dataesr/scientific-paragraphs-categorization huggingface dataset:
The final structure was generated and relaxed taking into consideration both FMN and heme cofactors (Figure  4A -C and Supplementary Figure  S3 ). The model that features an I-SC score of 35 was also validated using MolProbity (score of 1. 6) and fit the higher (score of 3) CAPRI class level for complexes. Interaction energy between domains was estimated in the range of 10 kcal/mol using the Prodigy software. This is very similar to the energies/dissociation constants described previously for CPR with CYP1A1  [51]  as well as with CYP2C19, 2D6 and 3A4  [52]. Residues involved in side chain contacts at the interface formed a dense double layer of interacting side chain atoms that could be involved in propagating conformational information from the FMN to the heme cofactor through the interface.
Caffeine Docking to CYP1A2 To understand how caffeine binding could affect the CYP1A2 structure, in silico binding studies were performed. First, the α-naphthoflavone molecule was removed from the CYP1A2 crystal structure (PDB code 2HI4)  [53]. After relaxing the resulting structure in water using Yasara (see Material and Methods), docking experiments (a total of 300) were performed with caffeine, resulting in 138 distinct clusters. An analysis of these clusters demonstrated that the first 12 corresponded to caffeine bound within the active site, in the distal cavity of the heme. In these clusters, only four caffeine orientations could be found. The three docked structures issued from the structures with the lowest interaction energies (thus probable best binding) and corresponding to the putative orientations of the three major caffeine products with CYP1A2 are presented in Figure  5A.
Phyto-compounds After a thorough literature analysis, the chemicals 1,8-Cineol (CID 2758), ellagic acid (CID 5281855), α-Terpineol (CID 17100), and epicatechin (CID 72276) were chosen as representative compounds to carry out the molecular docking investigation (Table  1 ). The standard antibiotic used was gentamicin (CID 3467). In Figure  3 , the 2-dimensional structures and functional groups of the four main compounds present in the extracts are displayed, along with gentamicin and the native ligand of the enzyme. These compounds were subjected to in silico testing to assess their ability to impede the aminoacylation process facilitated by TyrRS (PDB code 1JII). To determine their binding affinities, components of the highly effective in vitro extracts were evaluated and compared to the standard antibiotic, gentamicin.
Docking analysis and protein preparation AutoDock Tools 1. 5. 7 was utilized to determine the binding affinity of the selected phytoconstituents to the binding site of the bacterial tyrosyl-tRNA synthetase. The program implements the gradient optimization method in its local optimization process, and ranks the ligands based on the empirical binding free energy (ΔG in kcal/mol) function  [26]. The 3-D structures of the target enzymes were obtained in PDB format from the Protein Data Bank. PDBQT format files were generated by removing water molecules, polar hydrogen and Kollman charges were added to protein residues, and the protein's native ligand was eliminated.
Molecular dynamics simulations: The initial HMA model was obtained from the protein data bank (PDB ID 5GIX) which has been cocrystallized with seven palmitate (PLM) molecules  23. Missing N-and C-terminal residues were added using the Modeller software  24. Protonation states of titratables residues (namely arginine, lysine, glutamate and aspartate) were determined using the H++ server assuming a physiological pH at 7. 4. Since HMA was shown to be natively bound to PLM with a molecular ratio ranging from 0. 1 to 2. 0 in physiological conditions, we built the present HMA model with two PLM molecules, which were considered docked in fatty acid binding sites (FABS) 2 and 5  25, 26. The apo form of HMA was also considered in the present investigations. Three models of palmitate-bound PTM albumins were also built in the present study, namely HNA1, HNA2 and N-truncated HMA.
All systems were solvated in explicit water box for which minimal distance between atoms and box edges were set up at 10 Å. Systems were all neutralized considering physiological NaCl salt concentration ([NaCl] = 154 mM). FF14SB  27  and TIP3P  28, 29  force elds were used to respectively model protein residues and water molecules. Parameters from Joung and Cheatham  30  were used to model Na+ and Cl-counterions. Parameters for PLM, Sbonded cysteine were derived from Lipid17 31 and FF14SB force elds while those for cysteic acid residues were derived from amber 99SB-based parameters available in the literature  32. Each replica was minimized and then equilibrated for 10. 25 ns. MD production run were performed for 2 ms. Simulations were analyzed using the CPPTRAJ package  33  , and in house python scripts.
