dataesr/scientific-paragraphs-categorization huggingface dataset:
7 deaths per million, more than double the average seen for all other age bands. 2  In France, the DRAMES (Décès en Relation avec l'Abus de Médicaments Et de Substances) register is a database of drug-related deaths that aims to identify the psychoactive substances involved (medications or illicit drugs), assess their dangerousness, and estimate the trends in the number of these deaths. The register is based on the prospective annual nationwide collection of data on all deaths for which toxicology analyses have been performed. All drug-related deaths are notified to France's National Agency for the Safety of Medicines and Health Products. Postmortem toxicologic analyses are performed as part of the search for the cause of death, but only at the request of the judicial authorities. When there is no suspicion of drug implication in a death, an analysis is not usually ordered.
Data Collection Data were collected from the forms completed by the toxicologist that included the following information for each individual: demographic characteristics; medical and substance abuse history; circumstances in which the body was found; autopsy and pathological findings; identification of the substances found in analyses of biological specimens and quantities in the blood or in other matrices if blood specimen was not available; and probable cause of death. These data made it possible to classify each case as either directly substance-related cause of death (toxic cause alone, usually overdose; and toxic cause with related pathology) or indirectly substance-related (eg, falling from a high place while under the influence of cocaine). Indirectly drug-related deaths were not included in this report.
Statistical Analysis We performed χ 2 tests of homogeneity for selected drugs or drug classes over the whole study period. All analyses were conducted with R, version 3. 6. 3 (R Foundation for Statistical Computing), and its tidyverse meta-package, including the ggplot2 package for plotting. Smoothed curves correspond to LOESS (locally estimated scatterplot smoothing) curves obtained using ggplot2. To account for the multiplicity of comparisons for homogeneity tests, the statistical significance threshold for P values was adapted using a Bonferroni correction (P <. 0025). Data analyses were performed from January 1, 2012, to December 31, 2022.
INTRODUCTION Despite being considered one the safest pharmaceuticals, paracetamol/acetaminophen (Nacetyl-p-aminophenol, APAP) overdose is one of the most important causes of drug-induced liver injury, that can eventually lead to acute liver failure (ALF)  (1, 2). Data from the U. S. Acute Liver Failure Study Group registry of more than 700 patients with ALF across the US, implicates APAP poisoning in nearly 50% of all ALF. In Europe, APAP overdose was found to represent one-sixth of all-cause ALF  (2). Overall, APAP overdose, even without suicidal intent, represents a large proportion of ALF leading to registration for transplantation  (1). APAP-induced hepatotoxicity is mainly caused by the production of N-acetyl-p-benzoquinone imine (NAPQI), a highly reactive intermediate formed predominantly via the cytochrome P450 2E1 (CYP2E1).
Statistical Analysis Univariate analyses were performed to compare fold changes, calculate the areas under the curve (AUC) and determine statistical significance. AUCs were calculated using trapezoidal numerical integration (trapz function) (46) from MATLAB. Statistics Toolbox TM from MATLAB (Eigenvector Research Inc. , Wenatchee, WA, USA) was used to assess statistical significance using a one-way ANOVA t-test, considering P < 0. 001 statistically significant, followed by a multiple comparison test (multcompare) to perform pairwise comparisons, based on the Tukey-Kramer critical value  (47).
Funding: This article was supported by the  MoU  signed between  Inserm  and the  Mailman School of Public Health of Columbia University  on Nov. 12 2019. EG was funded by the  Inserm  and the  Mailman School of Public Health of Columbia University  collaboration project. The first observational study with PWs was partially funded by the  French Agency for Environmental Health Safety  ( PNREST Anses ,  2018/1/084 ). The  PELAGIE cohort  has been funded by  Inserm , the  French Ministries of Health  ( 2003-2004 ), Labor ( 2002-2003 ), and Research ( ATC 2003-2004 ) and the  French National Institute for Public Health Surveillance (InVS ,  2002-2006 ).
Fifty-five screened patients with venous leg ulceration and documented venous reflux (superficial or deep) on duplex ultrasound were identified using local databases and clinic lists; these were approached and provided with information on the study, with full informed consent taken from those willing to participate. Thirty-two patients consented, enabling the authors to meet the recruitment target. Patients were excluded as per the screening and recruitment workflow (Fig. 1 ); in total, 28 participants were included in the analysis. All participants underwent a comprehensive history and clinical examination. Ulcer size and surface area were recorded and baseline blood, urine, and ulcer fluid samples were collected for all participants. At week 20, ulcer size and healing status were recorded; in total, 15 patients had healed and 13 had not.
