dataesr/scientific-paragraphs-categorization huggingface dataset:
For each biological sample, 5 µL corresponding to 25% of digested sample were injected in duplicate on the system. After pre-concentration and washing of the sample on a Acclaim PepMap 100 column (C18, 2 cm × 100 μm i. d. 100 Å pore size, 5 μm particle size), peptides were separated on a LC EASY-Spray column (C18, 50 cm × 75 μm i. d. , 100 Å, 2 µm, 100 Å particle size) at a flow rate of 300 nL/min with a two-step linear gradient (2-22% acetonitrile/H20; 0. 1% formic acid for 100 min and 22-32% acetonitrile/H20; 0. 1% formic acid for 20 min). For peptide ionization in the EASY-Spray source, spray voltage was set at 1. 9 kV and the capillary temperature at 250 °C. All samples were measured in a data dependent acquisition mode. Each run was preceded by a blank MS run in order to monitor system background.
Protein identification and quantification. Relative intensity-based label-free quantification (LFQ) was processed using the MaxLFQ algorithm  [42]  from the freely available MaxQuant computational proteomics platform, version 1. 6. 3. 4  [43]. Analysis was done on two biological replicates, each injected two times on mass spectrometers. The acquired raw LC Orbitrap MS data were first processed using the integrated Andromeda search engine  [44]. Spectra were searched against the Mycobacterium Tuberculosis database extracted from UniProt (date 2021-03-10; 3,993 entries)  [45]. The false discovery rate (FDR) at the peptide and protein levels were set to 1% and determined by searching a reverse database. For protein grouping, all proteins that could not be distinguished based on their identified peptides were assembled into a single entry according to the MaxQuant rules.
The statistical analysis was done with Perseus program (version 1. 6. 15) from the MaxQuant environment (www. maxquant. org). Quantifiable proteins were defined as those detected in above 3 of 4 samples in one condition or more. Protein LFQ normalized intensities were base 2 logarithmized to obtain a normal distribution. Missing values were replaced using data imputation by randomly selecting from a normal distribution centred on the lower edge of the intensity values that simulates signals of low abundant proteins using default parameters (a downshift of 1. 8 standard deviation and a width of 0. 3 of the original distribution). To determine whether a given detected protein was specifically differential, a two-sample t-test was done using permutation-based FDR-controlled at 1% and employing 250 permutations.
To obtain the structure of HsaD in complex with the CyC inhibitors, the protein at 7. 5 mg/mL was mixed either with each of the CyC7β, CyC8β and CyC17 inhibitors dissolved in Transcutol® HP (Gattefossé SAS) at a molar excess xI = 100 and incubated at room temperature for 2 h, or with CyC31 dissolved in DMSO at a molar excess xI = 2. 5 and incubated at room temperature for 24 h. Crystallization of HsaD in complex with the CyC inhibitors was performed as outlined above for the native form of HsaD. Crystals of native HsaD, and HsaD in complex with the CyC inhibitors were cryo-protected with reservoir solution supplemented with 30% (v/v) glycerol prior flash-freezing in liquid nitrogen. X-ray diffraction data were acquired at beam lines Proxima1 and Proxima2a at the Synchrotron Soleil, Gif-sur-Yvette.
Diffraction data were processed with XDS  [52]  and scaled and merged using the CCP4 suite  [53, 54]  of programs Pointless  [55] , AimLess  [56]  and Truncate  [57]. The structure of native HsaD was solved by molecular replacement with the program Phaser  [58]  using the structural coordinates of the same protein (PDB: 2VF2 -2. 35 Å resolution  [35] , 100% sequence identity) as the search model. The complex HsaD-CyC structures were solved by molecular replacement using the coordinates of native HsaD as the search model. Refinement and model adjustment were carried out with the programs Refmac  [59]  and Coot  [60] , respectively. Random sets of ~5% of reflections, depending on the resolution limit, were set-aside for cross-validation purposes. The composition of cross-validation data sets was systematically taken over from the parent data set, except for the complex HsaD-CyC7β, which belongs to a different space group.