Acknowledgements This project was supported by the  European Union 's  Horizon 2020 research innovation program   FET-OPEN No 862179  and the  European Research Council  ( ERC-2019-COG-864576 ). We thank the  CEA of Saclay  for constant support of our research activities. The authors warmly thank  Clara Zavarise  ( DRF-JOLIOT-SCBM, CEA ) for the cover artwork and  Rémi Blieck  for proof-reading.
Impacts On Practice In hospitals, clinical pharmacists contribute to the optimization of patient care by detecting drugrelated problems through medication prescription review and issuing recommendations to physicians and nurses. Retrospective classi cation and analysis of recommendations made by clinical hospital pharmacists during medication prescription review can provide important ndings to improve inpatient care. The deep neural network classi er we used met the challenge of automatically classifying recommendations from a large database and highlighted the potential contribution of arti cial intelligence to improving clinical pharmacy practices.
Study design This retrospective observational study was conducted at the University Hospital of Strasbourg (over 2,400 beds) in 2022-2023. This hospital provides several activities (surgical, medical and research). Data were extracted for a period of 3 years (2018-2020) from the prescription support software of the hospital. The extracted records were limited to care units using Dxcare® (Dedalus, France, exclusion of intensive care units). Data collected included the following variables: prescriptions with ATC (Anatomical Therapeutic Chemical) code of the drugs, patient characteristics (age, gender), medical departments and clinical pharmacist recommendations. Prescription medication review depended on the setting and could be performed in the medical departments or in the central pharmacy.
The use of a deep neural network classi er is the strong point of this study. It met the challenge of automatically classifying recommendations made by clinical hospital pharmacists from a large database without mobilizing signi cant human resources. The interpretation of our results should however be tempered by some recognized limitations. First, the tool used to classify recommendations is 81% accurate; thus, errors undoubtedly occurred in the observed results. Second, the classi cation we used focused only on the identi cation of DRPs and not on their causes or consequences. We were therefore unable to assess the clinical relevance and potential impact of the identi ed DRPs on patient outcomes. Third, these results are limited because the data are from a single French hospital and cannot be generalized.
Quantification was performed by using the cell image analysis software CellProfiler (Broad Institute, Cambridge, MA, USA). Briefly, a threshold (minimum cross entropy method) was applied to every image to minimize non-specific fluorescence background signals. Cell nuclei counts were quantified after declumping using the "IdentifyPrimaryObjects" function of CellProfiler. Fluorescence intensities were measured as total fluorescence intensity (TFI) after masking and extracting the fluorescence regions. Mitochondrial ROS generation was measured after applying a "parent-child relationship" to MitoTracker and CellROX channels in CellProfiler to determine signal overlap. The separated signal quantification of CD206 on LSECs and MDMs was performed using ImageJ (Fiji, NIH, Bethesda, MD, USA). MDMs were identified by significantly brighter staining than LSECs due to their naturally high expression of CD206.
Cytokine analysis. Cytokine release in vascular effluents was quantified using LEGENDplex Human Inflammation Panel 1 (BioLegend, San Diego, CA, USA). The assay was performed according to the manufacturer's instructions. Samples were measured using a BD FACSCanto II flow cytometer (BD Biosciences). Data analysis was performed using the LEGENDplex Data Analysis Software v8. 0 provided by the manufacturer. Cytokines not shown in result figures were below the detection limit of the assay. Cell viability assay. Cellular viability of treated liver models was measured after 7 days of drug perfusion using the CellTiter-Glo Luminescent Cell Viability Assay (Promega, Madison, WI, USA). Biochip membranes were excised and transferred to a 48-well microplate containing cell-specific phenol red-free William's Medium E medium.
Risks of medicines concerned by the COVID-19 pandemic EPI-PHARE has realized several studies on the risks of COVID-19 associated with certain drugs; among them we can cite synthetic antimalarials, antihypertensive drugs and aspirin. In the first months of the pandemic, we conducted a nationwide cohort study using data from SNDS in order to evaluate the risks of hospitalization, oro-tracheal intubation and death during hospital stays for COVID-19 in patients using long-term synthetic antimalarial drugs (AMD: hydroxychloroquine or chloroquine) and to compare them to the risks in the general population. From a total of 54,873 patients with long-term use of synthetic antimalarial drugs matched with 155,689 unexposed individuals, no difference was observed between synthetic AMD long-term exposure ).
