dataesr/scientific-paragraphs-categorization huggingface dataset:
g. , HL-60 leukocytes) are also included in the microfluidic system  [44, 45]. Future research should focus on the creation of more physiological in vitro systems with their own vascularity and immunological components to provide more appropriate structure and composition of the artificial tissue. The current investigations can be regarded as an early stage discovery research, which makes a relatively rapid screening of the test drugs for barrier penetration and tissue accumulation possible. As the main components of the skin barrier are localised in Stratum corneum and granular epidermis (tight junctions), HRE also includes the most important elements of this barrier. The basic questions of the current study were (1) does the 5 h diffusion cell study have any effect on the properties of the skins/skin equivalents? (2) Can the differences be observed between the composition of human skin, rat skin and HRE by Raman analysis? (3) Can the transdermal diffusion be measured in parallel with the skin accumulation of the test drugs (caffeine and quinidine)? (4) Can the human skins be replaced with rat skins or with HRE in skin-on-a-chip diffusion experiments? Based on the results, the following answers can be given to the above questions: (1) In preliminary experiments, the rat skins were tested in skin-on-a-chip device without topical treatment and with 5 h topical treatment with blank cream (data are not shown).
| PCR ribotyping Strains were characterized by capillary gel electrophoresis PCR-ribotyping as previously described by  Bidet et al. (1999). After DNA amplification, 1 μl of a 1/200 dilution of each PCR product was mixed with 10. 5 μl formamide and 0. 5 μl GeneScan LIZ600 (Applied Biosystems ® ) as an internal marker. After 30 s of denaturation at 90°C, capillary electrophoresis was performed on an 8-capillary 3500 Genetic Analyzer (Applied Biosystems ® ). The GeneMapper software (Thermo Fisher Scientific) was used to analyze the banding patterns. PCR-ribotypes (RTs) were identified using the WEBRIBO software (https://webri bo. ages. at/).
The animals were housed with ad libitum access to water and food. Five days before infection, the animals received a subcutaneous injection of clindamycin (30 mg/kg) (5). The infection was performed by oral gavage with approximately 1 × 10 4 spores of C. difficile (strain K027) in 300 µl of PBS. Mock-infected animals received the PBS only. Infected and mock-infected animals were housed in separated cages in a BSL-3 environment. Starting on Day 1 post-infection, the animals were treated by oral gavage with 100 mg/kg of the compound 20 diluted in PBS + 6. 7% DMSO in a final volume of 300 µl/animal. Treatment was administered once a day for 4 days. Control animals received the PBS + DMSO only. All hamsters were followed-up twice a day.
Clostridioides difficile species The compound inhibitory potential against C. difficile was tested in vitro against eleven strains of different PCRribotypes. Susceptibility of these strains to different antimicrobial drugs is reported in Table  2. Depending on the nature of the substituent in 4-position, the 21 compounds were divided in three families. The data corresponding to the inhibition diameter values (δ) induced by 50 μg of each compound are reported in the Tables  3 4 5. In order to compare and select inhibitors, the total of diameter values, Σδ, and the diameter cumulative increment, ΣΔδ, were also calculated. Absence of activity of all compounds against E. coli and Enterococcus faecalis, lacking a PRAC enzyme, was also confirmed with δ values = 12 mm (contact). As reported in Table  3 , non-aromatic compound NG-P27 (21) was completely inactive but introduction of an aromatic substituent in position 4 induced an antibacterial activity (compounds 1-3).
ACKNOWLEDGMENTS This work was supported by  University of Rennes 1, Pasteur Institute, AP-HP  and in part by grants from the  Agence Nationale pour la Recherche  ( ANR-14-CE16-0001-01 ). The authors are very grateful to the  Centre Régional de Mesures Physiques de l'Ouest, Rennes (CRMPO)  team for the physicochemical analyses.
Assuming that NPs diffuse freely in a Gaussian excitation volume, the correlation function, G(τ), calculated from the fluorescence fluctuations was fitted according to Thompson  26  : G τ ð Þ ¼ 1 N 1 þ τ τ d -1 1 þ 1 S 2 τ τ d -1 = 2 where τ d is the diffusion time, N is the mean number of fluorescent species within the two-photon excitation volume, and S is the ratio between the axial and lateral radii of the excitation volume. The excitation volume is about 0. 34 fL and S is about 3 to 4. The typical data recording time was 5 min. Measurements were done with respect to a reference 5(6)-carboxytetramethylrhodamine (TMR) (Sigma-Aldrich) in water.