The covalent link between the CyC ligands and the catalytic serine S 114 , and stereochemical restraints for the inhibitors, were generated by JLigand  [61]. Model quality was assessed with internal modules of Coot  [60]  and with the Molprobity server  [62]. Ligands were fitted into unbiased Fo-Fc difference electron density maps calculated after 5-7 cycles of restrained refinement with Refmac  [59]. Figures representing structural renderings were generated with the PyMOL Molecular Graphics System (Version 2, Schrödinger, LLC).
In silico molecular docking experiments. Autodock Vina  [36]  was used as previously reported  [16, 20, 63]  to generate the putative binding modes of the various CyC inhibitors into the active site of HsaD. The PyMOL Molecular Graphics System (version 1. 4, Schrödinger, LLC) was used as the working environment with an in-house version of the AutoDock/Vina PyMOL plugin  [63]. The X-ray crystallographic structure of HsaD in complex with the CyC31 inhibitor (PDB: 7ZM4) was used as the receptor. Docking runs were performed after removing the bound CyC31 from the enzyme active site, and replacing the catalytic Ser 114 with a glycine residue to enable the ligand (i. e. , the inhibitor) to adopt a suitable position corresponding to the pre-bound intermediate before the nucleophilic attack in the enzyme active site.
The box size used was chosen to fit the whole enzyme's active site cleft and allowed non-constructive binding positions. The three-dimensional structures of the CyC compounds were constructed using Chem3D Ultra 11. 0 software, and their geometry was refined using the Avogadro open-source molecular builder and visualization tool (version 1. 2. 0. http://avogadro. cc/) [64]. and CyC32yne, respectively, according to the functional classification system of the Mycobrowser database  [65]. The numbers correspond to the total number of identified proteins in each category.
ACKNOWLEDGEMENTS This work was supported by the  CNRS ,  Aix Marseille University , and the  Agence Nationale de la Recherche  (grant  N°ANR-19-CE44-0011 ) and by the  French Infrastructure for Integrated Structural Biology (FRISBI)  (grant  N°ANR-10-INSB-05-01 ). Proteomics analyses were done using the mass spectrometry facility of  Marseille Proteomics  (marseille-proteomique. univ-amu. fr)supported by  IBISA , the  Cancéropôle PACA , the  Provence-Alpes-Côte d'Azur Région , the  Institut Paoli-Calmettes , and  Fonds Europeen de Développement Régional (FEDER). We thank  Synchrotron Soleil  for beamtime allocation and beam line staff for assistance with data collection. The synthetic substrate HOPDA was kindly provided to us by  Prof. Edith Sim  ( Oxford University ) and  Dr Ali Ryan  ( Northumbria University ).
Study design, settings, and participants We performed a descriptive, cross-sectional study using information from the emergency department admissions of Bordeaux University Hospital. The analysis considered data for all patients aged 75 years and over admitted to the emergency department between September 1, 2016, and August 31, 2017, and for whom at least one "usual treatment" questionnaire could have been completed. If a patient was admitted for different stays over the period, data for each emergency hospitalization was considered. This information was obtained from the department electronic medical records, which include, in addition to all clinical data, a dedicated geriatric investigation for all patients aged 75 years and over. The latter registers all treatments the patient was taking before being admitted to the emergency department.
Identification of contraindicated or warned against concomitant uses of interacting drugs (potential DDIs) Identification of contraindicated or warned against concomitant uses of interacting drugs was performed using a three-step method. These are hereafter referred to as contraindicated or warned against concomitant uses. First, we extracted the free text from the drug questionnaire. Second, we identified all drug international non-proprietary names (INNs) mentioned in these free texts using an automated detection method developed and validated in a previous study. The tool has a positive predictive value (PPV) of 99. 0% and a sensitivity of 96. 0% for such INN identification from free text. 14 Third, within the drug INN data obtained from these processed free texts, we identified all contraindicated or warned against concomitant uses of interacting drugs using a second tool developed with the Synapse Medicine start-up.
Data analysis Demographic characteristics of the population were described regarding the first admission for each patient and the number of drug distributions were described for all admissions. Quantitative variables were described in terms of median and interquartile range (IQR), and qualitative variables were described as prevalence and frequency. The most frequent therapeutic groups in the "usual treatments" questionnaires of the population were presented by Anatomical Therapeutical Chemical (ATC) classification therapeutic level three. 16 The prevalence of contraindicated or warned against concomitant uses over the study period was expressed as an absolute value and their overall frequency among emergency admissions was expressed as percentage with its 95% CI using a standard normal distribution.
