dataesr/scientific-paragraphs-categorization huggingface dataset:
After washes with blocking buffer, the secondary antibody (anti-rabbit Alexa Fluor 555, #4413, Cell Signaling) was added for 3 h at room temperature followed by nuclei staining with DAPI (#4083, Cell Signaling). Spheroids were transferred into μ-Slide 8 Well (Ibidi, CliniSciences, Nanterre, France), and images were acquired on a Dragonfly spinning disk confocal microscope (Andor, Oxford Instruments, High Wycombe, UK) equipped with EMCCD iXon888 Life Andor camera (Objective 20X/0. 75 NA) and controlled by Fusion software (Andor). Fluorescence intensity was quantified using ImageJ software. Values were obtained from z-stack projections (sum slices) and correspond to the median values of the pixels in the images after background subtraction. 3D views were performed with Imaris software (Bitplane).
RNA was eluted in water and immediately stored at -80 °C until use. The full procedure was performed using QIAcube automated workstation (QIAcube-QIAGEN) to optimize reproducibility of RNA extraction. RNA integrity was assessed using Agilent 2200 TapeStation with RNA ScreenTapes. RINe (RNA Integrity Number equivalent) scores were > 7. 7 for all samples. RNA-seq libraries were prepared following the protocol TruSeq Stranded Total RNA and validated on labchip GX platform. Human GRCh38. p13 genome was used as a reference. RNA-Seq data were mapped and annotated using Ensembl database release 108 (https:// www. ensem bl. org).
4C ). Our data suggest that donor-dependent educated spheroids might be capable of predicting iDILI risk. Next, we assessed the reliability of our educated spheroid system to predict DILI risk. For that, we performed 2 independent experiments including 10 donors in the first cohort and 23 donors in the second cohort. Educated spheroids were treated with sorafenib, and we calculated the TS for each donor. As expected, we found that all donors from both cohorts were DILI positive upon exposure to sorafenib demonstrating that our results are consistent between 2 independent experiments (Fig. 4D ). The performance of educated spheroids to predict DILI risk was assessed by comparing our results to those obtained from other in vitro and in vivo models. We found that educated spheroids correctly predicted clinical DILI in 9 drugs out of 9 and did not falsely mark albuterol, flavoxate, and lenvatinib as toxic, yielding a sensitivity and a specificity of 100% (Fig.
Acknowledgements Microscopy experiments were performed at the  MRI imaging facility , member of the  France-BioImaging  national infrastructure supported by the  French National Research Agency  ( ANR-10-INSB-04 , " Investments for the future "). We thank  B. Delaval  for the antibodies and for the reagents. The authors thank  A. Boulahtouf  and  Dr. C. Gongora  for the reagents and for fruitful discussions.
For mouse skin videocapillaroscopy, mice were anesthetized with a ketamine-xylazine cocktail (100 mg/kg and 10 mg/kg, respectively) to reduce mobility and slow respiration for more stable imaging. Injection sites and adjacent areas were delicately ointed with JCI Insight 2023;8(4):e151846 https://doi. org/10. 1172/jci. insight. 151846 an immersion oil, and microvessels were visualized using a clinical nailfold videocapillaroscope Xport Technology capXview. Images were acquired with the software capXview HD2 version 5. 0. Microvessel numbers were determined by manual counting from images of injection sites and adjacent areas using ImageJ v. 1. 8. 0_191. Rosacea microvessels were visualized with a FotoFinder medicam 800 HD. Statistics. Two-tailed unpaired Student's t test or 1-way ANOVA with Dunnett's multiple comparisons were used as appropriate, unless otherwise stated in the figure legend.
Statistical analyses were performed with GraphPad Software (version 8. 2. 1). Significance threshold was set to P < 0. 05. Study approval Studies with human biopsies were approved by the local IRB of Lausanne, Switzerland (ethical approval no. 265/12) and Dusseldorf, Germany (ethical approval no. 2048). Animal experiments were performed according to institutional guidelines and the Swiss Federal Animal Protection Act and cantonal laws on animal protection. Consent was received from the Swiss Federal Food Safety and Veterinary Office (ethical approval no. VD3299). Buffy coats from healthy donors were obtained from the local transfusion center and blood bank, with ethical approval from cantonal authorities.
