dataesr/scientific-paragraphs-categorization huggingface dataset:
Results Among the 113 oral targeted anticancer drugs recovered from our literature search, we selected those (n = 81) for which sufficient data studies to allow for the estimation of MAT were anticipated, either accessible from FDA files or from the international literature. Within this set of 81 molecules, 74 had an FDA file, and their pharmacologic class (ATC5 and ATC4 classification), initial approval indication and date by the FDA and the sponsor are reported in the Supplementary Material (Table  S1 ). Their main physicochemical and biopharmaceutic properties are presented in Table  1. Besides the 71 kinase inhibitors within this set of 81 molecules, 10 drugs with similar physicochemical properties are included (i. e. , inhibitors of isocitrate dehydrogenase enzyme IDH-I (n = 2: enasidenib, ivosidenib), hedgehog pathway inhibitors HP-I (n = 3: glasdegib, sonidegib, vismodegib), and poly(ADP-ribose) polymerase inhibitors PARP-I (n = 5: niraparib, olaparib, pamiparib, rucaparib, talazoparib)).
Physicochemical and Biopharmaceutical Properties The solubility characterization was based on the values of the solubility reported at neutral pH and classified according to the USP classification. The distribution of the solubility is illustrated in Supplementary Material (Figure  S2 ). Two-thirds of the compounds (n = 47) have a solubility rated as practically insoluble (PI, i. e. , <0. 1 mg/mL), and 88% (n = 65) have a pH-dependent solubility. Hence, most of these compounds (92%, n = 68) were rated as BCS class-2, BCS class-4, or BCS class-2/4. The mean MW (479 ± 83 daltons), TPSA (97. 3 ± 22. 8 Å), and Log P (3. 6 ± 1. 2) were close to the mean values calculated from a large database of PKIs either approved or in clinical trials (MW: 463 daltons, TPSA: 96. 6 Å and Log P: 3. 5  [14]. The mean (± SD) in silico-estimated Caco-2 and MDCK cell permeability was 9.
Physicochemical and Biopharmaceutical Properties The solubility characterization was based on the values of the solubility reported at neutral pH and classified according to the USP classification. The distribution of the solubility is illustrated in Supplementary Material (Figure  S2. ). Two-thirds of the compounds (n = 47) have a solubility rated as practically insoluble (PI, i. e. , <0. 1 mg/mL), and 88% (n = 65) have a pH-dependent solubility. Hence, most of these compounds (92%, n = 68) were rated as BCS class-2, BCS class-4, or BCS class-2/4. The mean MW (479 ± 83 daltons), TPSA (97. 3 ± 22. 8 Å), and Log P (3. 6 ± 1. 2) were close to the mean values calculated from a large database of PKIs either approved or in clinical trials (MW: 463 daltons, TPSA: 96. 6 Å and Log P: 3. 5  [14]. The mean (± SD) in silicoestimated Caco-2 and MDCK cell permeability was 9.
Impact of Tested Compounds on Cdk5-Dependent Neurite Growth The inverse agonist properties at Cdk5 signaling of 3e, 3f, 3g, and the prototypic inverse agonist SB-258585 were evaluated in NG108-15 cells transiently expressing 5-HT 6 R by measuring neurite outgrowth, as previously reported. NG108-15 cells were transfected with plasmids encoding either cytosolic GFP or a GFP-tagged 5-HT 6 R and grown on glass coverslips for 6 h. Then, cells were treated with either vehicle or the evaluated compounds. Neurite growth was measured on cells imaged using an AxioImagerZ1 microscope equipped with epifluorescence (Zeiss) using the Neuron J plugin of the ImageJ software (NIH). A detailed description is reported in the Supporting Information.
Introduction: Novel psychoactive substances (NPS) have been increasingly reported in the last 15-20 years. We aimed to describe presentations to the emergency department (ED) with acute recreational drug toxicity involving NPS. Methods: Data were extracted from the European Drug Emergencies Network (Euro-DEN) Plus database for all presentations to ED (36 EDs in 24 European countries) with acute toxicity between January 2014 and December 2019. Patient demographics, agents involved, and clinical outcomes were described and the subgroup of presentations involving NPS was compared with the rest of the cohort. Results: Out of 43,633 Euro-DEN Plus presentations, 3304 (7. 6%) involved at least one NPS. Agents were identified mainly based on self-report or clinical presentation, with analytical confirmation being performed only in 17.
