dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical Analysis The results were represented as mean with standard deviation (± SD), unless otherwise indicated. The statistical analysis was performed using GraphPad Prism: Version-5. 1 (GraphPad Software, Inc. , San Diego, CA, USA). The pharmacokinetics parameters was calculated by "PK-Solver Software, Nanjing, China using MS-Excel-2013" through the noncompartmental approach. The comparative analysis of the data was accomplished by Student's t-test and p < 0. 05 was considered as statistically significant, unless otherwise indicated.
Materials and Methods : The study was conducted at the University Hospital of Strasbourg. PIs data was collected over the year 2017. Data from the first six months of 2017 was labelled by two pharmacists, who manually assigned one of the 29 possible classes from the French Society of Clinical Pharmacy classification. A deep neural network classifier was trained to learn to automatically predict the class of PIs from the processed text data. Results: 27,699 labelled PIs were used to train and evaluate a classifier. The accuracy of the prediction calculated on the validation dataset was 78. 0%. We predicted classes for the PIs collected in the second half of 2017. Of the 4,460 predictions checked manually, 67 required corrections. These verified data was concatenated with the original dataset to create an extended dataset to re-train the neural network.
Dataset The study was conducted in a large, 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 the PIs generated on prescriptions of all adult inpatients at the exclusion of those admitted in intensive care units, obstetrics and gynaecology, or psychiatry units. The extracted data included pharmacists comments and documents describing (in French) the reason for the PIs together with messages forwarded to the prescribers.
Text processing PI documents were written as French plain text -often in the form of technical notes for prescribers and needed to be processed. Text processing was performed using R (version 4. 1. 0) [29]. Briefly, abbreviations and keywords have been replaced by their corresponding expressions. Accents, punctuations, numbers and duplicated documents were removed. The 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 similarly at the exception of the very last step where only terms initially present in the training document term matrix were kept to build the validation document term matrix.
Manual encoding of pharmacists' interventions A total of 27,699 unique text documents of PIs were extracted for the first six-months of 2017. For each document, a specific PI code was attributed independently by the two pharmacists. The amount of divergent coding between the two ratters were n= 1,006 discrepancies (3. 6%) for which a consensus labels were determined. It is important to remind that a single code was attributed to every document. Three subclasses (codes 2. 3, 6. 5, 9. 2) were not observed in the dataset. In order to make the algorithm able to learn these classes, we manually added credible synthetic comments proposed by clinical pharmacists, resulting finally in 27,721 unique documents (table 2).
Deep neural network classifier The training dataset and its corresponding    [20]. It contains explicit definitions for each category of PIs to reduce ambiguity in coding a problem. The 29 classes and subclasses of the initial dataset were unbalanced, with an overrepresentation of the situations corresponding to "non-conformity of the drug choice compared to the formulary" and "non-conformity of the drug choice compared to the guidelines". On the opposite, there was an under-representation of classes such as "adverse drug reaction" (~4/10,000) or "failure to receive drug" (~1/1,000) -in full line with the fact that these events are rare. But as compared to Bedouch et al. [32] , where "adverse drug reaction" or "failure to receive a drug" accounted for 4. 3% and 0. 8% of their 34,522 interventions, respectively, we observed clearly less events in these two categories -possibly because they were focusing on DRP.
Indirectly it shows that PIs are not strictly limited to DRP issues in the prescription review process. The SFPC classification seems well adapted to describe pharmacist interventions in our hospital. Indeed, the class named "other" represented only 7. 1% of all PIs of the initial dataset. This likely includes possible coding difficulties for the experts due to a lack of details in the documentation or because of complexity of interpretations. This has to be compared with some studies in which the "other" class represented up to 39% of all DRP  [15]. 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 specific patient".