Acknowledgments We appreciate the technical assistance of  Isabelle Surbeck  and  Ana Joncic  at the department of dermatology. We thank  Stefanie Hendrikx  and  Borja Luri Prat  for invaluable discussions and advice on ECs and the process of angiogenesis. We appreciate technical assistance by chief nurse  Sarah Grognuz , at the department of angiology. We sincerely thank all patients involved in the study. Funding was provided by the  Swiss National Science Foundation  ( FN 310030-156173 ) and  National Rosacea Society  to CC,  Gottfried & Julia Bangerter-Rhyner  foundation to CC and AM, and a grant from  Spirig Pharma SA Foundation  to AM. Address correspondence to:  Curdin Conrad ,  Department of Dermatology, University Hospital CHUV, 1011 Lausanne, Switzerland. Email: curdin. conrad@chuv.
Introduction The aging of the population, the onset of many chronic diseases, and the resistance to treatment often necessitate the use of multiple medications. Drug combinations can be beneficial and interesting, but they can also lead to problematic situations for the patient. Indeed, if we take into account the different interactions, we can take advantage of the synergistic effects between several molecules to propose an effective treatment, on the other hand, if we do not take into account all the interactions, this can lead the patient to dangerous or fatal iatrogenic accidents. To take into account drug interactions, clinicians usually rely on manually structured databases based on data from the literature. However, the absence of an interaction in the databases does not mean that it does not really exist.
Formal model proposal To define and create the model proposed in this work, we relied on the recommendations of the minimal clinically relevant information to be filled in for each interaction in clinical practice  [5] , the model of interactions generally found in databases on drugs (Drugbank for example), as well as the output formats of some machine learning models that predict the type of interactions  [3, 4]. It was also deemed necessary to distinguish between pharmacodynamic (PD) interactions that have consequences on the effects of the drug on the body and pharmacokinetic (PK) interactions that have consequences on the effect of the body on the drugs.
Proof of concept To show the feasibility and usefulness of our model and strategy, we have taken up the work of Yan et al  [4]  who developed a model and a database of interaction types obtained by machine learning techniques. This data is freely accessible on github  3. The predicted interaction types are in unstructured text format. It contains information on the action of the interaction (increase or decrease) but no information about the "victim drug" and the "perpetrator drug". There is also no distinction between PD & PK interactions. We developed a program that reclassifies the interactions present in this database in the proposed data format. We then manually validated the automatic classification. Finally, we applied the strategy described above to generate two tables summarizing all the interactions present in a set of arbitrarily chosen drugs.
Discussion With the proposed output data model for drug interaction prediction, it becomes possible to take into account all the interactions involved in a set of substances. This allows us to consider interactions between multiple drugs, including 2-to-2 interactions that share the same effect or PK interactions that modify the bio-availability of a drug that fits or does not fit into PD interactions. We believe that this method can be integrated into a comprehensive process of monitoring and discovering new clinically significant drug interactions. By aligning the model with MEDDRA terminology, we can query pharmacovigilance databases and health data warehouses to attribute adverse events to one or more drug interactions. This may lead to the discovery of new interactions based on real-life data or raise the need to conduct clinical trials to support the evidence of a predicted interaction with machine learning algorithms.
Conclusions The data model we propose is a compromise between the models of structured databases, which are often too complex, and the models obtained by machine learning techniques which are not structured enough. This work demonstrates the technical feasibility and clinical utility of our approach. Our proposed model for drug-drug interactions (DDIs) provides a simple and structured way of representing interactions, which can be beneficial for training machine learning (ML) algorithms. In perspective, we plan to fully automate the process of predicting and structuring interactions to adapt to the proposed model.
Acknowledgements The authors thank the  CNRS  ( Centre National de la Recherche Scientifique, France ), the  Université de Strasbourg and Université de Haute-Alsace. The authors are grateful to  Bayer S. A. S. for Ph. D. grants to L. S. and F. A. The  French Fluorine Network (GIS Fluor)  is also acknowledged. Finally, the authors also thank  Dr. Emeric Wasielewski  ( NMR platform ) and  Matthieu Chessé  (analytical facilities) of the LIMA ( UMR7042   CNRS-Unistra-UHA ), as well as the analytical facilities of the  Fédération de Chimie "Le Bel"  ( FR2010 ) of the  Université de Strasbourg , who contributed, by their valuable technical and scientific support, to the achievement of this research project.
