dataesr/scientific-paragraphs-categorization huggingface dataset:
The proportion of reported cases per 100 000 drug users was highly variable according to drug classes, thus correlated to the safety profile of these drugs and the characteristics of the population exposed but not necessarily to the novelty of the drugs. Indeed, in our study, the date of labeling was not significantly associated with the adverse event reporting rate in pharmacovigilance databases. This finding is consistent with previous studies exploring Weber's effects, which have found no evidence of this phenomenon in recent years  [3]. Surprisingly, the reporting rate of certain drugs, such as desloratadine, was particularly low. We can hypothesize that other contextual factors such as drug indications, patients' comorbidities, and perception of the drug can also influence this reporting, which needs to be further studied.
Identifying the factors associated with the low rate of reporting of some adverse events could indeed be used to design specific actions to address this gap. In our analysis, consistently with other studies, the rate of adverse events reported by women was higher than that of men, while non-significantly. Indeed, gender differences in adverse event reporting in pharmacovigilance databases in a well-known described factor due to a probable higher incidence of adverse drug reactions in women and/or a higher trend to report an adverse event  [7] [8] [9] [10]. This study has some limitations. Firstly, the extracted data were limited to a single country, France. Indeed, the rate of reporting adverse reactions varies greatly from one country to another depending on the national pharmacovigilance systems and the national scientific, demographic and media issues related to medicines.
We can hypothesize that comparing drug exposure using adverse events reporting between countries using different pharmacovigilance systems will be unreliable. Secondly, the data extracted from the Open Medic database refers only to box fillings at city pharmacies, excluding any data on hospital fillings. The proportion of dispensed drugs in-hospital may therefore vary on a drug-by-drug basis and impact our results. However, linear regressions within each drug class were consistent despite this potential bias. Finally, we worked with a small number of drug classes; extrapolation of other drug classes is therefore putative. In conclusion, our study suggests that within the same drug class, a comparison of the total number of cases reported in a pharmacovigilance database can be used as a proxy for a comparison of drug exposure.
Number of patients exposed and cases reported in the French pharmacovigilance database by drug class. Number of Total number of drug Number of cases Number of cases Drug class drugs users reported reported per 100 000 Antihistamines for systemic use 15 14 884 791 731 4. 9 BCR-ABL tyrosine kinase 5 16 749 459 2740. 5 inhibitors Benzodiazepine derivatives 11 8 607 501 2175 25. 3 Bisphosphonates 4 319 440 281 87. 9 Coxibs 2 500 268 49 9. 8 Dihydropyridine derivatives 9 3 609 581 605 16. 8 Selective serotonin reuptake 6 3 544 278 1197 33. 8 inhibitors Tetracyclines 3 1 188 654 211 17. 8 Tumor necrosis factor alpha 4 137 855 2306 1672. 8 inhibitors Vitamin K antagonists 3 644 132 877 427. 0 T A B L E 1.
Results of the mixed effect models. Correlation between the number of reported cases in the French pharmacovigilance database and drug exposure for each drug class at the ATC 4th level. Each individual dot corresponds to one single drug (ATC5) in each drug class. Variable Slope (SE) P value Number of drug users (dependent variable) Number of cases 0. 88 (0. 11) <0. 01 Proportion of female cases 0. 03 (0. 01) 0. 03 Reporting rate (dependent variable) Date of first case 35. 68 (21. 38) 0. 10 Proportion of female exposed À33. 85 (29. 04) 0. 25 403 Number of cases reported 55 7 1 403 2 981 22 026 162 755 1 202 604 8 886 111 Number of drug users Antihistamines for systemic use Benzodiazepine derivatives Coxibs Selective serotonin reuptake inhibitors Tumor necrosis factor alpha inhibitors BCR-ABL tyrosine kinase inhibitors Bisphosphonates Dihydropyridine derivatives Tetracyclines Vitamin K antagonists F I G U R E 1 T A B L E 2.
