dataesr/scientific-paragraphs-categorization huggingface dataset:
Acute liver injury (ALI) is a severe disorder resulting from excessive hepatocyte cell death, and frequently caused by acetaminophen intoxication. Clinical management of ALI progression is hampered by the dearth of blood biomarkers available. In this study, a bioinformatics workflow was developed to screen omics databases and identify potential biomarkers for hepatocyte cell death. Then, discovery proteomics was harnessed to select from among these candidates those that were specifically detected in the blood of acetaminophen-induced ALI patients. Among these candidates, the isoenzyme alcohol dehydrogenase 1B (ADH1B) was massively leaked into the blood. To evaluate ADH1B, we developed a targeted proteomics assay and quantified ADH1B in serum samples collected at different times from 17 patients admitted for acetaminophen-induced ALI.
In Silico Selection of Candidate Biomarkers of Hepatocyte Cell Death We first closely examined the biological and biochemical characteristics of the cell death biomarkers (also known as cytolysis biomarkers) currently used in medical biology. Most biomarkers are cytoplasmic proteins with a tissue-predominant expression profile and a molecular weight (MW) of less than 85 kDa. Using these features as selection criteria, we mined public databases to extract proteins that could constitute biomarker candidates for liver injury from the liver proteome. Candidate biomarkers for hepatocyte cell death were selected using tools from ProteoRE-a freely accessible Galaxy-based platform (https: //www. proteore. org). ProteoRE can be used to implement selection strategies to discover biomarkers of tissue leakage and cancer  [13, 16].
Next, using curated annotations from neXtProt  [18] , we filtered this list to only retain cytoplasmic proteins with no transmembrane domains and a MW of less than 85 kDa. The "diseases" information extracted from neXtProt was then used to filter out candidates annotated as being associated with diseases other than liver injury. This step helped to exclude non-specific candidates from the final list. Finally, as lack of detectability in biological fluids is a major challenge during biomarker development, we searched for indications that the candidates would be detectable in plasma using MS-based techniques. To do so, MS-based proteomics datasets downloaded from the PeptideAtlas database were mined to check whether these proteins had previously been detected by LC-MS/MS, and to glean information on the total number of peptide observations in plasma  [19]  (Figure  1a ).
For each peptide detected, sequence uniqueness was verified via BLAST search against the UniProt database. Among the four peptides detected, only one was found to be truly specific to ADH1B isoenzymes (peptide AAVLWEVK) (Supplementary Figure  S2 ). We also took genetic variation into account, and searched for non-synonymous single-nucleotide polymorphisms  [21]. Thankfully, the peptide AAVLWEVK was shared between the three variants of ADH1B associated with genetic polymorphisms-namely, ADH1B*1, ADH1B*2, and ADH1B*3and, consequently, can be used for patients from any population  [22]. To quantify ADH1B, we synthesized and used a PSAQ standard, which is a full-length isotopically labeled version of the target analyte. The choice of a PSAQ standard was essential to accurately quantify ADH1B, as sample prefractionation and proteolysis processes can lead to analyte losses  [15, 23].
Discussion Multiomics integration, trans-omics, and artificial intelligence methods hold considerable potential in medicine to explore massive omics data and deliver new biomarker candidates and drug targets  [29] [30] [31]. Along this line, recent studies have exploited proteomic maps to select tissue-specific proteins as biomarker candidates. In 2012, Prassas et al. [32]  combined data deposited in transcriptomics and proteomics databases to identify proteins with tissue-specific expression that could be relevant biomarkers secreted in cancers. In the field of hepatology, Qin et al. [33]  recently selected a panel of 66 human proteins based solely on their liver expression profiles. The levels of 23 of these proteins were demonstrated to increase in 14 serum samples from patients with acetaminophen overdose, but the potential clinical value of these proteins was not further explored.
In this study, we applied a similar initial hypothesis, and selected 13 proteins from omics databases as potential highly specific candidate biomarkers to monitor acetaminophen-induced ALI progression. Label-free quantitative MS-based proteomics was used to refine the selection and identify intracellular proteins specifically released into the blood as a result of hepatocyte necrosis and liver damage. Extensive leakage of ADH1B into the circulation was evidenced, motivating the further study of ADH1B serum concentrations in acetaminophen-induced ALI patients using targeted quantitative proteomics. In accordance with our hypothesis and the label-free proteomics results, longitudinal monitoring of patients with acetaminophen-induced liver injury confirmed extensive leakage of ADH1B into the circulation during the acute phase of the disease.