Statistics Results are presented as the mean ± standard deviation of the mean of at least three determinations (n = 3). Comparisons with the control were performed using the Tukey HSD test. All analyses were performed using the Statistical Package for the Social Sciences (SPSS) soware (version 12 for Windows, SPSS Inc. , Chicago, IL, USA). A value of p < 0. 05 was considered statistically signicant.
| The NACSELD Definition The North American Consortium for End-Stage Liver Disease (NACSELD) is a consortium of tertiary-care hepatology centres in the USA and Canada that prospectively collected data on hospitalized patients with cirrhosis admitted with or developed an infection during their hospitalization. 44  Cirrhosis was defined by either (1) biopsy, (2) clinical evidence of decompensation or varices or (3)   radiological evidence of liver nodularity and intra-abdominal varices in a patient with chronic liver disease. In contrast to other definitions,  8, 11  the authors did not explicitly state whether the participant could have compensated disease at the time of enrolment; however, it is likely that this was the case since the mean value of the model for end-stage liver disease (MELD) at admission was 20 (standard deviation, 8).
| Epidemiology of ACLF using the NACSELD criteria NACSELD-ACLF was present in 10% of the original total cohort (infected and non-infected participants with cirrhosis)  10  (Table  2 ). In the USA, other cohorts had similar prevalence estimates using such criteria: 7. 0% using the National Inpatient Sample cohort,  45  9. 8% in the Veterans Administration population,  46  or 15. 3% in the United Network for Organ Sharing (UNOS) database (2016-2020). 47  Cao et al in China found a 7. 4% prevalence of NACSELD-ACLF in 468 patients with decompensated cirrhosis. 37  While the NACSELD-ACLF expanded knowledge in non-infected patients, published granular data are only available in the infected cohort. 9  The most common organ failure noted in the I-ACLF cohort was hepatic encephalopathy (55. 7%), followed by shock (17.
Applying disease-specific definitions could be helpful as well. For example, for chronic hepatitis B, we Regarding the use of APASL in Europe, considering the differences in the phenotypic definition of the metabolic syndrome, diet/ food-host interaction, the use of APASL in Western populations is probably not indicated. Nevertheless, data using a cohort of U. S. veterans showed that both definitions (EASL-CLIF and APASL) identified a group with high short-term mortality, with a higher proportion identified with APASAL of liver-specific injury, compared to nonhepatotropic causes using EASL-CLIF. 59  We, therefore, conclude that, except for CHB, EASL-CLIF is still applicable worldwide  66  and that all definitions highlight patients with higher short-term mortality risk.
Funding information Center for Innovations in Quality , Effectiveness and Safety, Grant/Award Number:  CIN 13-413 ;  Horizon 2020 Framework Programme , Grant/Award Number:  825694  and  945096   M. J. Coenraad  received funding in the following research grants:  Horizon2020 EU Project id 945 096. Title ' Novel treatment of acute-on-chronic liver failure using synergistic action of G-CSF and TAK-242 ';  Horizon2020 EU  Project id:  825694. Title ' Microbiome-based biomarkers to predict decompensation of liver cirrhosis and treatment response '. Projects executed by  EASL-CLIF  consortium. authors do not have any disclosures to report. O RCI D ://orcid. org/0000-0002-1518-4020.
Acknowledgements This work was supported by a grant from the  Agence Nationale de la Recherche (ANR)  with  PRCI  funding ( ANR-17-CE07-0048-01 ) and by the computing facilities of the  CRCMM, 'Centre Régional de Compétences en Modélisation Moléculaire de Marseille'. Aurélie Damond  and  Flavien Bourdreux  are thanked for their support with mass spectrometry and NMR analysis. Dr. Karen Wright  is warmly acknowledged for improvement of the text as well as the  GIS Fluor  ( CNRS French Fluorine Network ) for support.
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 ). Pr. Antonio Togni  is warmly thanked for the many scientific exchanges and discussions. CNRS  and  GIS Fluor  are acknowledged. Karen Wright  is warmly acknowledged for improvement of the text.
tetraphylla. Gene annotation was performed through RNAseqbased gene modeling and resulted in the identification of 23,228 protein coding genes, which is comparable to previously characterized Apocynaceae species  17, 18, 20, 48. The Eudicotyledons BUSCO score of 93. 9% for this gene set indicates good completeness (Fig. 2b ). For almost all large scaffolds, a high gene density was observed (Fig. 3c ). The combination of BLASTX against the UniProt database and hmmscan against the PFAM database led to the functional annotation of 80. 3% (18,658 / 23,228 genes) of the predicted genes (Supplementary Data S1). More than half of the genes (55. 6%, 12,924 / 23,228 genes) were annotated by all four databases. Putative orthologs of functionally validated MIA biosynthetic genes were identified using BLASTP against protein sequences from C.