Several explanations can be given -including insufficient number of cases in the initial dataset or more linguistically complex documents that may require some inference to understand which class the entities involved are related to. Algorithmic prediction errors in the external validation dataset (second six-months of 2017) spotted by an expert pharmacist were low (1. 5% of the predictions with a probability larger or equal to 0. 95 and a code differing from the subcategory "non-conformity of the drug choice compared to guidelinescode 1. 1"). A detailed manual analysis of these errors showed that they corresponded to complex clinical situations where multiple PIs could be encoded. Incorporating additional validated data from the second half of 2017 (see figure  1 ) into an expanded dataset improved the performance of the classifier (accuracy reached 81%).
Statistical analysis All data management was performed using GraphPad Prism version 9. 2. 0 and 9. 3. 1 software  [33]. Statistical analyses including the Mann-Whitney test, one sample Wilcoxon test, nonlinear regression (four parameters, variable slope), simple linear regression, and regression slope test were performed with a significance threshold of 0. 05. For the quantitative test, a parasite growth inhibition curve was obtained using nonlinear regression analysis. The linear portion of the curve was visually located by plotting the parasites growth (Fluorescence Unit = FU) against the Log 10 artesunate concentration and analyzed by simple linear regression. A regression slope test was performed to conclude on the linear relationship between artesunate concentration and parasite growth.
O (462. 2 + 1). Found 463 (M + + 1). Spectroscopic data were in agreement with those reported. [22] ystal and refinement data Single crystals of 1 were covered with viscous hydrocarbon oil and were mounted on loops. Data were obtained at 123 K on a Bruker X8 APEX II CCD diffractometer equipped with graphite-monochromated Mo-Kα radiation (λ = 0. 71073 Å). For complex 2, a single crystal covered with oil based cryoprotectant was mounted on a cryoloop. The single crystal XRD measurement was carried out at 100 K on a Bruker D8 Venture equipped with a fine-focus sealed tube with a Triumph graphite monochromator displaying Mo K α1 wavelength (λ = 0. 7103 Å) and a PHOTON100 CMOS detector. Data were collected using Bruker Apex2 software. Single crystals of C 5 Ph 5 OH were coated with viscous hydrocarbon oil and mounted on glass loops, and data were collected on a Rigaku SynergyS diffractometer.
The SynergyS operated using microsource Cu-Kα radiation (λ = 1. 54178 Å) at 123 K. Data processing was conducted using CrysAlisPro. 55 software suite. [28] Single crystals of 3 were mounted on loops. Data were obtained at 190 K on an Oxford Diffraction Gemini Ultra S diffractometer, using Cu-Kα radiation (λ = 1. 54184 Å). The structures were solved using SHELXS7 and refined by full-matrix least-squares on all F 2 data using SHELX2014  [29]  in conjunction with the X-Seed graphical user interface. [30] ll hydrogen atoms were placed in calculated positions using the riding model. Data collection and refinement details are collated in Table  S1.
Acknowledgements This work was funded, in part, by the  Agence Nationale de la Recherche  ( Ferrodance  project). A CC-BY public copyright licence has been applied by the authors to present document and will be applied to all subsequent versions up to the Authors Accepted Manuscript arising from this submission, in accordance with the grant's open access conditions. This work was also supported by the  Université de Rennes 1 and CNRS. We gratefully acknowledge the  Fonds Européen de Développement Régional (FEDER ;  D8   VENTURE Bruker AXS  diffractometer), Thermofisher (generous gift of 2,2,6,6-tetramethylpiperidine) and  BASF  (generous gift of (R,R)-and (S,S)bis(α-phenylethyl)amine). W. E. would like to thank  Prof. F. Mongin  for support, critically reviewing this document and making valuable suggestions.
Phytochemical Retrieval and Preparation The Indian Medicinal Plants, Phytochemistry, And Therapeutics (IMPPAT) compound library was used to find the available compounds of the preferred plant  [3] , and the PubChem database was used to retrieve the phytochemicals compounds. A total of 63 compounds were identified from C. roseus  (46)  and A. marina  (17)  through the abovementioned databases listed in Table  S1. The phytochemicals of the two plants have been searched in PubChem through the smile identity and saved in a 2D (SDF) file format. The compounds were prepared and optimized, then converted into pdbqt file format and saved for further examination.