Euro-DEN plus project and database Euro-DEN was created in 2013 and was initially funded by a grant from the European Commission (JUST/2012/DPIP/AG/ 3591). Since 2014 the network has expanded to form the Euro-DEN Plus project. The case definition for an ED presentation to be included is: i) the patient and/or accompanying person reports the recreational use of psychoactive drug(s) related to the patient presentation to ED; and/or ii) the attending physician records that the presentation is consistent with use of such drug(s); and/or iii) such drug(s) are confirmed via analytical testing  [14]. In accordance with usual clinical practice, toxicological analyses were not routinely performed as management was based primarily on clinical features of toxicity and the drugs reported or suspected  [15].
Presentations associated with prescription or over-the-counter medication were included if these drugs were used for recreational purposes but not if the presentation was related to an adverse effect or deliberate/accidental self-poisoning of a prescribed or over-the-counter medication. Presentations were also excluded if they were related to lone ethanol toxicity, not directly related to acute recreational drug toxicity (e. g. , trauma, infection, and withdrawal), or associated with self-harm. A standardised minimum dataset of key demographic, clinical and outcome variables was purposely designed and collected for all consecutive presentations with acute recreational drug toxicity to participating EDs  [13]. Data were handled in compliance with relevant national legislation and local ethical approval.
Study design All presentations in the Euro-DEN Plus database between 1 January 2014 and 31 December 2019 (72 months) were included. The following variables were extracted from the database for each presentation: (i) demographic data (date of presentation, age, and sex); (ii) drugs reported including co-ingestion of ethanol; (iii) presence/absence of cardiac arrest on presentation; and (iv) outcome data (disposition from ED, length of hospital stay, and death within hospital stay). Overall frequency of NPS-related presentations and their geographical distribution were defined. Focusing on centres reporting cases for all 6 years, annual trends of NPSrelated presentations were evaluated. Patient demographics of the whole cohort were analysed. When two or more agents were identified in a single presentation, either by selfreport or by analytical confirmation, all agents were considered in their respective groups.
Agents are identified based on patient's and witness' selfreport or clinical presentation, with analytical confirmation being performed in a minority of presentations. In our database, although there was generally a high degree of agreement between self-reported drugs and analytical toxicology results for commonly used illicit drugs (e. g. , heroin and cocaine), NPS were reported more frequently in centres where mass spectrometry was available than in centres that performing immunoassays only  [48]. Another study from 2 Norwegian centres participating in Euro-DEN Plus reported that NPS were found in 8% of screened cases, though they were suspected in none  [49]. It is therefore likely that the prevalence of NPS is clinically underestimated in our study. Moreover, even in cases where patients self-report a specific agent, drug names can be misinterpreted (e.
g. , Mephedrone and Methedrone), and the chemical composition of products obtained may not be as advertised  [50] [51] [52] [53]. Finally, data was entered into the database prospectively by each centre based on information available in patients' clinical records, which resulted in a proportion of missing data for certain variables (as seen in Figure  1  and Table  1 ). Presentations with missing data were not deleted in a listwise fashion to avoid introducing bias.
Study Site and Population In 2021, the survey investigated undergraduate students registered at the Montpellier University, until the sixth year in the medicine, dentistry, pharmacy universities and before the end of the third year of midwifery school. The Montpellier University welcomes all health students from the center-south region of France. All students were contacted by email to participate to the survey: 653, 348, 551 and 240 students from medicine, dentistry, pharmacy and midwifery participated, respectively. Participant' recruitment was performed using the internet, leading to a self-nominated sample. Participants were informed about the purpose and procedure of the survey. The e-mail addresses database was provided by the administrator of each university (medicine, dentistry, pharmacy and midwifery) who was officially informed of the survey.