In this context, combinations of mechanistic biomarkers such as ADH1B, HMGB1, and functional biomarkers such as INR may improve the prediction of acetaminophen-induced ALI evolution, as well as patient stratification and management. Along this line, ADH4-which was also selected using our methodology (Table  1 )-could also be considered as a potential liver injury biomarker. In conclusion, in this study we established a link between large-scale omics data (proteomics, transcriptomics), bioinformatics, and MS-based proteomics to deliver a new biomarker candidate for hepatology and clinical toxicology. Specifically, a dedicated bioinformatics workflow was developed to extract mechanistic biomarker candidates from public "omics" databases. Then, discovery and targeted proteomics analyses were sequentially used to identify and quantify ADH1B in serum samples from patients with acetaminophen-induced ALI.
Bioinformatics Selection of Candidate Biomarkers The tools implemented and used in this study are part of the freely accessible Pro-teoRE platform (https://www. proteore. org)-a Galaxy-based platform dedicated to the functional analysis and exploration of biomedical-research-related proteomics data. The ProteoRE tools used in this study were the following: "Data manipulation and visualization" section: "ID Converter (Human, Mouse, Rat)", "Filter by keywords and/or numerical value", and "Venn diagram (JVenn)"; "Get features/annotation" section: "Build tissue-specific expression dataset (Human Protein Atlas)(no input required)", "Add expression data (RNAseq or Immuno-assays) (Human Protein Atlas)", "Get expression profiles by (normal or tumor) tissue/cell type (Human Protein Atlas)", "Add protein features (neXtProt)", and "Get MS/MS observations in tissue/fluid (Peptide Atlas)".
Human Blood Samples Patients with ALI/ALF were recruited at the Intensive Care Unit (ICU) of the Hepatobiliary Center at Paul Brousse Hospital (France, Villejuif). Blood samples were collected from 22 patients admitted for acetaminophen-induced ALI/ALF (n = 17) and nonacetaminophen-induced ALI/ALF (n = 5) (Supplementary Table  S4 ). Blood samples were analyzed immediately at the clinical chemistry laboratory to determine biological parameters of hepatic insufficiency, including alanine and aspartate aminotransferase (activated ALT and AST photometric assays, Abbott, Rungis, France), INR, and prothrombin time (STA-Neoplastine CI PLUS test, Ref. 00667, Diagnostica Stago, Asnières sur Seine, France). All patients tested negative for hepatitis viruses (HAV, HBV, HCV). Clinical data collected included age, gender, sampling time, and outcome on day 21 post-admission (Supplementary Table  S4 ).
1% formic acid) for 15 min. MS and MS/MS data were acquired using Xcalibur (Thermo Fisher Scientific). A data-dependent top-20 MS acquisition method was launched with the Q Exactive HF in positive mode. The nanospray voltage was set to 2 kV, the ion transfer capillary temperature to 270 • C, and the S-Lens RF amplitude to 55%. Survey full-scan MS spectra (m/z = 400-1600) were acquired in the Orbitrap at a resolution of 60,000 after the accumulation of 10 6 ions (maximum filling time: 200 ms). The 20 most intense ions (excluding z = 1 and unassigned charge states) from the survey scan were fragmented using HCD with a normalized collision energy of 30, a resolution of 15,000, and an AGC target of 10 5 ions (max injection time: 50 ms). Dynamic exclusion was set to 30 s, the intensity threshold to trigger MS/MS to 2 × 10 4 , and the default charge state to 2.
Discovery Proteomics Data Processing Peptides and proteins were identified using Mascot (version 2. 7. 0. 1) via concomitant searches against the UniProt database (Homo sapiens taxonomy, downloaded in May 2021) and a homemade database of frequently observed non-human contaminants. Trypsin/P was chosen as the enzyme, and two missed cleavages were allowed. Precursor and fragment mass error tolerances were set at 10 and 20 ppm, respectively. Peptide modifications allowed during the search were carbamidomethyl (C, fixed), acetyl (protein N-term, variable), and oxidation (M, variable). The Proline software  [40]  was then used to filter the results: conservation of rank 1 peptides; peptide length ≥ 6 amino acids; peptide score ≥ 25; ability to reach a false discovery rate of peptide-spectrum match identifications < 1%, as calculated based on peptide-spectrum match scores by employing the reverse database strategy.