Data and Statistical Analysis Design and analysis were done in accordance with the recommendations and requirements on experimental design and analysis in pharmacology published by  Curtis et al. , in 2018 (Curtis et al. , 2018). Statistical analysis was performed using GraphPad Prism 6 Software (RRID: SCR_002798) only for experiments where group size was at least n=5 and composed of independent values. To compare the groups, two-way ANOVA was applied and followed by Tukey's multiple comparisons test only if F was significant and there was no variance homogeneity. Normality and variance homogeneity were checked with the D'Agostino & Pearson omnibus normality test and the Levene's test, respectively. If assumptions were violated, a non-parametric approach, the Aligned-Rank Transform (ART) ANOVA, was used  (Wobbrock, Findlater, Gergle, & Higgins, 2011).
ACKNOWLEDGMENTS AND GRANT SPONSOR - This work was funded by  CNRS, University of Strasbourg (Unistra)  and  French Ministère Délégué à la Recherche et à l'Enseignement Supérieur  (PhD fellowship to F. G. , and V. H. ). We thank the following research programs of excellence for their support:  FHU Neurogenycs ,  French National Research Agency (ANR)  through the  Programme d'Investissement d'Avenir  (contract  ANR-17-EURE-0022 , EURIDOL graduate school of pain).
Ketamine and dextromethorphan are widely abused psychoactive substances. Inhibition of N-methyl-d-aspartate receptors (NMDARs) results in neurobehavioural effects including hallucinations, "out of body" sensations and dissociative effects. However, little is known about a possible extended addictive class effect linked to pharmacologically-related amino-adamantane derivatives (e. g. , amantadine and memantine). Using a quasi-Bayesian analytic method, we investigated the potential association between the use of approved NMDAR antagonists (i. e. , dextromethorphan, ketamine, amantadine and memantine) and the reporting of drug abuse and dependence in the WHO pharmacovigilance database (VigiBase ® ), which includes >21 million individual case safety reports collected from >130 countries. This disproportionality analysis identified a significant association for all investigated drugs: dextromethorphan (IC = 3.
5 Moreover, visual hallucinations and agitation due to memantine in individuals with Alzheimer's disease have been reported,  6  as well as psychostimulant effects in healthy volunteers with previous stimulant use 7 and recreational use. 8 To our knowledge, there is no such report of diversion with amantadine, even though this drug is commonly used as a CNS stimulant for the treatment of fatigue associated with multiple sclerosis. 9  order to identify a possible class effect, we performed a disproportionality analysis in VigiBase ® , the WHO pharmacovigilance database. The post-marketing phase is crucial to monitor drug safety and gain insight into the risk-benefit profile, as it reflects real-life use.
e. , the full database), this suggests a potential safety signal called "signal of disproportionate reporting". NMDAR antagonists included were dextromethorphan (pIC 50 6. 3), 10 ketamine (pIC 50 6. 2), 10 amantadine (pIC 50 4. 7)  10  and memantine (pIC 50 6. 3),  10  first approved in 1953, 1970, 1966 and   2002, respectively. Drugs with only secondary effects on NMDARs (e. g. , methadone, carbamazepine, valproic acid and phenytoin) were excluded from the present study, as well as illegal drugs (e. g. , phencyclidine). We extracted all ICSRs associated with the four drugs of interest from VigiBase ® , considered either as suspect or con-   1 ). Even if drug abuse and dependence are reported at a disproportionately higher level with dextromethorphan and ketamine, a significant association is also clear for amantadine and memantine, thus favouring a class effect.
What this study adds • A significant disproportionality signal for drug abuse and dependence was found for all investigated NMDAR antagonists, including amantadine and memantine, suggesting a class effect. • The risks and benefits of long-term treatment with NMDAR antagonists need to be carefully assessed, especially due to their growing interest for use as antinociceptives in the current opioid epidemic context. Our analysis in the context of real-life data highlights a signal of disproportionate reporting of drug abuse and dependence for ketamine and dextromethorphan, but also for amantadine and memantine, suggesting a possible class effect for all NMDAR antagonist drugs. The BCPNN model, a quasi-Bayesian approach, has demonstrated robustness in identifying disproportionality signals from the data recorded in pharmacovigilance databases.