This study evaluated the abovementioned PK properties of the selected compounds that found the optimum value of ADME properties of the selected four compounds. Toxicity is defined as a condition in which a substance shows adverse effect and can harm an organism. Toxicity is reportedly responsible for 20% of late-stage drug discovery failures. Animal trials are used for toxicity testing, which is a complicated, expensive, and time-consuming process. In silico toxicity analysis, which does not require animal trials and is quick and inexpensive, can be used to justify preclinical drug production  [37]. Therefore, the toxicity profile of the selected top four phytochemicals was assessed using in silico methods. The noncarcinogenic properties of the compounds were calculated using data from in silico toxicity test servers.
Target Preparation The three-dimensional (3D) experimental tertiary structure of the TS was retrieved from the RCSB protein data bank (PDB) (https://www. rcsb. org/, accessed on 1 September 2021). The targeted protein structure (PDB ID: 1HVY) having a resolution value of 1. 9 Å containing 288 AA with 135. 83 kDa molecular weight  [39] , served as a receptor in the docking process. From the beginning to the end, the target was prepared in BIOVIA Discovery Studio Visualizer 2020 software and all the metal ions, cofactors, and water molecules were removed during the structure preparation. AutoDock Tools were employed for the calculation of gasteiger charges and the addition of hydrogen atoms to the protein.
Compound's Retrieval and Preparation In this study, the compound's information was obtained through a comprehensive online database search of various phytochemical composites containing medicinal plants derived from a natural source that is employed for drug design and discovery processes. A manually curated Indian Medicinal Plants, Phytochemistry, And Therapeutics (IMPPAT) database covering >9500 phytochemicals along with >1742 Indian medicinal plants provided cheminformatic techniques to speed up drug discovery of natural bioactive compounds  [40]. From the IMPPAT database, a total of 63 phytochemicals of C. roseus  [38]  and A. marina  [41]  which possess anticancer properties respectively were identified and retrieved for virtual screening (Table  S1 ). To conduct further screening procedures, Simplified Molecular Input Line Entry System (SMILES) formats  [42]  were retrieved from the PubChem database.
DFT Method Based Geometry Optimization The Gaussian 09 program (revision D. 01) and gauss view 5. 0. 8 molecular visualization software were used to perform the theoretical quantum chemical calculations with a computer configuration of 2. 20 GHz (224 GB RAM, 4 processors) with Windows 10 pro version. Compound optimization was performed by the implementation of conventional functionals Becke's (B)  [51]  three parameters with Lee-Yang-Parr (LYP)  [52]  functionals (B3LYP) with DFT/6-31G (d,p) method. Moreover, the optimized structure was retrieved for further calculation of EHOMO, ELUMO, HOMO-LUMO band gap, electrostatic potential, electrophilicity index, global reactivity (global hardness and global softness), optimized energy and dipole moment parameter of the compounds. The compound's reactivity, chemical hardness, and softness calculation play an important role in determining the characteristics and polarizability of the electron's configuration  [53].
MD Simulations and Trajectory Analysis Molecular dynamics (MD) simulation was performed to understand the binding stability, fluctuation, conformational changes, and kinetic behavior of desired compounds to the targeted protein. 'Desmond v3. 6 Program' of Schrodinger (academic version in Linux environment) was utilized to analyze the MD simulation of the target-ligand complex structure by using the OPLS-2005 force field. The system was solvated by using the TIP3P water model. The boundary condition selected in this study was orthorhombic (box shape), and a buffer box was chosen as a calculation method with a box distance of 10 Å. Charges were electrically neutralized by adding suitable ions such as Na + and Cl - with the concentration of salt 0. 15 M. The MD simulations were performed at 300 K and one atmospheric pressure (1.
