dataesr/scientific-paragraphs-categorization huggingface dataset:
Mechanisms of action of levamisole and their potential synergy of action with 5-fluorouracil and cocaine If clinical trials on the use of levamisole in patients with COVID-19 give good results, its benefit-risk balance as an immunomodulator in this infection will have to be re-evaluated to enable its use in hospital or ambulatory settings. In June 2021, 4 clinical trials evaluating levamisole in the management of COVID-19 were reported in ClinicalTrials. gov, and the results of 1 of them have been published. The authors conclude that levamisole could potentially improve the cough and dyspnoea of patients with COVID-19 but no benefit could be demonstrated on mortality or aggravation of the disease. vasculitis, encephalopathies, neuropathies, agranulocytosis, retrieved: leucopenia, neutropenia, thrombocytopenia, rashes and hypothrombinaemia.
X-ray Diffraction (XRD) Pure drug powder and the prepared patches were assessed for crystallinity using X-ray diffraction. XRD measurements were performed in reflection mode (X'Pert MRD Pro, PANalytical, Almelo, Netherlands) with an X'Celerator detector and nickel filtered CuK α1 radiation (λ = 1. 54 Å) at 45 kV and 40 mA in the angular scan range from 4 • to 45 • 2θ at a scan speed of 0. 017 • /s. Patches were mounted on sample holders (15 mm) without the release liner, matrix-site facing upwards. Data evaluation was performed with X'Pert High Score version 2. 2.
Statistics All results were expressed as mean values ± SD. Statistical analysis were performed by GraphPad software (San Diego, CA, USA) using analysis of variance test (ANOVA), followed by significance analysis with Sidaks's multiple comparison test. Differences were considered significant at levels of * p < 0. 05, ** p < 0. 01 and *** p < 0. 001. Surface plots have been generated using OriginPro 8G (OriginLab Corporation, Northampton, MA, USA).
Storage Stability The absence of recrystallization after six months for all DMSO-based formulations regardless of the storage conditions was confirmed by XRPD (data not shown). In terms of DMSO loss, patches revealed to be stable during the first month under all test conditions as DMSO-content did not differ significantly (Figure  7 ). The benefit of low temperatures was minor as during longer periods, significant DMSO loss occurred of all formulations when stored at 4 °C (sealed, Figure  7A ). The slightest change in DMSO content was found when patches were stored sealed at 25 °C/60% RH (Figure  7B ). Generally, a more distinct reduction of DMSO content in patches was found for those with the initially highest amount (35 °C, 3 h), the lowest loss for those with the lower initial DMSO content.
Storage Stability The absence of recrystallization after six months for all DMSO-based formulations regardless of the storage conditions was confirmed by XRPD (data not shown). In terms of DMSO loss, patches revealed to be stable during the first month under all test conditions as DMSO-content did not differ significantly (Figure  7 ). The benefit of low temperatures was minor as during longer periods, significant DMSO loss occurred of all formulations when stored at 4 • C (sealed, Figure  7A ). The slightest change in DMSO content was found when patches were stored sealed at 25 • C/60% RH (Figure  7B ). Generally, a more distinct reduction of DMSO content in patches was found for those with the initially highest amount (35 • C, 3 h), the lowest loss for those with the lower initial DMSO content.
This is the French pharmacovigilance task-force. This organization allows an analysis and an evaluation in real-time. Beyond real-time ADRs assessments, one pair of RPVCs per vaccine marketed in France everyday colligate and deeply analyze all ADRs data from the vaccine they are in charge of. To complete accurately the assessment of ADRs, several RPVCs also provide a specific organ expertise (like in neurology, cardiology, allergy, dermatology). Every Tuesday, a report of expertise achieved by each of the pairs of RPVCs for each vaccine is transmitted to ANSM. These reports of expertise gather data concerning exposed population, and quantitative and qualitative analysis of ADRs, characterized by age, severity and type of ADRs. Qualitative, quantitative and medical analysis are performed for adverse events of interest (anaphylaxis, zona, Bell's palsy.