Statistics Selected characteristics of the study populations were described and compared according to healing status. Differences between healing groups were tested using the Kruskal-Wallis 1-way analysis of variance (ANOVA). The raw MS data were collected in centroid mode and converted to netCDF format using the DataBridge tool implemented in MassLynx software (Waters Corporation, Milford, USA). The data were pre-processed using the XCMS  18  package in R programming software; an output table was obtained comprising pairs of mass/charge ratio (m/z), retention tim, and intensity values of the detected metabolite features in each sample. Integrals of metabolic features with coefficient of variation > 30% in the quality control (QC) samples were removed. The resulting data underwent total area normalization.
For the NMR dataset, spectral data were imported into MATLAB R2014a (MathWorks, USA) for preprocessing. The regions corresponding to water, urea, and TSP were excised and the remaining spectra were calibrated and probabilistic quotient normalized using in-house developed scripts. Each dataset was analyzed via SIMCA 14 (Umetrics, Sweden) to highlight the difference between the experimental groups and to identify key discriminatory metabolic features. Principal component analysis (PCA) was applied to assess instrument stability, clustering of samples, and separation of experimental groups, as well as to detect analytical or biological outliers. Orthogonal partial least squares discriminant analysis (OPLS-DA) with significance testing by ANOVA of the cross-validated residuals (CV-ANOVA) was performed, using the orthogonal filter to remove extraneous systematic variance in the data.
19 Univariate statistical analysis was applied to each discriminatory metabolite identified from the multivariate modeling followed by a 2-tailed Student t test with a Benjamini-Hochberg correction for multiple testing (adjusted P value). An adjusted P value of < 0. 05 was defined as significant. Metabolite identification by MS was conducted by matching accurate m/z measurements of detected chromatographic peaks and molecular fragments using the literature,  20  Kyoto Encyclopedia of Genes and Genomes,  21  LIPID MAPS,  22  and METLIN. 23 Tandem MS fragmentation patterns were performed to obtain further structural elucidation. NMRderived metabolites were identified using in-house databases and the published literature. 24.
Metabolic Profiling Analysis The metabolic profiling analysis of serum, urine and ulcer fluid samples generated 13 datasets. PCA models for the RP-UPLC-MS profiles showed no obvious separation between the healed and nonhealed groups in serum, urine, and ulcer fluid in either ionization mode (see Figure, Supplemental Digital Content 2, Figures  1 2 3 4 5 6 , PCA models, http://links. lww. com/SLA/D134); conversely, OPLS-DA models highlighted separation according to healing phenotype. The significance and robustness of the corresponding OPLS-DA models for serum (ESI-, P = 0. 0005 for R 2 Y = 90% and Q 2 Y = 57%), urine (ESI+, P = 0. 046 for R 2 Y = 64% and Q 2 Y = 25% and ESI-, P = 0. 024 for R 2 Y = 87% and Q 2 Y = 31%) and ulcer fluid (ESI-, P = 0. 047 for R 2 Y = 58% and Q 2 Y = 20%) were confirmed by CV-ANOVA testing (Table  2  and Fig.
Review methodology The purpose of this review was to provide an update on the molecular anticancer mechanisms and signaling pathways of Erinacin A. To achieve this, recent data from studies recently published in electronic databases were researched, such as PubMed/MedLine, Springer, Web of Science, ScienceDirect, TRIP database; using the following MeSH search terms: "Antineoplastic Agents", "Phytogenic/therapeutic use", "Apoptosis/ drug effects", "Cell Line", "Tumor, Cell Survival/drug effects", "Diterpenes/chemistry", "Diterpenes/isolation & purification", "Diterpenes/ pharmacology", "Erinacine A", "Neoplasms/drug therapy", "Neoplasms/metabolism", "Neoplasms/pathology", "Proteomics", "Signal Transduction". The taxonomy of the plants has been validated according to the World Flora Online  [23]  and chemical formulas were verified according to ChemSpider  [24].