Purpose: Recommendations to improve therapeutics (Recos) are proposals made by pharmacists during the prescription review process to address sub-optimal use of medicines. In hospitals, Recos are generated daily as text documents that are sent to prescribers. If collected Recos data were easier and less time-consuming to summarize, they could be used retrospectively to improve safeguards for better prescribing. The objective of this work was to train a deep learning algorithm for automatic Recos classi cation in order to value the large amount of Recos data. Methods: The study was conducted at the University Hospital of Strasbourg. Recos data were collected throughout 2017. Data from the rst six months of 2017 were labeled by two pharmacists who assigned to each of the Recos one of the 29 possible classes of the French Society of Clinical Pharmacy classi cation.
This is currently the method of choice for recording Recos in France. It is used both in daily pharmaceutical care practice and for research. The implementation of prescription assistance software in hospitals has facilitated the deployment of MedRev, as prescriptions have been integrated into electronic medical record data. Several recent papers have used machine learning algorithms to facilitate the MedRev process  [22] [23] [24] [25] [26]  or to analyze this data ow  [27, 28]. However, to the best of our knowledge, no previous work has reported on the use of Recos data. Because these data are captured, logged and archived as part of the patient's medical record, they represent an information-rich data-mart that could provide a unique opportunity to develop expert arti cial intelligence-based tools to help pharmacists retrospectively to classify Recos, analyze the classi cation patterns and ultimately alert them to certain risky prescribing and administration behaviors.
Dataset The study was conducted in a public University Hospital (over 2,400 beds) in Strasbourg, France, that provides surgical, medical, research and teaching activities. Data were collected over a full calendar year period (January through December 2017) directly from the hospital information system. Data corresponded to comments generated by pharmacists for all prescriptions for adult inpatients, excluding those admitted to intensive care, obstetrics and gynaecology, or psychiatry units. Comments described (as technical notes in French) the reason for the pharmaceutical interventions together with the messages sent to prescribers.
Text processing Recos documents were written in French plain text -often in the form of technical notes for prescribersand had to be processed. Text processing was performed using R (version 4. 1. 0)  [29]. In short, abbreviations and keywords were replaced with their full equivalents. Accents, punctuation, numbers and duplicate documents were removed. Porter's stemming algorithm (package tm) was used to stem French words in the corpus of texts. Terms were tokenized as n-grams (n from 1 to 3) and a document term matrix was constructed using term frequency weighting. Data from the validation dataset were processed using the same pipeline, except for the very last step, where only terms initially present in the training document term matrix were retained to build the validation document term matrix.
Results Manual labelling of recommendations 27,699 unique text documents of Recos were extracted for the rst six-months of 2017. Each document was independently assigned a speci c label by the two pharmacists. The amount of divergent coding between the two raters was n = 1,006 discrepancies (3. 6%) for which a consensus label was determined. Importantly, a single label was assigned to each document. Three subclasses (codes 2. 3, 6. 5, 9. 2) were not observed in the dataset. To enable the algorithm to learn these classes, we manually added credible synthetic comments suggested by clinical pharmacists, resulting in 27,721 unique documents (Table  1 ).
Pharmacist interventions processing The   S3 ). As a further demonstration of the classi er's classi cation ability, we attempted to predict classes and subclasses for all the Recos collected between July and December 2017. A term-document matrix generated by the same preprocessing pipeline was fed into the trained neural network to obtain class/subclass predictions. All predictions with a prediction probability greater than or equal to 0. 95 and a code different from the subclass "Non-conformity of the drug choice compared to guidelines -code 1. 1" (this subclass can be easily checked automatically using dictionary of available drugs ) were selected and manually checked by a pharmacist. Of the 4,460 predictions checked, 67 had to be corrected, for an error rate of about 1. 5%. Finally, this dataset was concatenated with the original dataset to create an extended dataset (15% larger than the original), which we split into a training and a validation dataset as before.
The classi cation relies on the external validated SFPC classi cation -currently the most widely used classi cation method made by pharmacists in France  [20]. The SFPC classi cation seems well adapted to describe pharmacist interventions in our hospital. It contains explicit de nitions for each Recos category to reduce ambiguity in coding a problem. In fact, the class named "other" represented only 7. 1% of all Recos in the original dataset. Indirectly, it shows that Recos are not only issued to deal with DRP but also integrate additional sub-optimal drug prescriptions issues intercepted during the MedRev process. This has to be compared with some studies in which the "other" class represented up to 39% of all DRP  [15]. The 29 classes and subclasses in the original data set were unbalanced, with an overrepresentation of situations in which the choice of drug did not match drug availability or guidelines.