During the pharmacovigilance assessment on April 8, 9 new cases were analyzed, concerning people with a different profile than in the previous periods. They are 4 women and 5 men, older (average age 62 years) who presented more digestive thrombosis. Thromboembolic events are being reviewed by EMA to better understand the mechanism of action, the possible underlying risk factors and any other data needed to understand the phenomenon and guarantee the security of use.
Acknowledgements We would like to warmly thank  Gabrielle Barriera  ( Conservatoire et Jardin Botaniques de la Ville de Genève, Switzerland ) for her invaluable help in disentangling many conflicting nomenclatural points. We also thank the curators of  ATHU, B, BASBG, BC, BEI, BH, BM, BP, BR, C, E, FI, FR, G, GB, GH, GOET, GZU, H, HAL, HBG, HUJ, ISL, JE, K, L, LD, LE, LI, LY, MJSD, MA, MEL, MO, NY, O, OXF, PAD, PH, S, TARI, UC, UPS, US, W, WAG, WRSL, and WU  for their kind assistance, and  Dieter Reich  and  Christian Braüchler  for their valuable suggestions. We thank three reviewers ( Andrey Erst ,  Andriy Novikov ,  Martyn Rix ) and the Editor ( Marco Pellegrini ) for their helpful comments on the manuscript. The MNHN gives access to the collections in the framework of the RECOLNAT national research infrastructure.
Acknowledgements We acknowledge funding from the  ARD2020 Biopharmaceutical program  of the  R egion Centre Val de Loire (Bio-PROPHARM, CatharSIS and ETOPOCentre projects), Le Studium Institute  for a  Research Consortium Grant  and the  EU   Horizon 2020 research and innovation program  ( MIAMi  project-grantagreement N•814645).
"  5  Contextualizing AI to healthcare unstructured data, it could be conceived as the combination of semantic information (SI) and machine learning (ML) to perform data analysis. SI allows users to add meaning to data through ontologies and knowledge databases,  2  therefore mimicking the theoretical learning patterns of a human being, whereas ML provides potentiality for new assumptions by aggregating information compounded from existing knowledge. 6 Taken together, SI and ML bring machines one step closer to the way human beings manage and learn from information. The combination of SI and ML has already shown interesting results in the field of drug identification. 7 eyond medical imaging, all medical specialties could benefit from these technologies. This is especially true for pharmacovigilance, which could evolve considerably in the coming years.
The current context of pharmacovigilance because patients can report ADRs themselves is detailed in Supplemental Information 1 Text file S1. The development of tools allowing to automatically process ADRs is currently the object of intensive research and efforts, both from public and private entities. 14 When originated from industries, these developments appear to be mostly restricted to their sole products. 15 he underlying assumption of the present work is that a global knowledge database on drugs, enriched by supervised ML models trained on reporting data, could allow setting up a universal tool for preprocessing free text reported ADRs. This tool would increase efficiency in dealing with such information and improve capacities in drug surveillance.
Data collection We selected all cases of ADRs reported by patients to Bordeaux Pharmacovigilance Centre through the French national web-portal between March 1, 2017, and February 28, 2019, for a total of 2,058 report forms. For each of these, the report form containing the free text filled by the patient was obtained together with the expert annotation and coding realized within the pharmacovigilance center. The coding of ADR was realized using the MedDRA standardized medical terminology developed by the International Council for Harmonization of Technical Requirements for Pharmaceuticals for Human Use. 16 MedDRA has been translated and is maintained in several different languages that allow interoperability between data from different countries. There are five levels to the MedDRA hierarchy, arranged from the very general to the very specific.
Training and validation set These coded ADRs were used to train and validate ML algorithms for the identification of ADRs and ADR seriousness from the patient reportforms: 90% of cases (n = 1,852) were used for the train set and 10% (n = 206) for the internal validation set (test set). We have kept 90% of the data for the train set to maximize our learning abilities. We tested the robustness of the best performing model on this internal dataset through an external validation on a 10% sample of ADRs reported by patients to the Marseille Pharmacovigilance Centre over the same period (n = 187). The used dataset included in total 10,675 reported potential ADRs for the learning set (train set + test set) and 407 for the external validation set.