Measurement Tool The students answered directly online by filling out an anonymous questionnaire in a spreadsheet format via Google Forms. It comprised three pages of A4 format with a total of 35 items (single-item question), including demographic information and questions about N 2 O, illicit drugs (poppers, ecstasy, cocaine, ketamine, LSD) and alcohol use. Students who declared to be N 2 O users were asked subsequent questions about behavioural characteristics, such as dose and frequency of consumption, place of purchase, the use of other substances, and the sensations caused by N 2 O. Cigarette and/or cannabis smoking, coffee and alcohol drinking were assessed by four levels: 'never', 'sometimes', frequently' and 'daily'. It took approximately 10 min to complete the questionnaire. Information was collected from all respondents to the survey, via fill-in forms that were completed anonymously in the database.
The power calculation was derived from the logistic regression model which tested the effects of different covariables with the consumption of N 2 O (yes/no). Based on the work of Peduzzi  [17] , the minimum number of subjects to include was calculated by: N = 10 k/p, with 'k' being the number of covariates in the model (k = 20) and 'p' being the estimated proportion of N 2 O users (p = 0. 40). It was found that the sample size would be at least 500 subjects. All analyses were performed with the statistical software Stata 16. 1 (StataCorp, College Station, TX, USA) based on a two-sided type I error with an alpha level of 0. 05.
Accepted manuscript / Final version Drug profiling is not routinely performed in forensic toxicology laboratories and innovative data visualization solutions thus remain poorly used in such contexts. Molecular networking (MN) is a computational strategy that may help visualization and interpretation of the complex data arising from MS analysis. Initially developed within the field of natural products research, this tool is particularly useful for complex biological matrices analysis at the molecular level  [7] [8] [9] [10]. Data reprocessing using open source softwares such as MzMine 2 or Cytoscape allows visualization of the acquired data by non-targeted screening  [7, 11]. MN is able to identify compounds and potential similarities among all MS/MS spectra within the dataset and to propagate annotation to unknown but related molecules.
Drug powder analysis Samples were analysed on an Orbitrap Q Exactive™ mass spectrometer coupled to an Accela 1250 pump (both from Thermo Scientific, San Jose, CA) using a heated electrospray ionization source (HESI-II). Data acquisition, calibration and instrument control were managed using Xcalibur® 2. 1 software (Thermo Scientific). Quantitative assays of illicit drugs were performed using a LC-HRMS method. The mobile phases were composed of ammonium formate at 10 mM and formic acid 0. 1% in water (phase A) and acetonitrile and formic acid 0. 1% (phase B). LC was performed on a Hypersil Gold column (5 mm x 2. 1 mm, 3 µm) (Thermo Scientific, San Jose, CA). Heroin and cocaine were quantified on calibration curves using heroin-D9 and cocaine-D3 as internal standards, respectively (LOQ = 5ng/mL, range : 10 -1000 ng/mL , r² > 0.
Generation of molecular networks Molecular networks were generated according to Le  Daré et al. (2020)   [15]. Raw spectral data files were converted from. raw format into the. mzXML open mass format using Proteowizard software  [16]. Spectra were converted from profile to centroid mode in the positive ionization mode. Preprocessing of the data was required for the generation of multisample molecular networks, and the use of precursor ion abundance for relative quantification. Such processing additionally allows to take into account the retention time and thus to separate isomers into individual nodes in the resulting molecular networks. For this, the Feature Based Molecular Network process was followed  [17]. Preprocessed and aligned spectral file were saved as a single. mgf file using the integrated Global Natural Products Social Molecular Networking (GNPS) export module of MZmine 2  [17].
Powder X analysis To identify compounds in a drug seizure suspected to contain heroin, we generated a molecular network that displayed the MS/MS data acquired during analysis (Figure  1 ). We annotated the nodes with the precursor ion mass, and we labeled the edges with the mass shift between the two connected nodes. During the generation of the network, nodes are linked together in clusters according to their MS² spectral similarities. The size of each node is proportional to the peak area of each compound, allowing semi-quantitative analysis. We performed node annotation on the basis of their chromatographic retention time (if available), exact mass, and the match between the fragmentation spectrum and the GNPS and/or mzCloud (https://www. mzcloud. org, HighChem LLC, Slovakia) spectral databases.