On the opposite, there was an under-representation of subclasses such as "adverse drug reaction" (~ 4/10,000) or "failure to receive drug" (~ 1/1,000). This is in full line with the fact that these events are rare as already described by Bedouch et al  [32]. In their study focusing only on DRP, "adverse drug reaction" or "failure to receive a drug" accounted for 4. 3% and 0. 8% of their 34,522 interventions, respectively. Regarding performance, a global accuracy of 78% was achieved on the validation dataset. The model performed worst for subclasses such as, "physio-pathologic contra-indication", "valid indication without drug", "dose too low for this speci c patient". This may be due to an insu cient number of observations in the initial dataset or due to more linguistically complex documents that require some inference to understand which class/subclass the entities involved belong to.
Algorithmic prediction errors in the data of the second half of 2017 were low with only 1. 5% of the predictions. It shows that classes/subclasses predicted with a probability greater than or equal to 0. 95 are largely reliable. A closer look at these errors revealed that they corresponded to complex clinical situations where multiple classes/subclasses could be encoded. Incorporating the additional validated data from the second half of 2017 into an expanded dataset increases the performance of the classi er, con rming that the classi er has the potential to improve as the amount of data increases and strengthens the validity of the results. This study has several limitations. Because the study was conducted in a single French hospital, our ndings may be limited in scope. Its generalizability to other institutions has not been investigated.
27, 721  Recos documents were split (strati ed by classes and subclasses) to form a training dataset of 23,576 (85%) and a validation dataset of 4,145 (15%). In the training set, processed comments (stemmed and cleaned) were typically short with a median number of 82 characters (IQR = [59-115], min = 3, max = 1,153), corresponding to a median number of 13 words or expressions. Comments were tokenized using n-grams (n from 1 to 3). We used the full set of documents in the training set to generate a (178,680 x 23,576) term-document matrix based on term frequency weighting. The size of this initial The loss function was weighted by the inverse of the class frequencies of the training labels to account for unbalanced class representations in the training dataset. The class prediction accuracy calculated on the unseen validation dataset was 78.
This (6,356 x 23,576) term- document matrix was transposed into a (23,576 x 6,356) training dataset. Using a similar preprocessing, we obtained a (4,145 x 6,356) validation dataset. Deep neural network classi er The training dataset and its corresponding 23,576 ground truth Recos classes and subclasses were fed into a deep neural network consisting of three dense layers and a 29-class output layer activated by a softmax function.
3 ± 0. 1 µM and 31. 4 ± 4. 9 µM. Data are represented as mean of percentage of LPS ± SEM of four experiments performed in triplicates; * p < 0. 05 versus LPS (one-way ANOVA followed by Dunnett's test). In order to determine whether these compounds have a direct effect on PGE 2 formation, the COX activity and COX-2 expression were assessed. COX-1 activity was determined as the level of PGE 2 synthesized from exogenous added arachidonic acid in HEK-293 cells overexpressing COX-1 (Figure  3A ). In a similar setting, we previously showed that ibuprofen, a non-selective COX-1/COX-2 inhibitor, blocked PGE 2 synthesis  [18]. COX-2 activity was determined as the concentration of PGE 2 synthesized from exogenous arachidonic acid added on 24 h LPS-treated macrophages, where previous treatment with aspirin was applied to block basal COX (Figure  3C ).
01 ng/mL) and 71. 7 ± 5. 7 ng/mL for 10 µM AA. 3 were the only compounds that significantly decreased the NO secretion. Because NO is the breakdown product of NOS2 enzymes, we assessed their effect on the enzyme expression. Figure  4  shows the inhibition of LPS-dependent NOS2 protein expression by compounds 2 and 3. Compounds 1, 2, 3, and 8 did not modify the NF-κB or Iκ-Bα phosphorylation induced by LPS, excluding any effects of the compounds on the NF-κB (Figure  S4 ). and 9 , and 50 µM for the other compounds, for 30 min prior to the addition of 10 ng/mL LPS for 24 h. β-actin was used as a loading control, and densitometric analysis was performed using ImageJ software (NIH, MA). Data are expressed as fold of LPS-treated macrophages and represented as mean ± SEM of 4 different experiments; * p < 0.