Proline was then used to combine all validated PSMs into a single final mapping list of proteins. Only proteins with at least one specific peptide were kept. MS1 label-free quantification of the identified protein groups based on razor and specific peptides was finally performed with Proline. Statistical analyses were performed using ProStaR  [41]. Proteins identified in the reverse and contaminant databases, proteins identified by MS/MS in less than two replicates of one condition, and proteins for which fewer than 3 abundance values were available in a single condition were removed from the list. After log 2 transformation, abundance values were normalized by VSN (variance-stabilizing normalization) before imputing missing values (SLSA algorithm for partially observed values in the condition, and detQuantile algorithm for totally absent values in the condition); statistical testing was performed using a limma-moderated t-test.
Data Availability Statement: The LC-MS/MS data have been submitted to the ProteomeXchange Consortium via the PRIDE  [43]  partner repository under dataset identifier PXD028082 (Reviewer account details: Username: reviewer_pxd028082@ebi. ac. uk; Password: IfSnG15F). All LC-SRM raw data (. wiff files), Skyline files (. sky files), and calculations (. xlsx files) have been deposited in the PeptideAtlas SRM Experiment Library (PASSEL) under dataset identifier PASS01148 (password TX8572wv).
Blinded medication review by a trained clinical pharmacist During this case-control study, a sample of 30 prescriptions was daily randomly selected by a clinical pharmacist with access to the prescriptions' scores, with a ratio of one "low score" prescription (score < 2. 4) for three "high score" prescriptions (score ≥ 2. 4). One other trained clinical pharmacist, blinded to the prescriptions' scores, reviewed the 30 daily prescriptions, for a duration of 14 days. Medication review was performed in accordance with the French Society of Clinical Pharmacy (SFPC) guidelines  [14]. DxCare® medical software (Dedalus, le Plessis-Robinson, France) was used to access the patient's prescription, medical le, laboratory results and vital signs and to validate the prescription or alert the prescriber or nursing team of a medication error (pharmaceutical intervention).
MATERIAL AND METHODS The clinical trials included in this analysis were obtained using four online clinical trials registries (CTR): ClinicalTrials. gov, European Clinical Trials Registry (EU-CTR), International Standard Randomized Controlled Trial Number (ISRCTN) registry, and the Australian New Zealand Clinical Trials Registry (ANZCTR). These CTRs encompass 81. 0% of the registrations uploaded to the International Clinical Trials Registry Platform (ICTRP) from the World Health Organization (WHO), according to the information provided by others. 6 he inclusion criteria for trial registrations were: i) clinical trials with medicinal products ii) starting from 1/10/2004 to 30/09/2018 with iii) Portuguese Institutions as sponsor or with Portugal as participating country.
Search methodology of trials The search for the medicinal products' clinical trials was performed by two independent authors (MA and DM). In the Clinicaltrials. gov CTR, the three criteria (i -iii) were applied as described above in an advanced search. The EU-CTR only includes medicinal product trial studies, so only two criteria (ii -iii) were applied. In ISRCTN, it was only possible to consider criteria ii) and iii), and therefore observational studies were immediately discarded. In the ANZCTR registry, an advanced search was performed using the three aforementioned criteria needed (i -iii). Screening for duplicates was performed based on the trial secondary identification number (ID), sponsor name and title of the study, since one trial can be registered in one or more clinical trials registries.
This screening was performed independently by two authors (MA and DM). Both authors performed a manual identification of Industry and Investigator IT independently, because clinical trial registries do not have an unequivocal division of the trials in these two categories, with the exception of EU-CTR. This separation was based on the sponsor type. For example, if the sponsor was the pharmaceutical industry then it was considered an industry IT, or in case of a non-profit organization, it was considered an investigator IT. After this separation, the databases of each author were merged, and duplicate trials were immediately excluded. Trials that did not meet the inclusion criteria (e. g. observational trials, trials without medicinal products or trials that do not include Portugal as participating country or sponsor) were also excluded.