Single-particle measurements on surfaces NPs were immobilized on glass surface of LabTek slides as previously described. 16 Single-particle measurements were performed in the epi-fluorescence mode using Nikon Ti-E inverted microscope with CFI Plan Apo 60× oil (NA = 1. 4) objective. The excitation was provided by light emitting diodes (SpectraX, Lumencor) at 550 nm at power density of 1. 3 W•cm -2. The fluorescence signal was recorded with a Hamamatsu Orca Flash 4 camera. Integration time for a single frame was 200 ms. The single-particle analysis was performed using Fiji software, similarly to the previously described protocol. 27 riefly, particle locations were detected through a FIJI routine applied to a projection (maximum intensity) of 10 frames using an appropriate threshold. The mean intensities of circular regions of interest with a diameter of 6 pixels around the found particle locations were then measured.
Imaging analysis The fluorescence analysis was performed using FIJI software. 28 The Z-stack was converted to maximum intensity projection and the channels were split. The integrated density value of the each timepoint was divided (normalized) on the integrated density value of the baseline. To prevent fluctuation of the signal between animals, the signal from NPs channel was also divided (normalized) on FITC channel. The two-way ANOVA statistical analysis with Tukey correction for multiple comparison was performed using Graph Pad Prism® 8. 0. 2 (GraphPad Software, La Jolla, California USA). The video data as well as 3D-reconstruction were prepared using Imaris® software (Zurich, Switzerland).
Imaging and analysis of fixed tissue Brains. The whole hemisphere images were acquired using a Zeiss confocal microscope (ZEISS LSM 900, Carl Zeiss Microscopy GmbH, Jena Germany) with 40× magnification (objective: EC Plan-Neofluar 40x/1. 30 Oil DIC M27) with an image matrix of 512 × 512 pixel, a pixel scaling of 0. 593 × 0. 593 μm and a depth of 8 bit. Confocal-images were collected in tile scan Z-stacks 10 slices with a slice-distance of 2 μm. Single particles images were acquired with 100× magnification (alpha Plan-Apochromat 100×/1. 46 Oil DIC M27 Elyra) with an image matrix 2048 × 2048, a pixel scaling of 0. 059 μm × 0. 059 μm and a depth of 8 bit. Morphological analysis was performed using FIJI software. The whole brain hemisphere was selected as a region of interest and the channels were split.
The unpaired t test was used for statistical analysis using Graph Pad Prism 8. 0. 2. The video data as well as 3D-reconstruction were prepared using Imaris® software. Internal organs. Imaging was performed using confocal microscopy (ZEISS LSM 900, Carl Zeiss Microscopy GmbH, Jena, Germany). For colocalization analysis, 40× magnification (objective: EC Plan-Neofluar 40×/1. 30 Oil DIC M27) was used with an image matrix of 512 × 512, a pixel scaling of 0. 415 × 0. 415 μm and a depth of 16 bit. Three to four ROIs per animal were collected in z-stacks as tile scans with a slice-distance of 1. 07 μm and a total range of 5. 35 μm. For the co-localization analysis, we exploited, ImageJ compatible, "Just Another Colocalization Plugin" (JACoP). 29 -stacks were imported into the software in split into individual channels.
Funding support: This project was funded by the  Alexander von Humboldt Foundation , the  European Union   Horizon 2020 research and innovation program  under the  Marie Skłodowska-Curie  grant agreement no. 794094 , the  European Research Council   ERC Consolidator grant  BrightSens  648528 , the  Agence National de Recherche JC/JC  grant " Supertrack "  ANR-16-CE09-0007 , the  DFG  under the  Munich Cluster of Systems Neurology (Synergy) , and  ERA-NET Neuron TRAINS. Conflict of interest: Authors declare no financial or commercial conflict of interest.