roseus, V. minor, T. iboga, G. sempervirens, and Rauvolfia sp. by considering hits with at least 90% coverage and 60% identity (Supplementary Data S2). Notably, high confidence orthologs (82-96% protein identity) for all genes of the MIA terpene precursor and the MIA biosynthetic pathway up to geissoschizine were identified. We observed that these MIA genes were heterogeneously distributed among genome scaffolds with enriched regions (Fig. 3d ). In contrast, genes predicted to encode post-geissoschizine steps were of a lower identity, given the high MIA diversification across species, except orthologs for THAS notably (85-98% protein identity). Among the putative ADH genes, 79 MDR (PF00107. 26, PF08240. 12), 76 SDR (PF00106. 25) and 27 AKR (PF00248. 21) were identified in the R. tetraphylla genome (Supplementary Fig.
The second dimension more likely distinguished young leaf tissues (seedling) from the old ones. Other tissue types such as berries, flowers and stems were not resolved in these two dimensions, showing they are characterized by specific gene expression profiles. According to these results, our dataset displays a strong variability in the gene expression levels across the different tissue types, thereby creating a powerful resource for gene co-expression studies. In addition, we performed an extensive MIA quantification on the same R. tetraphylla samples (Supplementary Data S6). Notably, this confirmed the presence of high amounts of reserpilline and isoreserpilline in young, old leaves and flowers as observed in other Rauvolfia sp  47. (Fig. 5b ). Heteroyohimbanes were also well represented with THA and ajmalicine highly accumulated in flowers and young leaves, respectively.
sign. p. : auxin-activated signaling pathway, reg. ABA-act. sign. p. : regulation of abscisic acid-activated signaling pathway, cell. resp. B-cont. subst. : cellular response to boron-containing substance levels, xenobio. metab. : xenobiotic metabolic process, detoxification: detoxification, cell. resp. Se: cellular response to selenium ion, cell. resp. Cu. starv. : cellular response to copper ion starvation, resp. C. starv. : response to carbon starvation, flav. biosyn. proc. : flavonoid biosynthetic process, sec. metab. proc. : secondary metabolic process, flav. metab. proc. : flavonoid metabolic process, Trp. biosyn. proc. : tryptophan biosynthetic process, IG. biosyn. proc. : indole glucosinolate biosynthetic process. 558 transcripts were found in the coexpression network, including 64 with close homology to known MIA genes and 32 ADHs encoding transcripts annotated as MDR (PF00107.
26; PF08240. 12), SDR (PF00106. 25) or AKR (PF00248. 21) (Supplementary Data S7). The resulting coexpression network clearly shows some tightly connected communities (Fig. 6a ). Five out of the 19 MDR encoding transcripts (MSTRG. 14221. 1, MSTRG. 5289. 1, MSTRG. 5293. 1, MSTRG. 5294. 1 and MSTRG. 5528. 1) had weaker similarities to known MIA genes (<90% identity), suggesting they may catalyze still unknown enzymatic reactions in the MIA pathways. Two had strong similarities with known MIA related genes (MSTRG. 5284. 1 98% id with VR and MSTRG. 5290. 1 95% id with THAS1). These first candidates were then completed by prediction based on a feedforward artificial neural network. A simple deep learning network was thus trained to classify transcripts as MIA-or non-MIA-related as described for the identification of missing steps in the tropane alkaloid metabolism  50.
Despite the definition of true positives (best blast hits for known MIA genes), it was less obvious to select a proper set of true negatives (non-MIA-related). We used genes predicted as conserved genes using the BUSCO methods. We finally trained a model able to correctly predict 41 transcripts as MIA related (out of 75 annotated by BLAST). The Area Under Curve (AUC) on the validation datasets and cross-validation was always lower than that of the training datasets, while still retaining a good performance (Fig. 6b, c , Supplementary Table  S4 ). Besides, the model predicted 485 transcripts (initially unlabeled) as Prediction of yohimbane synthase candidate by the proteomic analysis of the R. tetraphylla latex. In a second step, we searched for yohimbane synthase candidates via a more targeted approach.