Data extraction and preprocessing The dataset we were provided with for this study is composed of tables in PDF forms containing several fields of interest. The very first step was to extract the text from these PDF files preserving their structure. To do so we used the Python library Camelot. 17 The most informative section of the report forms was the description of the adverse events written by the patients. We performed basic text processing on the raw data, such as accents and punctuation removal, case lowering, and stemming. These transformations are useful to reduce noise and to limit the size of the vocabulary, specifically when we deal with small datasets. Some other fields can be turned into structured features represented as: • Integers: age, body mass index as a summary feature of weight and size • Booleans: sex and one-hot representation of drugs These additional features make it possible to take into account the patient's specificities and treatments.
Recent gradient-boosted trees methods, such as XGBOOST  28  or LGBM,  29  provide generally state-of-the-art performance on various classification and regression ML problems. We chose the widely used python lightGBM  29  library for its remarkable performance in terms of accuracy as well as computing speed. The main difference between lightGBM and the other tree-based gradient boosting methods consists in the fact that the binary decision trees grow at the leaf level instead, whereas in algorithms, such as XGBOOST, they grow at the depth level (each leaf of the same depth level splits at the same time). This leaf level split provides a better adaptability and thus a better fit to the dataset. FastText. FastText is an open-source method and python library for words representation and text classification.
30 The classification algorithm built on top of this representation method is a multinomial logistic regression. 31 We trained the word embedding on the Synapse dataset composed of 260 MB of text dealing with medical topics extracted from French reference sources. This word embedding was used by the FastText classification model as well as the deep learning models as embedding layers. In this study, we used extremeText, which is an extension of FastText. It implements loss functions-like probabilistic label treeswell adapted for extreme classification tasks (i. e. , classification tasks with a large amount of classes). Deep learning models. Deep learning is a subset of ML methods gathering multilayer neural networks. 32 Deep learning methods are generally better than conventional methods to process complex data and large datasets.
This processing method was used on both the MedDRA labels and the narrative itself. On the one hand, we used this RegEx engine as a baseline for our ML models' benchmark; on the other hand, we built hybrid methods combining ML models and RegEx. We do not expect patients to use terms that could exactly fit with a technical terminology, such as MedDRA. However, we can suppose that the RegEx approach allows the system to detect the rarer ADRs that are not present in the training dataset and for which ML techniques would not be efficient at all. Hybrid models combine the RegEx approach and the ML models described above. There are several ways to aggregate predictions from several models. Here, we chose to average both vectors to build the final predictions, which is equivalent to methods used in bagging (for instance, random forests).
Overfitting and validation Overfitting is more likely with nonparametric and nonlinear models that have more flexibility when learning a target function. The best solution to control the overfitting of an ML model is to perform an external validation on independent data (external validation). In general, but even more for relatively small datasets, internal validation of prediction models by bootstrap techniques may not be sufficient and indicative for the model's performance in future data. Our internal validation was carried out on 10% of Bordeaux dataset: 206 cases. Our external validation was carried out on 10% of cases declared to the CRPV of Marseille during the same period: 187 cases. Moreover, the drug distribution of these 187 cases differed from the Bordeaux dataset and these cases were analyzed by different experts, which allows us to have a better estimate of the transposability of our system.
Programming Concerning the model implementations, we used the python libraries scikit-learn and lgbm (gradient boosting) for the conventional ML models, keras (with Tensorflow backend) for the LSTM and CNN models and FastText + ExtremeText for the FastText model. The models were trained on an Intel Core i7 processor with 16 GB RAM. Example code for LGBM (ML model with best performance) and for LGBM + regex are provided on the following git repository: https:// github. com/louis letin ier/MAITAI. git. For more information about data management or our algorithms, you can contact the corresponding author at the email address indicated on the first page.