Introduction Adverse drug reaction reports are usually manually assessed by pharmacovigilance experts to detect safety signals associated with drugs. With the recent extension of reporting to patients and the emergence of mass media-related sanitary crises, adverse drug reaction reports currently frequently overwhelm pharmacovigilance networks. Artificial intelligence could help support the work of pharmacovigilance experts during such crises, by automatically coding reports, allowing them to prioritise or accelerate their manual assessment. After a previous study showing first results, we developed and compared state-of-the-art machine learning models using a larger nationwide dataset, aiming to automatically pre-code patients' adverse drug reaction reports. Objectives We aimed to determine the best artificial intelligence model identifying adverse drug reactions and assessing seriousness in patients reports from the French national pharmacovigilance web portal.
We used data from 26 Pharmacovigilance Centres for training and internal validation. External validation was performed using data from the remaining Pharmacovigilance Centres during the same period. Results Internal validation: for adverse drug reaction identification, Term Frequency-Inverse Document Frequency (TF-IDF) + Light Gradient Boosted Machine (LGBM) achieved an AUC of 0. 97 and an F-measure of 0. 80. The Cross-lingual Language Model (XLM) [transformer] obtained an AUC of 0. 97 and an F-measure of 0. 78. For seriousness assessment, FastText + LGBM achieved an AUC of 0. 85 and an F-measure of 0. 63. CamemBERT (transformer) + Light Gradient Boosted Machine obtained an AUC of 0. 84 and an F-measure of 0. 63. External validation for both adverse drug reaction identification and seriousness assessment tasks yielded consistent and robust results.
Other teams  [16, 17]  obtained better performance in more recent studies, but focused on published case report abstracts retrieved from PubMed, which do not share the same structure and style of patient-written reports, which are massively the cause of over-reporting. Because of this lack of evidence and tools dedicated to the automatic processing of patient ADR reports, the French National Agency for the Safety of Medicines and Health Products (ANSM) and its associated Regional Pharmacovigilance Centres (CRPVs) partnered in 2020 with Synapse Medicine to develop an AI tool, automating the evaluation of patient ADR reports, with the ambition to help PV experts cope with the upcoming COVID-19 vaccination campaign and future sanitary crises. Following a recently published first study showing the feasibility of such a system  [18] , this partnership was strengthened by allowing Synapse Medicine to use a larger nationwide dataset from the French national PV web portal.
Data Sources We retrospectively collected all available cases of ADR reports filled by patients through the national ADR reporting web portal between March 2017 and December 2020 and transmitted by ANSM to Synapse Medicine. Each report form contained structured and unstructured free-text information filled by patients, together with the annotation and coding realised by PV experts from CRPVs. Cases were considered unusable when linkage between reports and coding by experts was impossible.
Selection of Training, Internal Validation and External Validation Sets Datasets from 27 CRPVs (total of 31) were available. We used data from 26 of them for the development set (train and internal validation set), aiming to represent approximately 90% of the data to maximise our learning abilities. The development set included the cases used in our previous study  [18]. Data from the remaining CRPV were used as an external validation set to evaluate the robustness of our models on an unlearned sample and estimate the generalisability of their performance. We used a different CRPV for external validation to obtain a conservative estimate of our models' performance, as practices or types of reports might be heterogeneous across teams, even if the coding of ADRs and their seriousness is supposedly standardised.
Data Extraction and Pre-Processing Methods for data extraction and pre-processing were already described in our previous study  [18]. Briefly, the datasets were provided by ANSM, and composed of tables in Portable Document Format containing fields of interest. We extracted the text from these Portable Document Formats using the Python library Camelot  [22]. We performed basic text processing on the raw data (accents and punctuation removal, case lowering, stemming). We turned the following fields into structured features, to better take into account patient's specificities and treatments: age, body mass index as a summary feature of weight and size, sex, a one-hot representation of outcomes and a one-hot representation of drugs. The final feature vectors were built concatenating these structured features and the free-text patient ADR descriptions (vectorised using approaches described below).