To investigate the potential of position C5 of the benzothiazole for optimization, we first solved a crystal structure of A in a complex with a 24 kDa fragment of E. coli GyrB (GyrB24) at a resolution of 1. 16 Å (PDB code: 7P2N) (Figure  3 ). As expected, the inhibitor is bound in the ATP-binding site of GyrB24. The benzothiazole ring is involved in a cation-πstacking interaction with the Arg76 side chain. The aromatic carboxylate group at C6 of the benzothiazole of inhibitor A and the Arg136 side chain form a salt bridge. The carboxamide oxygen interacts with a conserved water molecule and the hydroxyl group in the side chain of Thr165. The 3,4-dichloro-5methylpyrrole moiety is involved in hydrophobic interactions in the lipophilic pocket with residues Val167, Val120, Ile78, Val71, Ala47, and Val43, while the NH group of pyrrole forms a hydrogen bond with the side chain of Asp73.
The hydroxy group at C5 of the benzothiazole scaffold is oriented toward the solvent-exposed area. This allows introduction of larger substituents to C5, which could influence the pharmacokinetic properties of benzothiazole-based compounds and possibly broaden the antibacterial activity. Based on the data obtained from the crystal structure and favorable on-target activity of inhibitor A, we decided to further explore the chemical space by the introduction of a fluorine atom and amine substituents at the C5 position (Figure  2 ). To probe potential interactions of new substituents with the enzyme, docking calculations and MD simulations were performed using the cocrystal structure of E. coli GyrB containing the flexible loop Leu98-Gly117, which is absent in the crystal structure of A and GyrB (PDB ID 7P2N).
2. 4. Docking Calculations, MD Simulations, Structure-Based Pharmacophore Modeling. The binding modes of inhibitors D and E in the ATP-binding site of E. coli DNA gyrase were studied by a combination of docking calculations, MD simulations, and MD-derived structure-based pharmacophore modeling. For molecular modeling, we used the cocrystal structure of E. coli GyrB in complex with phosphoaminophosphonic acid-adenylate ester (PDB ID 4WUB),  24  in which the flexible loop Leu98-Gly117, absent in the complex with A (PDB ID 7P2N), is present and allows investigation of the potential interactions between the substituent at C5 and the enzyme. Docking calculations showed that the binding mode of the core 2-(3,4-dichloro-5-methyl-1H-pyrrole-2-carboxamido)benzo-[d]thiazole-6-carboxylic acid is identical to that of A (Figure  3 ).
Determination of Antibacterial Activity. Antimicrobial assays (MICs) were performed by a standard serial broth microdilution method. For details on antimicrobial assays, see the Supporting Information. 4. 4. X-Ray Crystallography. The X-ray structure of compound A in complex with E. coli GyrB24 was obtained at a resolution of 1. 16 Å (PDB code: 7P2N). For details on protein expression and purification, as well as crystallization, X-ray data acquisition, and structure solution, see the Supporting Information. 4. 5. Molecular Modeling. For molecular modeling, the cocrystal structure of E. coli GyrB in complex with phosphoaminophosphonic acid-adenylate ester (PDB ID 4WUB)  24  was used. Molecular docking calculations were performed in Schrodinger Release 2022-1 (Schrodinger, LLC, New York, NY, USA, 2022).
MD simulation of compound D or compound E in complex with E. coli GyrB was performed using the NAMD package (version 2. 9)  26  and the CHARMM36m 27 force field. Pharmacophore feature analysis of E. coli GyrB in complex with compound D or compound E was performed using LigandScout 4. 4 Expert. 28  For details on molecular docking, molecular dynamic simulations, and structure-based pharmacophore modeling, see the Supporting Information.
5 ms for long-range coupling evolution (32 scans were accumulated). Typically, 1024 t2 data points were collected for 256 t1 increments. For all compounds, the 5-nitrofuran-2-acryle hydrazone frame presents comparable characteristics in terms of chemical shifts and multiplicities. For the nitrofuranyl system, the chemical shifts are between 7. 65 and 7. 80 ppm for the C 4 -H hydrogen atom and 115. 48-116. 09 ppm for the C 4 carbon atom. For the C 3 -H position, the chemical shifts are between 7. 06 and 7. 21 ppm for the hydrogen atom and 113. 27   Compound 5 has very broad peaks at 298 K for all hydrogen and carbon atoms of the benzothiazole frame. This is also the case to a lesser extent when we operate at an elevated temperature (378 K). At 378 K, we can identify all peaks except the quaternary C 18 and C 19 carbon atoms.
NMR Analysis 1 H and 13 C nuclear magnetic resonance (NMR) was performed using on a Bruker Avance NEO 600 spectrometer equipped with a 5 mm broadband inverse triple resonance probe 1 H BB (31P-103Rh)/31P with Z field gradients and Bruker 400 MHz spectrometers (except indication for 300 MHz) with IconNMR automation software that allows fully automated acquisitions. All chemical shifts for 1 H and 13 C were expressed in parts per million (ppm) relative to TMS using 1 H (residual). DMSO-d 6 was used as solvent (except indication).