We examined the different COVID-19 risks in patients treated by angiotensinconverting enzyme inhibitors (ACE) and angiotensin receptor blockers (ARBs) compared with calcium channel blockers (CCBs). Using the data from the SNDS, we constructed a cohort of 2 million hypertensive patients followed for 16 weeks in which 2,338 were hospitalized and 526 were intubated or died for COVID-19. Patients treated with ACE inhibitors and ARBs were at lower risk of COVID-19 hospitalization compared with patients treated with CCBs (HR, 0. 74 [0. 65; 0. 83] and 0. 84 [0. 76; 0. 93], respectively) and at lower risk of intubation/death. In addition, patients treated with ACE inhibitors were at slightly lower risk compared to ARB users. These results, who have been awarded by the American Heart Association the prize for the best article on population data published in the journal Hypertension in 2021, overturned the initial assumption that patients treated with sartans were at greater risk of severe COVID-19, and have been subsequently confirmed by other studies  [10].
Factors associated with the risk of COVID-19 In the pandemic context, fast-track access to hospital medico-administrative databases has been implemented in France, and EPI-PHARE worked in near real-time on the entire French population (67 million people). We were thus able to estimate the chronic diseases and health conditions associated to COVID-19 related hospitalization and in-hospital mortality. In the whole French population between February 15, 2020 and June 15, 2020, we identified 87,809 people who were hospitalized for COVID-19 and 15,661 people who died in hospital. We confirmed in this study that age is by far the most important risk factor for severe forms of COVID-19, with a risk of hospitalization more than 5-fold higher and a risk of in-hospital mortality more than 100 -fold higher in people aged 85 years and older compared to people aged 40 to 44 years.
Pharmaco-epidemiological surveillance of COVID-19 vaccines in France With the beginning of the COVID-19 vaccination campaign, ANSM set up a specific system for a strong surveillance of the efficiency and security of COVID-19 vaccines in France. This system, which is part of the risk management plan coordinated by the European Medicines Agency (EMA), relies on both pharmaco-vigilance, i. e. , the analysis of adverse drug reactions reported by healthcare professionals and vaccinated individuals in the national pharmacovigilance database (BNPV) and pharmaco-epidemiology that aims at measuring the efficiency of COVID-19 vaccines and to quantify their risk of adverse drug reactions at the population level. The objectives of this reinforced surveillance were to perform an ongoing evaluation of the efficiency and safety of COVID-19 vaccines in order to take rapid action if necessary, and to allow the Ministry of Health to adapt the vaccination strategy to ensure the safety of vaccinated individuals.
The fast response of EPI-PHARE during the COVID-19 pandemic is the result of a long history of collaboration between ANSM and Cnam who understood very early on the advantages of implementing pharmaco-epidemiological studies using medico-administrative databases. It allowed the French authorities to have reliable data on the use, benefits and risks of health products for their decision-making during a health crisis, and highlighted the full potential of the exploitation of medico-administrative databases. The unique model of EPI-PHARE and its results during the COVID-19 pandemic could encourage other health authorities to develop high-level competences in epidemiology, biostatistics and data-management, in order strengthen their decision-making process for the safety of patients.
Using NMR data to constrain metabolic simulations We next estimated metabolite exchange fluxes from the HRMAS  13  C NMR trajectories, linking NMR datasets to the supporting metabolic pathways, genes and their dynamic recruitment over the experimental time series (Fig. 1b ). To estimate metabolite concentration trajectories, the integrated NMR signal over time for each  13  C compound was first fit to a logistic curve, using the equation: f (x) = L 1+e -k(x-x 0 ) + C ( 1 ) where L is the upper asymptote, k is the growth rate and x 0 is the inflection point of the sigmoidal curve (Supplementary Table  2 ). For the U-  13  C substrates, the constant C accounts for remaining substrate after 36 h; for the products, C equals zero. Final concentrations of  13  C-labeled products were estimated using the known U-  13  C substrate concentration, ratios of  13  C NMR peak area and empirically determined correction factors for NOE-induced signal amplification (Estimation of exchange fluxes for dFBA).