Information about clinical trials Variables associated with the ID number of the trial, secondary ID, sponsor name, status, start and end date, intervention type, if randomized, title of the study, therapeutic area, were extracted from each database. When a trial was published in more than one database and information was not in agreement between databases, a priority in descending order of the information was given to EU-CTR, Clinical-Trials. gov, then ISRCTN and ANZCTR. The final database with the registrations analyzed in this study is presented as Appendix 1 (Appendix 1: https://www. actamedicaportuguesa. com/revista/index. php/amp/article/view/14554/Appen-dix_01. pdf).
Information about published papers Information about the date of publication, Portuguese authorship, participating centers, journal name, DOI (digital object identifier), and time from the end of the trial to the publication of results was collected. When the information collected from the clinical trials database was in disagreement with the information provided in the publication, the latter was considered as the correct one. Journal impact factors from the year of the publication were obtained at Web of Science (www. webofknowledge. com). In the publications with Portuguese authorship, information about the purpose (such as treatment with a new product, dose or regimen, prophylaxis, biomarkers evaluation, etc) and the target population was extracted and a screening for compliance with the CONSORT 2010 checklistwas carried out independently by two authors (MA and CM).
Statistical analysis The analysis performed was a descriptive statistical analysis performed using Excel controls. The presented values represent sums, percentage, means, median and relative and absolute frequencies. For the statistical analysis, data results are presented as mean ± standard error or medians and were analyzed with IBM Statistical Package of Social Science version 25. 0 (SPSS). Significance analyses were performed for p values < 0. 05 using the Mann-Whitney-U test to evaluate the significance of the difference between two groups of independent samples. None of the samples show a normal distribution, as confirmed by Shapiro Wilk's test. Kaplan-Meier survival plots were used to show the time to publication. The median time to publication and 95% confidence intervals (CI) were calculated.
RESULTS A total of 2672/2679 registrations were found in the four CTR (ClinicalTrials. gov, EU-CTR, ISRCTN and ANZCTR) by two authors (MA and DM) -Fig. 1. As the same study can be registered in different CTR, several duplicates were found and excluded by each author (909/891) based on the secondary ID, sponsor name and title of the study. Therefore, each of the authors considered at this step 1763/1788 studies, dividing those as industry and investigator-initiated trials.
Identification of clinical trials After merging the datasheets prepared by both authors, discrepancies were identified. Non-interventional studies, trials without medicinal products as the intervention or those with no recruiting sites in Portugal were discarded. A total of 1485 trials from the initiative of the industry and of the investigators were considered eligible from all the screened databases (Fig. 1 ). From those, 1345 (91. 0%) are industry IT and 140 (10. 0%) were investigator IT (Table  1 ). For the screening of publications, only completed trials were considered, identifying a total of 288 publications, 271 (28. 9%) publications of industry IT and 17 (27. 9%) investigator IT (Table  1 ). Notably, a considerable higher percentage of investigator IT (47. 1%) has Portuguese clinical investigators as authors when compared with those from the industry (8.
Acknowledgements We gratefully acknowledge the  Frontier Research in Chemistry Foundation (FRC) , the  Université de Strasbourg  (grant  FLE-FRC-0002-0035 , SuperFlOx), the  Université de Poitiers , and the  CNRS  for nancial support. The authors also acknowledge nancial support from the  European Union (ERDF)  and the  Région Nouvelle Aquitaine  ( SUPERDIV project-HABISAN program ). The authors thank the  French uorine network (GIS-FLUOR ).
Acknowledgements This work was supported by a grant from the  Swiss National Science Foundation  (Grant  200021E_175680/1 ) and the  Agence Nationale de la Recherche (ANR)  with  PRCI  funding ( ANR-17-CE07-0048-01 ). J. K. would like to thank  Sebastian Küng  for help with preparing starting materials. Dr. N. Trapp  and  Michael Solar  are thanked for their support with  X-Ray Crystallography.
Phosphatidylglycerols represent a large share of the lipids in the plasmamembrane of procaryotes. Therefore, this study investigates the role of charged lipids in the plasma membrane with respect to the interaction of the antiviral saponin glycyrrhizin with such membranes. Glycyrrhizin is a natural triterpenic-based surfactant found in licorice. Vesicles made of 1,2-dioleoyl-sn-glycero-3-phospho-rac-(1'-glycerol) (DOPG)/glycyrrhizin are characterized by small-angle scattering with neutrons and X-rays (SANS and SAXS). Small-angle scattering data are first evaluated by the modelindependent modified Kratky-Porod method and afterwards fitted by a model describing the shape of small unilamellar vesicles (SUV) with an internal head-tail contrast. Complete miscibility of DOPG and glycyrrhizin was revealed even at a ratio of lipid:saponin of 1:1.