Dataset characteristics and representativeness Patients who reported ADRs to the Bordeaux Centre were mostly women (88%) of median age 52 years (interquartile range (IQR): 40-62); 28% of reports mentioned serious ADRs. Altogether, 11,591 potential ADRs were reported in the 2,058 forms (median: 5 ADRs per report; IQR: 3-7). After coding by experts, they were found to correspond to 593 distinct ADR terms (MedDRA PTs). Overall, 2,396 drugs were mentioned; Levothyrox (levothyroxin) was the main represented (n = 1,623, 68%). We stratified the 10% random sampling of the Marseille reports for the representation of levothyroxine (10%) to ensure a wider variety of cases would be considered for external validation. Without stratification, the Marseille reports included a proportion of levothyroxine cases comparable to Bordeaux due to the "Levothyrox crisis" detailed in Supplemental Information S1.
After this, 187 reports from Marseille were selected, 44% of which were serious. They concerned mostly women (74%); median age was of 44 years (IQR: 28-60). The reports mentioned 549 potential ADRs (median: 2 ADRs per report, IQR: 1-4) corresponding to 199 distinct PTs, and 201 drugs. The most frequent PTs for each center are listed in Table  S1. Owing to the size of the dataset, the training and the internal validation were performed considering PTs with at least 10 occurrences in the Bordeaux dataset, which led to consider 10,675 potential ADRs, and 125 distinct PTs in this dataset. The external validation focused on PTs present in the learning set, which led to consider 407 potential ADRs and 85 distinct PTs from the Marseille Centre reports in the validation set. Practically, the 125 PTs included in our dataset corresponded to 64.
ML models performance for ADR seriousness determination Regarding AUC and F-measure, the 4 conventional ML models tested showed similar performance with AUC close to 0. 75 (Figure  3a ) and F-measure close to 0. 60 (Figure  3b ). Neural network models were unable to fit in the dataset effectively. As ADR seriousness determination is more complex than ADR identification, it is possible that this relates to the limited size of our learning set. Overall performance of conventional ML models to determine ADR seriousness are presented in Table  S2.
DISCUSSION Our study showed that an AI pipeline using a knowledge database to structure free text data and a ML model to learn ADR coding from human expertise could allow for automatic identification of a large proportion of the ADRs described in patients' report forms unstructured data. The best performing AI pipeline using LGBM (gradient boosting) showed interesting results with an AUC of 0. 93 and an F-measure of 0. 72. More concretely, from the used test set comprising 1,061 ADRs, our system succeeded in correctly identifying and coding 703 ADRs, and incorrectly spotted or coded 190 others. The performances obtained for the automated determination of the ADR seriousness task were lower; enlarging the experiment to a larger set could allow improving these. Other teams, academic or industrial, are currently also working on this latter aspect using ML models disregarding the aspect of ADR identification from free text.
36, 37 or ADR identification, we found a relative discrepancy between AUC and F-measure, highlighting how these metrics differ in considering a classifier's performance. In the commonly used ROC curve/AUC, TP and the FP rates are considered of similar importance, which might present limitations for the evaluation of a classifier especially in the case of unbalanced data. 38 AUC can indeed present with very high overall values if the modality "absence of ADR" is dominant in the dataset and is related to a very high level of specificity, even the sensitivity of the model is low. The F-measure conversely provides a single score that balances both the aspects of positive predictive value (PPV) and sensitivity in just one score and is thus not affected by the predominance of a modality in the dataset.
ARTICLE The external validation strengthened the validity of our results, allowing us to determine how the model performances would transfer to new users and new patients. 40 In our results, the performances obtained were lowered during the external validation. Because the model was trained on data from a single center, this was, however, expected and should be improved by enlarging the learning to other centers. Similarly, the lower performances of the models in determining ADR seriousness, a more complex task involving numerous parameters, could be improved if training was performed on a larger dataset. The use of real-life, expert-annotated data is the most important strength of our study. In contrast to simulation studies, it demonstrates the concrete capacities of the AI pipeline to classify ADRs from unstructured textual data.