It was selected as it was the best performing in our first study  [18] , which consisted of a similar study on a smaller dataset. The second approach, XLM, is a transformer model. Transformers, also called "BERT-like", are deep learning attention-based neural networks  [25] , which process text sequence inputs simultaneously by forming direct connections between individual elements through an attention mechanism, unlike traditional methods that process each sequence element in turn. Unlike LGBMs, transformer models are already pre-trained on plain text corpus, and allow for a contextual bidirectional representation of words in sentences. Multi-class text classification in transformerbased models is then achieved using a classification layer on top of the transformer model, using n output neurons corresponding to each class.
Transformers have recently been considered state of the art for NLP and subsequent classification tasks  [26] , but were not evaluated in our first study because the dataset was too small for deep learning approaches. Regarding data formatting, distinct procedures were applied for both approaches. For the LGBM approach, we vectorised text in numerical vectors, as gradient-boosting models need numerical features as inputs. More specifically, we vectorised unstructured text data in numerical vectors using the Term Frequency-Inverse Document Frequency (TF-IDF) method  [27]. We then used the TF-IDF vectors and the previously mentioned structured data (except the one-hot representation of outcomes) as model features. For the XLM approach, data formatting was not needed, as transformer models directly use unstructured text data as inputs.
Additional structured features were not included in this approach, as valuable information regarding symptoms was considered to be in free text, and XLM does not support structured data as inputs. All reports transmitted by the 26 CRPVs were included in the development set for both approaches. Likewise, the external validation set included all transmitted reports from the remaining CRPV. Models were trained and evaluated considering PTs with at least ten occurrences in the development set, owing to the size of data needed for this NLP task.
Seriousness Assessment Task For the assessment of ADRs seriousness, we decided to compare two LGBM models with different data formatting procedures. At first, similar to the ADR identification task, a full transformer approach was tested. It was deemed unsatisfactory though, considering that seriousness is a task that sensibly relies on the structured data available in reports (such as age, sex and outcomes) and that transformers cannot directly use structured data as inputs. Therefore, we compared two gradient boosting approaches: one with classical word embedding for data formatting, vs one with a transformer model dedicated to data formatting (both structuring unstructured text data into inputs usable by LGBM). For the first approach, we used the FastText library  [28]  and its extremeText extension, pre-trained on a dataset composed of text dealing with medical topics extracted from French reference sources.
Statistical Methods We evaluated the performance of all AI models using multiples metrics: receiver operating characteristics area under the curve (ROC AUC), positive predictive value 1 , sensitivity 2 (= true positive rate), specificity 3 (= true negative rate) and F-measure  4  (also called F 1 score, the harmonic mean of precision 1 and recall 2 ). We aimed to improve the result of our previous study  [18] : AUC 0. 93/F-Measure 0. 72 for ADR identification, and AUC 0. 75/F-Measure 0. 60 for seriousness assessment. For the initial prototyping of approaches, k-fold cross validation  [30]  was used for training and internal validation, using k = 10 for the identification of ADRs and k = 5 for the assessment of seriousness. Records in the development dataset were randomly divided into k parts, where k-1 parts were used for training and the k th one left out for testing.
A similar process was repeated k times across the dataset, using a decision threshold that maximised the F-measure, distinct for each task or approach. Metrics reported in this paper were estimated using a bootstrap procedure in order to obtain better average estimates and 95% confidence intervals (95% CIs). For training and internal validation, we used n = 100 samples, randomly split using a 90/10 ratio for ADR identification and an 80/20 ratio for seriousness assessment. Such ratios were chosen so that they could maximise learning abilities for both tasks, yet leave enough cases for a precise evaluation of models' performance through the estimation of confidence intervals. A narrower ratio was thus used for the seriousness assessment because of the lower prevalence of serious cases compared with PTs (multiple PTs can be coded for each report, while seriousness is assessed in a binary manner).
Models were therefore internally trained using random samples containing 90% or 80% of the data, according to the task, and evaluated on the remaining 10% or 20%. As for all metrics reported in this paper, we used the decision threshold that maximised the F-measure. For external validation, fully internally trained models were also evaluated using a bootstrap procedure on 100 random samples, representing 80% of the external validation dataset for both tasks. For each task and validation sets, medians and 95% CIs of all n bootstrap metrics were estimated. Confidence intervals were estimated with the percentile bootstrap interval, using the definition recommended by Hyndman and Fan for quantiles  [31] , considered unbiased regardless of data distribution. Hyperparameters were tuned using a classical grid search strategy on a dedicated train-test tuning split.