Effects of Compounds 2, 3, 5, and 6 on Inflammation in Subcutaneous Zymosan-Induced Air Pouch in Mice The zymosan-injected air pouch model of inflammation has been shown to mimic the synovium cavity and is largely used to assess inhibitors of inflammation  [19] [20] [21] [22]. To test the anti-inflammatory effects of the synthesized compounds in vivo, compounds 2, 3, 5, and 6 were co-administered in the sterile air pouch with zymosan (Figure  5A ). At 24 h post-treatment, the number of cells of the air pouch exudates was significantly reduced for compounds 2, 3, 5, and 6, whereas the PGE2 levels were inhibited by compounds 2, 3, and 6 (Figure  5B ). and 9 , and 50 µM for the other compounds, for 30 min prior to the addition of 10 ng/mL LPS for 24 h. β-actin was used as a loading control, and densitometric analysis was performed using ImageJ software (NIH, MA).
Data Analysis IL-6, CCL2, nitrite (for NO), and PGE 2 concentrations from 3-5 independent experiments were expressed as percentage of LPS alone and as mean ± SEM. COX activity was determined as percentage of PGE 2 produced from exogenous arachidonic acid. Curve fitting and calculation of IC 50 values were performed using GraphPad Prism (version 9. 5. 1 Software, La Jolla, CA, USA). Densitometric analyses were performed using ImageJ software (NIH, MA, USA). The ratio of the different analyzed bands to β-actin was determined and data are represented as fold change compared to the LPS-treated cells. Percentage of viable cells for proliferation assay was determined as percentage to control (non-treated cells). For TUNEL assay, apoptotic cells were quantified, and results were expressed as percentage to positive cells (cleaved DNA) compared to control (non-treated cells).
Statistical Analysis All data were analyzed using GraphPad Prism (version 6; GraphPad Software, San Diego, CA, USA) and are herein presented as means ± SD. A comparison of the results of the swelling behavior, in vitro dissolution test, enzymatic stability assessment, permeability test, TEER measurement, DPPH anti-oxidant test, in vitro anti-inflammatory effect test and MTT cell viability assay was performed with a one-way ANOVA and repeated measures ANOVA followed by Tukey or Dunnett post-testing. The difference in means was considered significant in the case of p < 0. 05. All experiments were carried out in quintuplicates and repeated at least five times.
Acknowledgements This work was supported by a grant from the  Agence Nationale de la Recherche  with  JCJC  funding ( ANR-20-CE07-0008-01 ). M. B. thanks  UVSQ  and the  French ministry of research  for a PhD scholarship. Dr. Karen Wright  is warmly acknowledged for improvement of the text as well as the  GIS Fluor  is also acknowledged.
Study population All patients with RSTI of acetaminophen (n = 4,522) were included between January 2012 and December 2017. In addition, 4,522 randomly selected acute acetaminophen ingestion cases were included. Criteria for exclusion were missing demographic data and clinical findings. We assumed that duplicated data was not included. We obtained de-identified data from NPDS. Based on the Colorado Multiple Institutional Review Board (COMIRB) on Human Subjects Protection guidelines and procedures, the analysis of NPDS data for this research did not meet the criteria for human subjects research according to the 45 Code of Federal Regulations (CFR) 46. 101(b). Therefore, this study was determined to be exempt (COMIRB#: 22-1088).
ML-based approach We used a DT model using Statistical Package for the Social Sciences (SPSS) 26 and Python (version 3. 9) for distinguishing between RSTI and acute acetaminophen ingestion using clinical and laboratory characteristics extracted from the NPDS. The DT model uses split criteria to divide an end node and set a tree with a rate for each predictor variable  [19]. It divides the data into binary parts and builds a binary tree based on them so that two edges come out of each inner node, and the resulting trees are pruned. This algorithm is used to create regression and classification trees. A DT model evaluates the variable that best divides the data  [20]  using the Gini index criterion to decide how to select tree nodes. The Gini Cost Function shows how pure the nodes are, wherein the purity of the node refers to the degree to which the training data assigned to each node is combined.