01325 bar) with a 10 ps time interval. The SID module in the Schrodinger package was implied to analyze the quality of the MD simulation and the simulation event. From the simulation trajectories, the root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), hydrogen bonding interaction, solvent accessible surface area (SASA), and protein-ligand interaction were evaluated. 4. 9. 1. RSMD Analysis RMSD is a standard numerical measurement of average conformational changes between the backbone of a protein structure (target) and the ligand (reference) over a certain time frame compared to a reference time  [59]. During the MD simulation, the protein frames and the reference frame backbone were first aligned, and then the system's RMSD was determined based on the atom selection. RMSD can be calculated from the following Equation (1) with the time frame x to the MD simulation during 50 ns.
Acknowledgments: We would like to acknowledge the  Taif University Researchers Supporting Project  ( TURSP-2020/68 ),  Taif University, Taif ,  Saudi Arabia. The  Deanship of scientific research at Umm Al-Qua University  for supporting this work by grant code ( 22UQU4290565DSR07 ). This research was also supported by  Basic Science Research Program  through the  National Research Foun-dation of Korea (NRF)  funded by the  Ministry of Education  ( NRF-2020R1I1A2066868 ), the  National Research Foundation of Korea (NRF)  grant funded by the  Korea government (MSIT)  (No. 2020R1A5A2019413 ), a grant of the  Korea Health Technology R&D Project  through the  Korea Health Industry Development Institute (KHIDI) , funded by the  Ministry of Health & Welfare, Republic of Korea  (grant number:  HF20C0116 ), and a grant of the  Korea Health Technology R&D Project  through the  Korea Health Industry Development Institute (KHIDI) , funded by the  Ministry of Health & Welfare, Republic of Korea  (grant number:  HF20C0038 ).
Data collection The study was conducted from February to April 2021. To select informants, we either used a random approach by visiting household or street vendors in each previously cited town, or we followed the guidance of key informants (i. e. , members of Haururu association and Académie Marquisienne) who helped us to find traditional healers or knowledgeable persons in the area. All the interviews were carried out by the first author of this study, and in a few cases, a translator (key informant or family member) was also asked to help. A semi-structured questionnaire was used to interview each informant. This questionnaire comprised different parts, including: -Sociodemographic data: age, gender, place of residence, place of birth, number of children, occupation, level of education, religion, spoken language(s).
Acknowledgements: We would like to thank all the participants for their kindness, their patience and for sharing their knowledge with us. Our warmful thanks go to the  Association Haururu  and the  Académie Marquisienne  for their time, their fruitful feedback, and their help on the field. Special thanks to  Maiana Teihotu ,  Sylvana Tiatoa ,  Jenny Torea , and  Yvette Temauri  for being such great facilitators in the field. We are thankful to  David H. Lorence  (senior researcher at  NTBG ) for providing us some information on Marquesan plant species. This study was made possible thanks to the support from the  Maison des Sciences de l'Homme du Pacifique (MSHP)  through an early-stage researcher award obtained by the first author.
DI SC US SION Literature is very scarce about the risk of burn after capsaicin patch application. A case of a third-degree burn induced by high concentration topical capsaicin in a 57-year-old man has been already detailed. 8 In this case, the fourth application of the patch on the left hip has led rapidly to a second-degree burn, and necrosis 2 days after. Seven months later, no functional impairment or sequelae, apart from aesthetics, have been reported. One "application site burn" has been also reported in a randomized clinical trial, but not further described. 9 The summary of product characteristics raises the possibility of second-degree burns, but its frequency remains unknown. An analysis of VigiBase, the WHO global database of individual case safety reports, made on 15 March 2022, showed 45 cases of second-degree burns (3.