Discussion We illustrate advancements in HRMAS NMR and genome-scale metabolic modeling to track single carbon and nitrogen flow through complex anaerobe metabolism. The sealed rotor chamber used in HRMAS NMR maintains an anaerobic environment, ensuring maintenance of reducing conditions, conservation of mass and biological containment. The nondestructive nature of HRMAS NMR spectroscopy enables high-resolution analyses of living cells in small reaction volumes, on the order of tens of microliters, whereas alternatives using GC, MS or regular solution NMR often require orders of magnitude more volume and cellular mass to support longitudinal analyses. The acquisition of HRMAS NMR datasets is also far more rapid and does not require further extraction or preparation. Additionally, we show that HRMAS NMR detects metabolic shifts accompanying defined perturbations in nutrient availability as demonstrated by the impact of selenium limitation on C.
difficile's metabolism of [U-  13  C]proline by its proline reductase selenoenzyme  25. Longitudinal tracking of fermentable  13  C-labeled substrates supports high-resolution monitoring of individual pathways within complex nutrient conditions. The cellular scope of genome-scale metabolic models provides means to incorporate multiple NMR datasets tracking fermentable substrates into a unified dFBA solution with inference of flux through pathways not directly captured by NMR analyses and of cellular or experimental objectives that may include ATP generation, biomass or production of industrially and physiologically important metabolites. To overcome previous limitations in using NMR time series data for genome-scale predictions of cellular metabolism, we present approaches to transform integrated  13  C NMR trajectories into flux estimates to support dFBA simulations (Estimation of exchange fluxes for dFBA).
Nonspinning control experiments To evaluate the impact of spinning of the HRMAS NMR rotor on cellular morphology, metabolism and biomass, sealed HRMAS inserts were prepared as described in the section Strain culture conditions and incubated without spinning for the duration of runs that evaluated a paired insert subjected to HRMAS NMR. Single 1D and 1D 1 H and  13  C NMR spectra were acquired from the nonspinning insert after the end of the incubation period for comparison with the spun insert, as described in the section HRMAS NMR. Rotor contents were analyzed as described in the section Strain culture conditions (Supplementary Table  1 ). Analyses showed comparable metabolic profiles. By Gram stain, rotors maintained outside the NMR showed more advanced progression of cultures to a postsporulation state, as evidenced by the presence of spores and lysed mother cells (Extended Data Fig.
Identification of metabolite productions with 2D NMR 13 C-labeled metabolites produced from [U-  13  C]glucose, l-[U-  13  C]proline and l-[U-  13  C]leucine in live cells were identified through 2D NMR (Extended Data Fig. 6 ) according to reported  13  C and 1 H chemical shift values availed from Human Metabolome Database (HMDB, https:// hmdb. ca) and Biological Magnetic Resonance Bank (BMRB, https:// bmrb. io).
H and 13 C spectra analyses Individual free induction decay (. fid) files were processed using NMR-Pipe  42  , nmrglue  43  and custom Python scripts available in the GitHub repository. Fourier transformed spectra were normalized by the noise root-mean-square error of the sparse 130-160 ppm region (Supplementary Figs. 2 3 4 ). The normalized spectrum stack was rendered as a surface plot in MATLAB R2019b (MathWorks) with face lightness mapped to the log 2 of signal. Peaks with height ≥6 times the noise root-mean-square error (130-160 ppm) and separated from other peaks by 0. 08 ppm were classified as detectable signals and assigned to compounds using the following algorithm: (1) Subpeaks within 0. 3 ppm of each other were clustered. (2) Reference shifts for expected compounds within 0. 45 ppm of the cluster were associated with the cluster.
Assigned peak signals were concatenated into signal ridges, color-labeled by metabolite and superimposed over the stack as a scatter plot with stems to the xy plane and a smoothing spline curve fit. Surface regions within 0. 5 ppm of each reference peak were colorized. Assigned peaks in the  13  C spectra ≤100 ppm were curve fit using a Voigt (Gaussian-Lorentzian convolution) lineshape and integrated. Logistic curve fits of metabolite integral versus time were calculated in Python using a least-squares regression (SciPy 44 v. 1. 6. 2) according https://doi. org/10. 1038/s41589-023-01275-9 to equation (1) (Supplementary Table  2 ). The peaks assigned to the acetate and ethanol methyl hydrogens in the 1 H spectra from the [U-  13  C] glucose experiment were also curve fit to estimate the production of natural-abundance acetate.