From the metabolite profiling of R. tetraphylla tissues, we identified rauwolscine as a major MIA from the latex exuding from leaves, besides alstonine (Fig. 7a , b, Supplementary Data S6). This suggested the potential presence of ADHs displaying a rauwolscine synthase activity in latex, thus prompting us to determine its protein content. The proteomic analysis of the latex revealed a total of 575 unique proteins, 550 of which were annotated via the Swissprot database (Fig. 7d , Supplementary Data S9). In addition to the expected highly accumulated major latex proteins, we found 9 putative MDR, 10 SDR and 3 AKR within 92 proteins with GO term associated to reduction process such as GO:0055114 (oxidation-reduction process) or GO:0016491 (oxidoreductase activity). (Fig. 7d ) (Supplementary Data S9).
This notably includes the ortholog of the PR from R. serpentina (MSTRG. 20447. 1  38  ) but also two MDRs (MSTRG. 5283 and MSTRG. 5534) displaying homology with the THAS previously identified in C. roseus  36, 37. MIA gene clusters encompassing putative yohimbane synthase candidates. Finally, we searched for yohimbane synthase candidates by seeking for physically co-localized metabolic genes of interest by anchoring the predicted MIA orthogs onto the R. tetraphylla genome and examining the surrounding genomic regions for identifying putative metabolic genes of interest. This analysis revealed 76 putative MIA gene clusters (Supplementary Data S10) including 37 MDR and 7 SDR (Supplementary Data S11). Two clusters containing STR and TDC (cluster 32 and cluster 55), which have been previously described in C.
sexta (Supplementary Data S5). Samples were flash frozen in liquid nitrogen and RNA was extracted using the Macherey-Nagel NucleoSpin Ⓡ RNA plant and fungi kit (Düren, Germany) as per manufacturer's instructions. The RNA library construction and sequencing were performed at FGTech using Illumina NovaSeq 6000 technology. Sequencing data has been deposited under the Bioproject accession number: PRJNA771251. Gene model prediction and functional annotation. RNA-seq reads were processed using FastP (v0. 20  63  ) with default settings. The resulting reads were then aligned to the R. tetraphylla reference genome using HiSAT2 (2. 2. 1  64  ) for each of the 94 individual samples. Each individual alignment was then assembled into individual transcriptomes using StringTie (v2. 1. 7  65  ). The resulting 94 individual transcriptomes were then merged into a non-redundant set 56,389 representative transcripts using stringtie --merge.
Functional annotation of the consensus transcriptome was achieved with the Trinotate pipeline (v3. 0. 1 66 ,), which integrates results from blastp and blastx searches of TransDecoder (v5. 5. 0 67 ,) predicted ORFs against the Uniprot database, and hmmscan (v3. 1b2  68  ) against the PFAM database (https://pfam. xfam. org/). Assembly completeness assessment. Assembly quality assessment was performed combining the stat program from BBMap tool (v. 38. 94  69  ), MerQury (v. 1. 3  70  ) and LTR assembly index from LTR_retriever (v2. 9. 6  71  ). Benchmarking Universal Single-Copy Orthologs (BUSCO v. 5. 2. 2  72  ) with default settings using a plantspecific database of 2,326 single copy orthologs (eudicots_odb10) was used to assess assembly and gene models completeness. The agat_sp_statistics from the AGAT package (v.
0. 8. 0  73  ) allowed us to get gene models statistics. Transposable elements prediction and annotation. To identify and annotate transposable elements (TE), Extensive de novo TE annotator (EDTA v. 1. 9. 5  74  ) was used using sensitive and evaluate options. Whole-genome duplication analysis. The DupPipe pipeline  75  was used to infer whole genome duplication (WGD) events using transcript sequences of R. tetraphylla, V. thouarsii 18 , V. minor  17  , C. roseus  20  , Arabidopsis thaliana  76  , Mytragyna speciosa 15 , Solanum lycopersicum  77  , C. acuminata  13  , Calitropis gigantea  48  , G. sempervirens  12  , and O. pumila 14. To identify duplicated gene pairs (40% sequence similarity over 300 bp), discontiguous MegaBLAST  78, 79  was used on each dataset. Open reading frame of each gene pair was inferred from the NCBI's plant RefSeq protein database (May 21, 2021) using BLASTx (v.
d. , New Objective). The Xcalibur interface was used to monitor data-dependent acquisition of peptide ions. This included a full MS scan covering a mass-to-charge ratio (m/z) of 350 to 1400 with a resolution of 70,000 and an MS/ MS step (normalized collision energy, 27%; resolution, 17,500). The MS/MS step was reiterated for the eight major ions detected during the full MS scan. Dynamic exclusion was set to 50 s. Only doubly and triply charged precursor ions were subjected to MS/MS fragmentation. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD046315.