Background: The duration of trial follow-up affects the ability to detect recrudescent infections following antimalarial treatment. The aim of this study was to explore the proportions of recrudescent parasitaemia as ascribed by genotyping captured at various follow-up time-points in treatment efficacy trials for uncomplicated Plasmodium falciparum malaria. Methods: Individual patient data from 83 anti-malarial efficacy studies collated in the WorldWide Antimalarial Resistance Network (WWARN) repository with at least 28 days follow-up were available. The temporal and cumulative distributions of recrudescence were characterized using a Cox regression model with shared frailty on study-sites. Fractional polynomials were used to capture non-linear instantaneous hazard. The area under the density curve (AUC) of the constructed distribution was used to estimate the optimal follow-up period for capturing a P.
Sensitivity analyses When the analysis was restricted to outcomes at day 42 and observations beyond this time-point were censored, the distribution of the timing of recrudescence after AL in Africa shifted to the left compared to the distribution including all data up until day 63 (Fig. 4 ; left panel, solid lines). Similarly, the distribution of recrudescence for DP in those treated with ≥ 48 mg/kg also shifted to the left The temporal trend of observing recrudescence during the follow-up period, estimated by the instantaneous baseline hazard function (right panel). Data in Africa was restricted to children < 5 years whereas patients of all ages were included in Asia and S. America. Data from S. America were grouped with Asia. All studies used at least 3 locus genotypes (the usage of microsatellites was considered as a separate locus).
In a population of patients the hazard of recrudescence rises as blood concentrations of the antimalarial drug fall below MIC values and then falls again as the majority of recrudescences have occurred. New infections are also constrained by residual concentrations of the slowly eliminated antimalarial drugs and "bunch" together after blood levels fall below prevailing MIC values  [22]. Characterization of the temporal trend enabled the construction of the probability distribution of the infections estimated to be recrudescences. In African children less than 5 years old treated with DP, the peak distribution of recrudescence was around day 39 after initiation of therapy in children who were adequately dosed (≥ 48 mg/kg total dose of piperaquine), but this fell to Table  1  The area under the curve of the estimated probability distribution AL, artemether-lumefantrine; DP, dihydroartemisinin-piperaquine; PIP, piperaquine; CI, confidence interval a Estimated in children < 5 years in Africa and in patients of all ages from Asia The 95% confidence interval was estimated from 1000 bootstrap samples of data for AL; for DP not under-dosed this was from 925 samples, and for DP under-dosed this was based on 987 bootstrap samples drawn from the original dataset day 30 in children treated with a lower total dose of piperaquine (< 48 mg/kg).
Acknowledgements We would like to thank the patients and all the staff who participated in the clinical trials included in this IPD meta-analysis. We would like to thank  Dr. Birgit Schramm ,  Dr. Mary Oguike ,  Dr. Maman Laminou ,  Dr. Patrick Sawa ,  Dr. Ingrid van den Broek  and  Prof. Christopher Whitty  for their help with data access. We thank the  Novartis team  ( Dr. Caroline Boulton  and  Dr Guoqin Su ) for thoroughly reviewing the manuscript. We thank the  Sanofi team (Dr. Andrew Hockey, Sanofi-Global Health)  for providing support through making patient level data available. Study group members: The complete list of author's contribution is listed in Additional file 2. The members of the  WorldWide Antimalarial Resistance Network (WWARN) Methodology Study Group  are the authors of this paper: Prabin Dahal ,  World Wide Antimalarial Resistance Network (WWARN) ,  Oxford, UK ;  Infectious Diseases Data Observatory (IDDO) ,  Oxford, UK ;  Centre for Tropical Medicine and Global Health ,  Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom; Julie Anne Simpson, Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia ;  Salim Abdulla ,  Ifakara Health Institute, Dar es Salaam, Tanzania ;  Jane Achan , Malaria.
Acknowledgements We thank all the participants, especially the PhD students at the  Centre for Public Health, Queen's University Belfast , for their comments and participation during the pilot study; and  Dr Gabriel Baron  for his clarification on nonparametric statistics. This study is based on a thesis prepared as part of the  MSc in Public Health 'Comparative Effectiveness Research' of Université de Paris, France , and the research was conducted in the  Centre for Public Health, Queen's University Belfast, UK.