The division continues until the node has the minimum number of training samples or exceeds the maximum depth of the tree. The root node is the most critical variable that starts the decision tree graph. It is the variable that best divides the data in a DT approach. Intermediate nodes are those in which variables are estimated but not the ultimate nodes in which predictions are formed. Similar to other ML methods, standard performance measures were used to measure the performance of the DT model, including precision, recall, accuracy, F1 score, and confusion matrices. A confusion matrix was generated with all the required information to measure specificity, accuracy, and sensitivity  [13].
Discussion We assessed the efficacy and accuracy of a DT model to distinguish acute and RSTI of acetaminophen using retrospective large-scale NPDS dataset analysis. Our DT model had acceptable characteristics and could correctly predict 3,361 cases of acute acetaminophen poisoning and 3,381 RST exposures. These findings show promise in using ML to help differentiate acute ingestions from RSTIs. In our DT model, the most relevant predictive variable was age. This result is unsurprising given that elderly patients are more likely to use acetaminophen as an analgesic due to various ailments, raising the possibility of poisoning due to RSTI. Our study found that most RSTI exposures tend to produce aminotransferase concentrations between 100 and 1,000 IU/L or greater than 1,000 IU/L. Prior studies have found that all cases with RSTI who developed hepatotoxicity were found to have abnormal ALT (more than 50 IU/L)  [8]  and more likely to have encephalopathy requiring renal replacement therapy, mechanical ventilation, or death  [21].
Other studies have shown that patients with RSTI have increased creatinine concentrations, and serum INR had a significant association with hepatotoxicity. All patients who died or had a liver transplant showed increased creatinine concentrations  [23]. It is possible that people with RSTI had an acute illness (e. g. , hepatitis, dehydration) causing the acute kidney injury and minor elevation in transaminases. The dataset does not include information  about the prior or concomitant illness that likely caused the kidney injury. Our findings also showed that gastrointestinal manifestations, including abdominal pain and nausea/vomiting, might help differentiate acute and RSTI of acetaminophen exposures. Previous literature characterized nausea, vomiting, anorexia, diaphoresis, malaise, pallor, and lethargy during the first 24 h post-acute ingestion  [28, 29].
The retrospective design of the current study may have resulted in biases due to confounding factors. Future studies will be required to assess and validate our models' performance. Other limitations include insufficient documentation during poison center calls or possible transcription errors. Since NPDS provides data that has been fully anonymized, we assumed duplicate data was not included but could not determine precisely. Also, this study included cases of acetaminophen exposure between January 2012 and December 2017. To enhance predictive accuracy, future studies are needed to include data from recent years, larger data sets, and more sophisticated modeling methods considering different variables such as the acetaminophen formulation taken and co-ingested drugs. The doses of acetaminophen and time to N-acetylcysteine administration are the most important considerations that cannot be determined from NPDS data.
5 and δ 39. 52 corresponding to DMSO-D6 in 1 H NMR and  13  C NMR respectively, δ 1. 56 and δ 3. 30 related to moisture present in CDCl3 and DMSO-D6, respectively. δ 8. 32 and δ 79. 16 peaks orresponding to CHCl3 traces observed in 1 H NMR and  13  C NMR in DMSO-D6, respectively. Chemical shifts were reported in δ (ppm) relative to TMS as an internal standard and J values were given in Hz (hertz). Multiplicity is indicated as, s (singlet); d (doublet); t (triplet); m (multiplet); dd (doublet of doublets), td (triplet of doublet) etc. FT-IR spectra were recorded on Alpha (Bruker) infrared Spectrophotometer. High-resolution mass spectroscopy (HRMS) [ESI+] were obtained using either a TOF or a doublefocusing spectrometer. 1-Methyl-2-(arylethynyl)-1H-indole-3-carbaldehyde (1) were Prepa red using the reported procedure and spectral data was compared (1a, 1b, 1c, 1f, 1l, 1m)  16  and (1g).
Acknowledgements This work has been executed as part of the  Indo-French "Joint Laboratory for Natural Products and Synthesis towards Affordable Health". We thank  CSIR ,  CNRS  and  University of Rennes 1  for the support. The authors thank the  Department of Science and Technology (DST), New Delhi , ( DST-INSPIRE/03/2018/001878 ) and  CSIR, New Delhi , for research fellowships. [ CSIR-IICT Communication No IICT/Pubs. /2022/345].