X-ray Single Crystal A series of low-temperature single crystal X-ray diffraction experiments were performed using an XtaLAB Synergy-S Rigaku diffractometer equipped with a hybrid photon counting Hypix-6000HE detector and Cu (λ = 1. 5406 Å) microsource. The crystal was cooled down to 100 K using an Oxford Cryosystems 800 cryostream cooler device and measured at increasing temperature up to 300 K with a step of 50 K. The CrysAlisPro program suite was used for pre-experiment, data collection, determination of the UB matrices, and initial data reduction  [41]. Crystal structures of 2 were solved and refined using SHELXS and SHELXL programs  [42]. Hydrogen atoms were located from geometry after each refinement cycle with U iso = 1. 2U eq of their carriers.
Evaluation of the Antileishmanial Activity L. donovani (MHOM/ET/67/HU3, also called LV9) promastigotes and axenic amastigotes were maintained according to the protocols previously described  [43]. In vitro antileishmanial evaluation on L. donovani axenic amastigotes. This evaluation was performed using the SYBR Green method as previously described. IC 50 values were calculated using the ICEstimator version 1. 2 software (http://www. antimalarialicestimator. net/runregression1. 2. htm, accessed on 5 April 2023). Miltefosine was used as the reference drug. In vitro antileishmanial evaluation on intramacrophage amastigotes. RAW 264. 7 macrophages were infected with L. donovani axenic amastigotes according to a ratio of 10 parasites per macrophage. In these conditions, the percentage of infected macrophages was around 80%, and the mean number of amastigotes per infected macrophage was 4 to 5 in the untreated controls.
Acknowledgments: The authors gratefully acknowledge the  Centre National de la Recherche Scientifique (CNRS)  and the  University Paul Sabatier  for financial support. We (A. K. , M. B. ) thank the  Occitanie-France Region  for financing the 6 months internship of A. K. in the frame of the  MECH-API  ( ESR_PREMAT-00262 ) Prematuration Project coordinated by  E. Colacino  and the  Montpellier University. This work was also supported by the  Paris Saclay University  (S. C. and P. L. ).
Acknowledgements We gratefully acknowledge the  French Agence Nationale de la Recherche  ( ANR ) for finding (grant number  ANR-20-CE07-0004-02 , Ap-PET-I), as well as the  CNRS , the  Université de Strasbourg , the  Université Lyon 1 and CPE-Lyon  for support. AL is much grateful to the  French Ministry of Higher Education, Research and Innovation  for a PhD grant. The  French Fluorine Network (GIS-Fluor)  is also acknowledged for its support. Finally, the authors also thank  Dr. Emeric Wasielewski  ( NMR platform ) and  Matthieu Chessé  (analytical facilities) of the LIMA ( UMR7042   CNRS-Unistra-UHA ), as well as the analytical facilities and staff of the  Fédération de Chimie "Le Bel"  ( FR2010 ) of the  Université de Strasbourg , who contributed, by their valuable technical and scientific support, to the achievement of this research project.
Instrument Conditions A LC-HRMS method described in a previous publication  [27]  was used in the present study. A phenyl-hexyl (PH) column (100 mm × 2. 1 mm i. d. , 2. 6 µm particle size, Thermo Fisher Scientific, Waltham, USA) was kept at 40 • C, and elution was performed in gradient mode using a mixture of water/ammonium formate (2 mM)/0. 1% formic acid (solvent A) and ACN/MeOH (50:50, v/v)/1% water/0. 1% formic acid (solvent B) at a flow rate of 500 µL/min. The total run time was 20 min, with a gradient of solvent B from 1-99% in 17. 5 min. The Q-Exactive orbitrap accurate mass spectrometer (Thermo Fisher Scientific, Waltham, MA, USA) with a heated electrospray ionization (HESI) probe was used to detect compounds in positive-ion mode. The data were acquired in Data Dependent Acquisition (DDA) mode, consisting of one full MS scan and up to five data-dependent MS/MS scans (full MS/ddMS2).