Wide-angle X-ray scattering (WAXS) resolves the glycyrrhizin content dependent acyl-chain correlation distance. Small-angle scattering with neutrons and X-rays (SANS/SAXS) is used to determine the overall size, membrane thickness, and membrane contrast profile. The evaluation of the data is performed by model-independent as well as model-dependent analyses. The overarching aim of this study is to gain a deeper understanding of the interaction of glycyrrhizin with model membranes mimicking procaryotic cells.
A. Fischione Instruments, Pittsburgh, PA, USA). The microscope was operated at an acceleration voltage of 200 kV and the images were recorded digitally by a bottom mounted camera (Gatan OneView, Gatan, Pleasanton, CA, USA). The images were afterwards processed with a digital imaging processing program (Digital Micrograph ® , Version 3. 21, GMS 3, Gatan, Pleasanton, CA, USA).
From the position q max,MKP the membrane thickness d m,MKP can be calculated using the relation q max,MKP •d m,MKP, SAXS = 2π in the case of SAXS  [55]  and q max,MKP •d m,MKP, SANS = π in the case of SANS  [57]. Additionally, structural parameters of the SUVs are derived from model-based fitting with a core multishell sphere (CMS) describing a hollow sphere with head-to-tail contrast in the sphere-building lipid membrane  [58]. The CMS model is implemented in the program SASView  [59]  and the SUV size expressed as the core radius R c , its polydispersity σ R c , the membrane thickness d m , and the corresponding polydispersity σ d m are determined. A more precise description of the model can be found in Section 3. 3. 2.
Small-Angle Neutron Scattering (SANS) SANS experiments were performed with samples prepared in D 2 O buffer. Measurements were performed using the D22 instrument at the Institut Laue-Langevin (ILL) in Grenoble (France). The samples were filled into 2 mm quartz cuvettes (Hellma Analytics, Müllheim, Germany) and measured in a 15-position sample holder at a temperature of 30 • C. A q-range from 1. 7 × 10 -3 Å -1 to 0. 4 Å -1 was covered using a neutron wavelength of 6 Å for the sample to detect distances of 2. 8 m and 17. 6 m, and a neutron wavelength of 12 Å at 17. 6 m. The wavelength resolution was ∆λ/λ =10 %. Initial treatment of the 2D data was carried out with the software GRASP provided by the ILL  [60]. The data were reduced with respect to empty cell, background, transmission and direct beam measurement to finally obtain the circularly averaged absolute intensity.
Small-Angle X-ray Scattering (SAXS) DOPG samples prepared in D 2 O as well as in H 2 O buffer were measured on an inhouse SAXS/WAXS system (XEUSS, Xenocs, Sassenage, France) equipped with a CuK α source (λ = 1. 541 Å, GeniX Ultra low divergence, Xenocs) and a Pilatus 300K hybrid pixel detector (Dectris, Baden Deattwil, Switzerland). The samples were measured to detect distances of 2. 7 m and 0. 8 m convering a q-range from 6•10 -3 Å -1 to 0. 4 Å -1. The 2D data were analyzed using the Foxtrot software (V3. 3. 4)  [61]. The samples were measured in a flow-through Kapton capillary (1 mm, GoodFellow GmbH, Bad Nauheim, Germany) positioned in a Linkam stage (Linkam Scientific, Tadworth, UK) at a temperature of 30 • C. The scattering of the sample was normalized with respect to incident intensity, sample thickness, acquisition time, transmission and background.
Wide-Angle X-ray Scattering (WAXS) Wide-angle X-ray scattering was used to determine the chain-chain correlation distance d WAXS in the lipid bilayers. The correlation signal observed in WAXS occuring at the position q WAXS is directly related to d WAXS via d WAXS = 2π/q WAXS  [64]. WAXS measurements for samples prepared in D 2 O as well as in H 2 O buffer were also performed on the XEUSS SAXS/WAXS setup (see Section 2. 3. 2 for experimental details and data reduction). All measurements were performed at a temperature of 30 • C at a sample to detect distance of 0. 16 m, covering a q-range from 0. 5 Å -1 to 2 Å -1.