Overfitting was controlled by performing the internal validation of models using the previously described k-fold cross validation, bootstrapping, as well as externally validating the models on independent data (external validation set). Concerning model implementations, we used Python 3 (Python Software Foundation, Beaverton, OR, USA) and the following libraries: scikit-learn, lgbm, pytorch, xlm, transformers. Analysis also involved the use of R 4. 0. 2 (R Foundation for Statistical Computing, Vienna, Austria) for descriptive statistics, predictive metrics and plots (ROCR package).
Dataset Selection and General Characteristics Figure  2  shows the selection process for datasets used for both tasks and both validations. A total of 11,633 usable ADR report forms were transmitted by ANSM and CRPVs to Synapse Medicine. These represented roughly one quarter 1 Also called precision in machine learning literature. PPV = TP/ (TP+FP). If a prediction is made, it informs on its likelihood to be correct. In a multi-class problem, precision is the sum of true positives across all classes divided by the sum of true positives and false positives across all classes. 2 Also called recall in machine learning literature. Sensitivity = TP/ (TP+FN). It informs on the likelihood to correctly capture positively predicted entities. In a multi-class problem, recall is calculated as the sum of true positives across all classes divided by the sum of true positives and false negatives across all classes.
3 Specificity = TN/(TN+FP). It informs on the likelihood to correctly capture negatively predicted entities. In a multi-class problem, specificity is calculated as the sum of true negatives across all classes divided by the sum of true negatives and false positives across all classes. of all patient reports filled on the national web portal during the study period. Among those, 10,403 reports (89. 4% of the total), coded by 27 CRPVs, were included in the development dataset for ADR identification, and 4786 (41. 1% of the total) in the development dataset for seriousness prediction. The external validation set for ADR identification included 1230 reports (10. 6% of the total available), while the external validation set for seriousness assessment included 404 reports (3. 5% of the total available).
All reports in the external validation datasets came from Nantes's CRPV. Table  1  presents detailed characteristics of the included datasets. Regarding data used for ADR identification, in the development set, patients were mostly women (84. 5%), with a median age of 51 years (interquartile range [IQR] 37-62). Reports mentioned a median 1 drug per report (IQR 1-1), and 2. 6% mentioned a vaccine. Experts coded a median number of four distinct PTs per report (IQR 2-6). Overall, 50,546 PT terms were coded in this dataset. Among these, 1465 were distinct PTs, and 311 (21. 2%) were present in at least ten reports and thus learned by our ADR identification models. These 311 distinct PTs represented 94. 7% of all PTs coded in the dataset (47,845/50,546). In the external validation dataset, patients were also mostly women (85.
9%), with a median age of 51 years (IQR 39-63). Reports mentioned a median 1 drug per report (IQR 1-1), and 1. 1% mentioned a vaccine. Experts coded a median number of five distinct PTs per report (IQR 3-7). Overall, 6636 PTs were coded in this dataset, representing 506 distinct terms. The 311 PTs considered during training corresponded to 95. 0% (6302/6636) of all PTs coded in the external validation dataset. Regarding seriousness assessment, both development and external validation datasets were also very similar. Median age of patients was 41 years (30-58) and 39 years (31-56), respectively. Most patients were women in both datasets (78. 0% vs 77. 5%). In the development dataset, 23. 6% of reports were coded as serious, compared with 20. 8% in the external validation dataset. Figure  3A  shows the most frequently coded drugs (top 15) and Fig.
3B  the most frequently coded PTs (top 40) in both datasets used for ADR identification. Levothyroxine was by far the most frequently reported drug, accounting for around half of the reports in the development dataset External validation on fully internally trained models Fig. 2  Flowchart of the selection of datasets. ADR adverse drug reaction, ANSM French National Agency for the Safety of Medicines and Health Products, CRPV Regional Pharmacovigilance Centre and two thirds in the external validation dataset. Mirena ® followed, accounting for around 5% of reports. Overall, across all datasets, 2836 distinct drug names were coded by experts. Regarding ADRs, fatigue was the most frequently coded PT, among 1529 distinct PTs in all datasets.