11 In addition, a deduplicated dataset was used to minimize information bias. However, several limitations should be acknowledged. The results obtained from disproportionality analysis depend upon the drug-related adverse events recorded in pharmacovigilance databases and are limited by underreporting. In our study, reporting is also influenced by a notoriety bias: abuse/dependence is more likely to be considered as being related to ketamine or dextromethorphan than to the other NMDAR antagonists. Moreover, disproportionality analysis is a hypothesis-generating approach that does not allow risk quantification. Thus, the weak signals observed for the amino-adamantane derivatives, amantadine and memantine, should not be considered as alarms but are intended to stimulate active vigilance and further research to establish actual event rates and identify risk factors that might lead to proper patient management.
Development of the Optimized Rhombic Designs The rhombic checkerboard designs were developed using the R software (version 3. 6. 2)  [10]. Minimizations were performed using 'optim' from the R package stats (version 3. 6. 3) [10]. For design development, 1000 parameter sets of two random drugs A and B were simulated to mimic typical antibacterial drugs (Fig. 1 ). Additionally, to simulate different types of drug interactions for each of those drug pairs three sets of interaction parameters describing a synergistic, antagonistic and asymmetric interaction were sampled and applied as interactions on EC50 and Emax. The drugs Emax values (log10 CFU/mL) were sampled between 5 and 10, the EC50 values (µg/mL) between 2 and 3 and H between 1 and 3. The INT parameters were sampled between -0. 9 and 4.
Design Evaluation in Stochastic Simulation and Estimation The optimized designs were subsequently evaluated in stochastic simulation and estimation (SSE) studies. The SSE were performed with the R software (version 3. 6. 3) [10]. Differential equations were solved using the 'deSolve' package (version 1. 28)  [14]. To improve performance, differential equations were encoded in C, compiled as shared objects (. so) and linked to the 'deSolve' interface. Estimation of parameters was performed using 'optim' from the R package stats (version 3. 6. 3) [10]. For the SSE, a realistic time-kill experiment mimicking scenario was chosen: 1000 hypothetical drug combinations were randomly sampled and the effect on colony forming units (CFU) in an ordinary differential equation system (Eq. (  11 )) with simultaneous growth with a first order growth rate k G and killing effect rate E was simulated.
The aim of this study was to develop and to validate a toxicological untargeted screening relying on LC-HRMS in meconium including the detection of the four main classes of drugs of abuse (DoA; amphetamines, cannabinoids, opioids and cocaine). The method was then applied to 29 real samples. Analyses were performed with a liquid chromatography system coupled to a benchtop Orbitrap operating in a data-dependent analysis. The sample amount was 300 mg of meconium extracted twice by solid phase extraction following two distinct procedures. Raw data were processed using the Compound Discoverer 3. 2 software (Thermo). The method was evaluated and validated on 15 compounds (6-MAM, morphine, buprenorphine, norbuprenorphine, methadone, EDDP, amphetamine, MDA, MDMA, methamphetamine, cocaine, benzoylecgonine, THC, 11-OH-THC, THC-COOH).
Very few methods devoted to large screening of meconium have been published. The first broad-spectrum drug screening of meconium was developed by  Ristimaa et al. in 2010 [17]. This method relies on liquid chromatography coupled to high resolution mass spectrometry (LC-HRMS) and an in-house database containing 869 molecular drugs. More recently (2021), López-Rabuñal et al. proposed another large screening devoted to new psychoactive substances (NPS)  [21]. This method allows the simultaneous determination of 137 NPS in meconium by LC-HRMS. These two methods are efficient and innovative; however, they are targeted. To our knowledge, there is no untargeted LC-HRMS method devoted to toxicological screening in meconium, and none allowing the simultaneous detection of the four main classes of DoA (amphetamines, cannabinoids, opioids and cocaine) in the current literature.