We also used spectral in-house database and bibliography information for annotation. Visual analysis of the molecular network allows us to raise several observations. First, the annotation step using spectral match revealed the presence of heroin (m/z 370. 167), confirming illicit compound content. Its derivative 6-monoacetylmorphine (6-MAM) (m/z 328. 156) was identified, linked to the heroin node by a mass shift of -42. 0104 (typical of an acetyl group). Second, caffeine (m/z 195. 088), acetaminophen (m/z 152. 071), noscapine (m/z 414. 156), papaverine (m/z 340. 156) and 6monoacetylcodeine (m/z 342. 172) were also detected as impurities (from synthesis or from plant origin) or cutting agents (Figure  1 ). Taken together, these results suggest that MN allows to quickly visualize groups of structurally similar compounds and thus to quickly differentiate cutting products from structurally related metabolites (coextracted compounds or synthetic impurities), with identification of illicit compounds.
Lastly, levamisole adulteration, widely used in South America, seems to take place immediately after production or just before exportation  [22]. Here, MN helped in sample-to-sample comparison, and appeared to be of particular interest in major component profile screening. Furthermore, all cocaine derivatives were grouped in a unique cluster, allowing to quickly discriminate cocaine-related ions from adulterants. Illicit drug powder profiling usually corresponds to a combination of complex, expensive and timeconsuming analytical and mathematical methods hyphenated to adequate databases  [22, 31]. However, visualization tools, such as MN, appear to be under-used. Guéniat and Esseiva (2005)  reported that powder comparison is usually based on GC-MS analysis, searching for major compounds (e.
CONCLUSION This is the first application of a molecular network approach for the analysis and comparison of illicit drug seizures of natural origin. Here, organization of data using MN brings a major benefit compared to classical approaches. It provide visual modeling that facilitates the interpretation and understanding of toxicological test results by forensic scientists and magistrates in the context of law enforcement agencies seizures analysis. When used in conjunction with spectral databases, it thus provides valuable information on the product manufacturing, from synthesis to final shaping. Further investigation and exploitation of the spectral similarities evidenced by MN in the frame of illicit drugs profiling will undoubtedly indicate such visualization approaches as necessary assets in the toolbox of the legal toxicologists.
In case of DRP detection, the pharmacist suggests an optimization of the treatment, by performing a "pharmacist's intervention"  [11]. To homogenize this practice, a SFPC (Société Française de Pharmacie Clinique) working group has implemented a validated tool for the documentation of hospital pharmacists' interventions as of 2003  [12]. In 2006, this instrument was computerized to make it accessible for all French pharmacists and a website, Act-IP©, was created to gather all detected DRPs and pharmacists' interventions performed  [13]. Pooling pharmacists' interventions on the website provide a national observatory of clinical pharmacy practices in France. Analyzing the registered data recorded there helps understand the epidemiology and DRPs characteristics, as well as identify the drugs associated with high iatrogenic risk.
X-ray Diffraction Analysis (XRD) IP-DHA were analyzed via XRD using a AX2 D2 diffractometer (Brucker, USA) with Cu Kα radiation (30 kV, 10 mA) and θ-θ geometry, using a lynxeye detector with an angular range of 5 to 42 • 2θ. A total of 2 mg IP-DHA was lightly pressed on a silicon wafer to obtain a flat surface. Data was interpreted using DIFFRAC. EVA software.
Histological Analysis All animals were killed via cervical dislocation. The eyes were rapidly enucleated and fixed in 4% paraformaldehyde for 24 h at 4 • C. Eyecups were embedded in paraffin and cut into 5 µm sagittal sections. For hematoxylin/eosin/saffron (HES) staining, the sections were deparaffined, labeled with HES using standard protocols, rinsed, and mounted in Moeviol. The thickness of the inner nuclear layer (INL) and the outer nuclear layer (ONL) was measured and expressed in arbitrary units using ImageJ software. Thickness measurements were made manually on the central part of the retina (near the optic nerve). A total of 20 measurements were made per eye (10 for the ONL measurement and 10 for the INL measurement), and these values were averaged to calculate the ratio of INL/ONL thickness.