Post-Processing of HRAM Data The raw data obtained from the study were processed using Compound Discoverer 3. 2 software (Thermo Fisher Scientific, Waltham, MA, USA), which utilized a metabolic workflow to identify potential metabolites. This workflow involved phase I biotransformation reactions, such as oxidation, reduction, and hydrolysis, as well as phase II reactions, such as glucuronide conjugation. The 3-HO-PCE chemical structure was uploaded in a. mol file format, and data was processed after background subtraction. Metabolites were predicted based on their exact mass, elemental composition, isotopic pattern, and ddMS2 spectra. The m/z shifts from the parent compounds fragmentation were used to localize metabolically transformed moieties. Only features triggering MS2 experiments were considered, and the mass error between theoretical and experimental exact mass had to be within ±5 ppm.
The resulting annotated spectra were manually reviewed to generate a final metabolic profile using ChemDraw 20. 1 software (PerkinElmer, Waltham, MA, USA). Metabolites were sorted in ascending order based on their exact mass, and isomers were indicated in ascending order of retention time. Although reference standards were not available for confirmation, metabolite abundance was expressed as peak area ratio (PAR, %) with respect to the total peak area (TPA) for each sample. In pHLM, TPA did not include 3-HO-PCE peak area corresponding to the remaining fraction after 2 h incubation, which did not undergo metabolization. However, in biological samples (urine, hair), 3-HO-PCE peak area was included in the calculation of total peak area as it represented the fraction of unchanged 3-HO-PCE excreted in urine or as a parent drug integrated into the hair shaft.
To cross-check the metabolites identified, raw data from biological samples (urine, hair) analyzed by LC-HRMS were processed using the same metabolic workflow as pHLM incubates. Drug-free urine and hair samples were analyzed and processed simultaneously to subtract background signals and avoid false positive results. Since commercial reference standards of the detected metabolites were not available, NMR spectra could not be acquired, thus structure assignments are tentative, based on ddMS2 fragmentation. ddMS2 spectra and extracted ion chromatograms of 3-HO-PCE and its related metabolites obtained from HLM, urine or hair are reported as Supplementary Material (Figures  S2  and S3 , respectively).
Sensory Analysis Flavored and unflavored goat cheese sensory analysis took place in the sensory laboratory at VetAgro Sup (Lempdes, France), equipped with individual booths. The sensory panel consisted of 17 panelists (fourteen females and three males) who were experienced in tasting dairy products (more than 100 h on sensory characterization on cheeses). A triangle test was carried out to evaluate the sensitivity of panelists to differentiate the bitter perception among flavored goat cheeses (JCG and MCGC), according to ISO 4120 standards. A total of 3 samples of cheese, 2 of which randomly belonged to the same treatment (i. e. , JCG or MCGC), were offered for tasting (serving temperature 20 • C), and panelists were asked to identify the odd sample. Data were collected with the Tastel software (version 2019; ABT Informatique, Rouvroy-sur-Marne, France).
Statistical Analysis Statistical data analysis was carried out using R studio software. Student's t-test and one-way analysis of variance (ANOVA) were performed on the biological triplicates of microbiological and chemical variables, followed by a Tukey's HSD (honest significant difference) post hoc test, in order to evaluate the significant differences (p-value < 0. 05) between the three typologies of cheeses. For the values of volatile compounds relative area, we consider a statistical difference between the three typologies of cheeses, considering a p-value below 0. 01, in order to identify the most reliable volatile compounds in cheese differentiation  [41]. For sensory analysis, the Shapiro-Wilk test was first used to evaluate if the distribution of bitterness scores followed a normal law.
As this condition was not checked, sensory data were analyzed using the non-parametric Friedman test followed by Nemenyi post hoc test. The free vocabulary description of each cheese was analyzed by pooling together descriptors presenting the same sensations. We consider only descriptors with a minimum of 2 citations by the whole sensory panel. Principal component analysis using the number of citations of sensory descriptors and volatile compounds relative area for the three typologies of cheeses was carried out on OriginPro 2020 (OriginLab Corporation, Northampton, MA, USA).
Acknowledgments: The authors would like to thank GAEC des Trèfles à Quatre Feuilles in Brousse (63490) for the possibility of implementing the gentian flavoring process during its usual cheesemaking process; CPPARM for gentian root sampling and LEXVA Analytique for their instrumental support. We thank Noëlle Champroux and Tom Coelho for the cheesemaking protocol design and Gilles Figueredo for laboratory and expertise support. We thank our financial supporters: Agence Nationale de la Cohésion des Territoires (ANCT) and the Fonds national d aménagement et de développement du territoire (FNADT).