Its performance could, however, consequently depend on peculiarities in the data and the transferability of the system to other settings (especially other languages) should be studied. As the overall pipeline we propose is entirely transposable to English and other languages with comparable structures, this should theoretically not constitute an issue. Medical concepts, drug names, and the MedDRA classification of ADRs indeed benefit from multilingual reference systems, and the semantic approach based on a knowledge base proposed here makes it possible to link identical concepts expressed in different languages. 41 he main limitation of our study is the limited size of the datasets used. ML methods, especially deep learning ones, require a large amount of data. 42 One of the difficulties when dealing with textual data is the ability to differentiate between sentences almost similar in form but very different in meaning, for instance, because of a negation.
Deep learning is theoretically the most adequate method to overcome this  32, 43  but it requires having learned enough about these complex situations to identify them and determine new ones afterward. Attention-based transformer architectures, such as BERT and its numerous variants, are showing very promising results and seem to be the best performing methods for this kind of task. 44 However, we did not find models pretrained on French convenient biomedical datasets and our dataset was too small to consider fine-tuning a BERT model so we did not include these models into the study. With more data available, it will be a good perspective to improve results obtained in the current work. In larger studies, the model ranking regarding learning capacities could thus change. 45 However, even using a limited dataset, the performance obtained was already valuable.
This could be due both to the quality of the expert-annotated data and to our semantic approach, which optimizes their understanding. However, the annotations we used to train the models and evaluate their performances were made manually. Moreover, due to the high complexity of the MedDRA terminology there is a high variability in the human ADR annotation process. This leads to a not insignificant noise in the dataset and hard to assess model errors. We did not perform any measurements of this inter-annotator variability in this study as the necessary qualified human resources were not available. However, we consider doing it as future work. The results of this study are encouraging. On their basis, we will extend this learning to all pharmacovigilance French centers, which should enable to train our models on ~ 20,000 expert-annotated patient reports.
2 database. 3. 4. Analytical procedure 10 mg of sample were extracted with 1 mL of dichloromethane (DCM) (Merck) under ultrasound for 10 min and then centrifuged at 6000 rpm for 10 min. The supernatant was set aside to extract the solid pellet again. This step was performed 2 more times. The three fractions thus collected were then combined and evaporated to dryness under a stream of nitrogen. After having undergone a trimethylsilylation reaction with 200 μL of BSTFA/TMCS (99/1, v/v) (Sigma Aldrich) and heated at 70°C for 30 min, the mixture was again evaporated to dryness and then dissolved in 0. 2 to 1 mL of a hexane/DCM mixture (1/1, v/v) and filtered on a polytetrafluoroethylene cartridge (0. 45 μm, VWR). 1 μL of the solution was injected into GC-MS. The oven temperature program started at an initial temperature of 50°C, constant for 2 min, then increased from 10°C/min to 200°C, followed by a second increase of 2.
Acknowledgements The authors want to thank  Mrs Margaret Méjean , the  Museum of Confluences in Lyon , the  Agence Nationale de la Recherche (ANR) , the  LabEx ARCHIMEDE and Human Egyptian LYon COnfluences Mummies (HELYCOM)  -Mourir pour renaître project for the partial funding of this project. The research has been funded by a PhD grant given by The  French Ministry of Higher Education and Research.
, 2018; Stringlis et al. , 2019). To identify metabolites of Fra, Qui, Emo, Mad and VH characterized by the fluorescence spectra detected by SImaging, non-targeted metabolomics of methanolic extracts from treated and non-treated BY-2 cells was carried out using UPLC-HRMS/MS. The results were compared to a database including the reference compounds and related metabolites deduced from in silico biotransformation. In this regard, 54 metabolites absent in the control extracts were annotated according to m/z of the parent ion and isotopic profile as compared to references or putative catabolites. In addition, hypothetical isomers and/or conjugates were annotated according to MS/MS fragments. As it cannot be excluded that after separation a loss of conjugates occurred in the MS source, the metabolites with different retention times (R t ) but similar m/z and MS/MS fragments were annotated as derivatives represented by putative isomers and/or conjugates.