This researched was funded by the  French National Research Agency (ANR)  (LiPoPheRet  ANR-18-CE18-0017 ) and by  SATT AxLR. Institutional Review Board Statement: The mice were housed in facilities that were accredited by the  French Ministry of Agriculture and Forestry  (n •  C34-17236-19 December 2014 ). Experimental procedures were conducted in accordance with the European directive, 2010/63/ EU , and the  French Ministry of Education and Research  (APAFIS n •  15117-2018051712092842 v3 ), concerning the care and use of the animals. Cryo-electron microscopy was supported by the  French Infrastructure for Integrated Structural Biology , a national infrastructure supported by the  French National Research Agency  ( ANR-10-INBS-05 ). authors declare no conflict of interest.
Acknowledgements The  Centre National de la Recherche Scientifique (CNRS)  and the  Université Paul Sabatier (UPS)  are acknowledged for financial support of this work. M. R. thanks  MESRI de l'Enseignement Supérieur, de la Recherche et de l'Innovation)  for her PhD fellowship (Contrat Doctoral Spécifique Normalien). The " Direction du Numérique " of the  Université de Pau et des Pays de l'Adour, CINES  under allocation A011080045 made by  Grand Equipement National de Calcul Intensif (GENCI)  and  Mésocentre de Calcul Intensif Aquitain (MCIA)  are acknowledged for computational facilities. The  NMR and XRD services of ICT  ( P. Lavedan  and  S. Mallet-Ladeira ) are acknowledged for assistance with the HOESY NMR experiments and single-crystals X-ray diffraction analyses.
The skin-on-a-chip device is an appropriate platform to study the kinetics of transdermal drug delivery of different topical formulations in a dynamic diffusion system. Practically, it is a miniaturised version of a Franz diffusion cell with a direct connection between the tissue and the flowing perfusion media. Based on the data in the literature, a three-step session has been developed. (1) First, a composition analysis was performed in the intact skin models. Six different characteristic components of the skins were analysed by Raman Spectroscopy. Ceramides are a family of waxy lipids, which are composed of sphingosine and a fatty acid. Ceramide is the main component of the Stratum corneum of the epidermis in human skin  [16, 17]. Ceramides, cholesterol and saturated fatty acids create a water-impermeable, protective structure to prevent water loss through the skin due to evaporation, and they make a barrier against the entry of microorganisms and external chemicals  [17].
Determination of Caffeine and Quinidine in the Perfusion Samples (HPLC) High-performance liquid chromatography (HPLC) analysis was performed using an Ultimate 3000 (Thermo Fisher Scientific, Voisins-le-Bretonneux, France) piloted with Chromeleon 7. 1 software (Thermo Fisher Scientific, Voisins-le-Bretonneux, France). A C18 column with a particle size of 5 µm and dimensions of 4. 6 × 150 mm (Interchim, Montluçon, France) was used and adjusted on 25 • C. The software used was Chromeleon 7. 1 (Thermo Scientific, chromatography data system software). Caffeine quantification: Samples were analyzed to quantify the concentration of caffeine and establish the time-dependent permeation kinetics using a DAD UV ultimate 3000 detector. Detection wavelength for caffeine was 272 nm. The mobile phase used in isocratic mode was methanol 50%: water 50% (v/v) + 10 mM of phosphoric acid.
Data Analysis Data analyses were performed using SkinTools 3 (RiverD International B. V. , version 3. 3. 201202, Rotterdam, The Netherlands). The spectra were cut between 400 and 1800 cm -1 for the FP region. For the HWN region, the spectra were cut between 2500 and 3600 cm -1. A baseline subtraction was applied using a 3rd-degree polymer function. TEWL values were compared before and after the diffusion experiments using paired Student t-test. p < 0. 05 was confirmed as a statistically significant difference. In the case of skin composition analysis, the Arbitrary Unit vs. Depths curves were calculated, and the individual dots were shown as means ± standard error of mean (SEM). The Area Under the Curve (AUC) values were generated from these plots by OriginPro 2015 Sr2 software (MacaSoft Bt, Győr, Hungary) with the trapezoid method.
The AUC values were compared with human data using unpaired Student t-test. p < 0. 05 was regarded as a statistically significant difference. In the case of HRE, only a few tissue samples (2 for composition studies, 1 for drug penetration experiments) were available; thus, these data can only be regarded as pilot results. Both on the excised skins and on the in vitro skin substituents, 8-10 different locations were analysed by Raman spectroscopy, and for HRE, the replicates showed very low variability.