Metabolic modeling A previously published genome-scale metabolic model of C. difficile strain 630, icdf834 (ref. 45 ), was modified using the COBRApy toolbox  46  and custom Python scripts. For metabolites not constrained by NMR trajectories, exchange reaction bounds were set to 3% of the millimolar concentration of media components used experimentally (Strain culture conditions). The cysteine uptake upper bound was set to 1,000 to enable uptake according to cellular demand. All other exchange reactions were blocked by setting their flux upper and lower bounds to zero. icdf843, an updated model included in the GitHub repository, added the changes noted in Supplementary Table 4 (refs. 5, 9, 20, 28, 35 ), as supported by experimental data, and supported biologically relevant processes energetically and thermodynamically.
Steady-state FBA solutions were calculated along a simulated 36-h timescale with a resolution of five solutions per hour. FVA solutions were computed along the same simulated timescale with an objective flux threshold of 99. 5%. Metabolic flux trajectories of selected reactions were visualized using custom MATLAB scripts (Fig. 3 ). Fluxes contributing to the production and consumption of cytosolic l-alanine, l-glutamate, ATP, pyruvate and ammonia were recorded at each timepoint (Supplementary Table  6 , columns C-AL).
C alanine For detection of  15  N in the  13  C spectrum, triplicate cultures of C. difficile ATCC 43255 ΔPaLoc were grown anaerobically in MMM containing [U-  13  C]glucose and either natural-abundance leucine or [ 15 N]leucine. Cultures were centrifuged 48 h after inoculation and supernatants were collected, filter-sterilized, lyophilized and reconstituted in D 2 O. NMR data measured with Bruker BioSpin (Bruker Corporation) were analyzed with NUTS, 2D Pro (Acorn NMR Inc. ). The. fid files were processed using 0. 2 Hz line-broadening and were zero-filled before Fourier transformation. After the Fourier transformation, resonance peaks were quantified using curve fitting with Lorentzian line shapes. Curve fitting was first performed globally to determine the average peak width across all fitted peaks.
Acknowledgements We thank  M. Delaney  for microbiology support,  M. Judge  and  A. Edison  for helpful comments and for confirming capacity of sealed HRMAS NMR rotors to maintain anaerobic conditions. This project was supported by the  National Institutes of Health  (L. B. , grant nos. R01AI153653 ,  R03AI174158  and  P30DK056338 ; L. L. C. , grant nos. S10OD023406 ,  R21CA243255  and  R01AG070257 ), the  Brigham and Women's Hospital Precision Medicine Institute  and  Presidential Scholar's Award  (L. B. ), the  MGH A. A. Martinos Center for Biomedical Imaging  (L. B. and L. L. C. ) and the  Massachusetts Life Sciences Center  (L. B. and L. L. C. ).
Data availability All NMR free induction decay files generated during the current study are available at the Metabolomics Workbench  47  as study ST002433 (https://doi. org/10. 21228/M88M5G). The updated C. difficile metabolic model icdf843 is available on GitHub at https://github. com/ Massachusetts-Host-Microbiome-Center/nmr-cdiff. The remaining data generated in this study are included in this article and its supplementary information files. Reference spectra used in this study to identify molecules in the NMR spectra were accessed from the HMDB (https://hmdb. ca/) and BMRB (https://bmrb.
Acknowledgements This research was funded by the  International Center Frontier Research in Chemistry (icFRC) , the  LabEx CSC  ( ANR-10-LABX-0026 CSC ) and the  French National Research Agency  ( ANR-21-CE07-0014 ). RW thanks the  LabEx CSC, Strasbourg , for a PhD fellowship and LG thanks the  ANR  for a PhD fellowship. We thank the EXPLOR mesocenter for providing access to their computing facility (project  2021CPMXX2483 ) and the  NMR service (B. Vincent)  of the LeBel Federation FR2010 for the easy access to the spectrometers and for advice. EA thanks  Dr Axel Gansmüller  for fruitful discussions.