based on a multitude of knowledge sources, including FDA approved drugs, MeSH Chemicals and Drugs, RxNorm and internal curation efforts.
Drug-MCAEs combinations in the P/P subset of FAERS were further filtered based on a number of criteria. First, Drug-AEs combinations where the Drug entry was not considered as "Suspect" in FAERS for a particular AE, were discarded. Drug-MCAEs combinations where the name of the AE was the same with the name of the indication were also discarded as they were considered as a data entry error  (Maciejewski et al. 2017 ). Other FAERS entries (i. e. Drug-AEs combinations) with errors due to data entry (e. g. abnormal patient ages, duplicated content, etc. ) were also discarded. Drug-MCAE combinations for patients over 90 years of age were not included for further analysis not only due to their very small contribution to the overall number of reports but also because they included invalid age entries (data not shown).
Disproportionality analysis and information component methods Disproportionality methods and Information Component (IC)  (Lindquist et al. 2000)  analyses are standard approaches of identifying statistical associations between Drugs and AEs reported in PV databases (such as FAERS)  (Duggirala et al. 2018). In this respect, we sought to calculate the Proportional Reporting Ratio (PRR)  (Bate and Evans 2009; Evans et al. 2001 ) and the IC in order to calculate the association between the Drugs in the P/P FAERS subset and Mood/Cognitive MCAEs. Two separate two-by-two contingency tables were constructed, one for Mood AEs and one for Cognitive AEs, using the MedDRA AEs from Fig. 1a  (Duggirala et al. 2018; Montastruc et al. 2011; Sakaeda et al. 2013). Two-by-two contingency table used for Disproportionality analysis for a Drug X against the combination of Mood or Cognitive AEs.