Data Reprocessing Data were processed using the Compound Discoverer 3. 2 (Thermo, Les Ulis, France) software following a specific workflow. All ions presenting a signal over 3 times the background noise and a peak intensity over 500,000 were taken into account to create the extracted ion chromatogram (EIC). MS and MS2 spectra were then used to identify ions. Three processes were used for compound identification: (1) MassList: this process includes an in-house library containing 150 molecules. Identification is carried out using exact mass, isotopic profile and retention time. (2) MzCloud: mzCloud™ is an online library containing 19,521 molecules with MS and MS2 spectra  [22]. The mzCloud™ database contains 17 compound classes, screening was performed including all classes. Identification was performed using the HighChem HighRes algorithm.
Most of the analytical methods described in the literature for the determination of DoA in meconium are based on LC-MS/MS  [10] [11] [12] [13] [14] [15] [16] 18]. All these methods are targeted, therefore limiting the number of compounds analyzed. LC-HRMS allows the inclusion of hundreds of compounds in the database without compromising on sensitivity. In addition, retrieval of new compounds in the acquisition data is easy because the formula database is updatable with literature data for current substances, such as designer drugs. These results highlight the interest of this untargeted analytical method in the context of high-risk pregnancies. The application to samples collected in the framework of the care of newborns has made it possible to highlight most of the molecules of interest. This made it possible to document in a precise and almost exhaustive manner the exposure of newborns in utero, to adapt medical monitoring and care at birth and in particular to adjust treatment in the event of a withdrawal syndrome.
The plasma trough concentrations on day 10 for itraconazole (inhibition cohort) and rifampicin (induction cohort) were measured to ensure adequate exposure for inhibitor or inducer of CYP3A. The concentrations obtained in this study were comparable to steady-state itraconazole and rifampicin concentrations reported in the literature following 200 mg/day itraconazole or 600 mg/day rifampicin administration (Novartis data on file). 30 oadministration of itraconazole increased the AUC inf of capmatinib by 42% with no significant change in C max , compared to capmatinib administered alone. These results suggest that coadministration of CYP3A inhibitors may increase capmatinib exposure in patients and subsequently lead to increased incidence and severity of adverse reactions. Accordingly, we advise caution and close monitoring of patients who are on concomitant treatment with CYP3A inhibitors and capmatinib.
A transient reversible increase in creatinine has been observed following a single dose of capmatinib in healthy subjects and after repeat dosing in patients (Novartis data on file). Renal transporters are responsible for 10%-40% of creatinine excretion in addition to glomerular filtration. 18 Lepist et al  31  have shown that inhibitors for organic anion transporters and MATEs could impact the active secretion of creatinine in renal tubule without affecting glomerular filtration. Additionally, there are several drugs and investigational new chemical entities that inhibit renal transporters and therefore increase serum creatinine without change in the estimated glomerular filtration rate or other renal biomarkers. 32, 33 Serum creatinine has been proposed as an endogenous substrate to evaluate the renal transporter-related drug interaction.
In vitro data indicated that capmatinib is an inhibitor of renal transporters MATE1 and MATE2K with a K i value of 0. 28 μM and 0. 29 μM, respectively. Based on the steadystate C max of 11. 6 μM (0. 464 μM unbound) at a therapeutic dose of 400 mg twice daily, capmatinib is likely to inhibit MATE 1 and MATE2K in humans. With creatinine being an endogenous substrate for renal transporters, it is likely that the transient increase in serum creatinine by capmatinib could be due to the reversible inhibition of MATE transporters. The renal biomarker cystatin C was selected in this study to compare with creatinine because it is solely cleared through glomerular filtration and is considered a better marker for risk determination based on renal function. 34 No change in the cystatin C concentration following capmatinib administration confirmed that renal function measured by glomerular filtration has not been impacted by capmatinib.
Data Availability Statement Novartis is committed to sharing with qualified external researchers, access to patient-level data and supporting clinical documents from eligible studies. These requests are reviewed and approved by an independent review panel on the basis of scientific merit. All data provided are anonymized to respect the privacy of patients who have participated in the trial in line with applicable laws and regulations. This trial data availability is according to the criteria and process described at www. clinicalstudydatarequest. com.