dataesr/scientific-paragraphs-categorization huggingface dataset:
9  [Chang CC et al. , 2015]. Standardized sum scores were used for analysis. Because of incomplete compatibility between PGS SNPs and variants in the ConLiGen dataset, only PGSs with compatibility > 78% were used. These corresponded to the following traits: chronotype (PGS ID: PGS002209), sleep duration (PGS ID: PGS002196), insomnia (PGS ID: PGS002149), hypertension (PGS ID: PGS002047), hypothyroidism (PGS ID: PGS001816) and type 2 diabetes (PGS ID: PGS003118)  [.
Funding The study was supported by the joint project " Individualisation in Changing Environments " (InChangE)  of the  universities of Münster and Bielefeld, Germany. The project received funding from the programme " Pro lbildung 2020" , an initiative of the  Ministry of Culture and Science of the State of Northrhine Westphalia. The sole responsibility for the content of this publication lies with the authors. The primary sources of funding for ConLiGen were grants  RI 908/7-1 ,  FOR2107  and  RI 908/11-1  from the  Deutsche Forschungsgemeinschaft (Marcella Rietschel)  and grant  NO 246/10-1  ( Markus M. Nöthen ) and grant  ZIA-MH00284311  from the  Intramural Research Program  of the  National Institute of Mental Health  (ClinicalTrials. gov identi er:  NCT00001174 ). The genotyping was funded in part by the  German Federal Ministry of Education and Research through the Integrated Network IntegraMent (Integrated Understanding of Causes and Mechanisms in Mental Disorders) , under the auspices of the e: Med Programme  ( Thomas G.
Schulze ,  Marcella Rietschel  and  Markus M. Nöthen ). The Canadian part of the study was supported by grant # 166098  from the  Canadian Institutes of Health Research  and by a grant from  Genome Atlantic/Research Nova Scotia  ( Martin   Alda). Collection and phenotyping of the  Australian University of New  South Wales sample was funded by Program Grant  1037196  from the  Australian National Health and Medical Research Council  ( Philip B. Mitchell ,  Peter R. Scho eld ,  Janice M. Fullerton ), and acknowledges support from  Lansdowne Foundation ,  Betty Lynch OAM  (dec) and the  Janette Mary O'Neill Fellowship. AT Amare  is currently supported by  National Health and Medical Research Council (NHMRC)   Emerging Leadership  ( EL1) Investigator Grant  ( APP2008000 ). The collection of the Barcelona sample was supported by grants  PI080247 ,  PI1200906 ,  PI12/00018 ,  2014SGR1636 ,  2014SGR398 , and  MSII14/00030  from the  Centro de Investigación en Red de Salud Mental, Institut d'Investigacions Biomèdiques August Pi i Sunyer , the  Centres de Recerca de Catalunya Programme/Generalitat de Catalunya , and the  Miguel Servet II  and  Instituto de Salud Carlos III.
The  Swedish Research Council , the  Stockholm County Council ,  Karolinska Institutet  and the  Söderström-Königska Foundation  supported this research through grants awarded to  Lena Backlund ,  Louise Frisen ,  Catharina Lavebratt  and  Martin Schalling. The collection of the Geneva sample was supported by grants  Synapsy-The Synaptic Basis of Mental Diseases   51NF40-158776  and  32003B-125469  from the  Swiss National Foundation. The work by the  French  group was supported by  INSERM  ( Institut National de la Santé et de la Recherche Médicale) ,  AP-HP (Assistance Publique des Hôpitaux de Paris) , the  Fondation FondaMental (RTRS Santé Mentale) , and the  labex Bio-PSY (Investissements d'Avenir program  managed by the  ANR  under reference  ANR-11-IDEX-0004-02 ). The collection of the Romanian sample was supported by a grant from  UEFISCDI, Bucharest, Romania  (grants  PCCA-89/2012 ;  PCE-203/2021 ) to  Maria Grigoroiu-Serbanescu.
The collection of the Czech sample was supported by the project Nr. LO1611  with a nancial support from the  MEYS  under the  NPU I program  and by the  Czech Science Foundation , grant Nr. 17-07070S. Biju Viswanath  is funded by the  Intermediate (Clinical and PublicHealth) Fellowship  ( IA/CPHI/20/1/505266 ) of the  DBT/Wellcome Trust India Alliance.
Patients' Data A total of 73 adult patients with primary membranous nephropathy participated in a prospective multicenter cohort in France, at Departments of Nephrology in Besancon, Marseille and Nice University Hospitals. The patients were enrolled between July 1, 2015 and January 31, 2020 and were followed-up for 2 years after inclusion. The inclusion criteria were as follows: (i) membranous nephropathy diagnosed by biopsy or serological assay; (ii) primary membranous nephropathy defined by the absence of concomitant autoimmune disease, negative hepatitis B and C serologies, and negative cancer workup; (iii) rituximab treatment according to the Kidney Disease: Improving Global Outcomes guidelines  7  (i. e. , in cases of persistent nephrotic syndrome or life-threatening complications of nephrotic syndrome); and (iv) monitoring of rituximab levels 3 months after treatment.
Machine Learning Approaches Missing Data and Preparation of the Data All preprocessing (imputation, upsampling, normalization of numeric variables, and factorization of categorical variables), machine learning and statistical analyses were performed using the Tidymodels framework in the R software (R version 4. 1. 2). 26  To evaluate the impact of the "upsampling" step, we compare the performance of clustering. Descriptive statistics included quantitative variables (median and interquartile ranges) and qualitative variables (percentages). Missing data were imputed using the K-nearest neighbors method implemented in R package "Recipes"  27  including in the meta package "Tidymodels". 28  The imputation of data was only performed on variables with a proportion of missing data lower than of 30%.
Next, the performance of the best model to predict the categorical target range of rituximab levels (<2 mg/ml or $2 mg/ml) was evaluated using accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F-score, metrics allowing to appreciate the global performances of the algorithm, in the testing data set. To distinguish the contribution of the algorithm from that of chance, the <2 mg/ml category was arbitrarily applied to all patients, and the fictive accuracy was calculated to determine the "no information rate" in the training and testing data set. The calculated accuracies, according to the 2 methods, were compared using a binomial test. Retrospective analysis to explore the relationships between: (i) machine learning algorithm prediction and time to remission or (ii) machine learning algorithm prediction and cumulative dose of rituximab required to achieve remission were performed in patients from the training set.
Patients' Data A total of 73 patients were enrolled in this study (Figure  1 ). Most patients were men (69. 8%). The median (interquartile range) age was 59 (40-70) years. The median (interquartile range) weight and height were 74. 8 (68. 4-84. 0) kg and 172 (165-178) cm, respectively. The most common drug used as a concomitant treatment was furosemide (>75% of patients), followed by angiotensin-converting enzyme inhibitors (63% of patients). The serum albumin level and urinary proteinto-creatinine ratio (median [interquartile range]) at the first rituximab infusion were 22. 0 g/l (17. 0-29. 0) and 5. 0 g/g (4. 1-7. 8), respectively. A total of 92 rituximab levels were obtained in these patients at month-3 postrituximab treatment, 59. 8% of which had rituximab levels <2 mg/ml. The training data set and the testing data set were composed of 68 and 24 patients' rituximab levels from 55 and 22 patients, respectively (Figure  1 ).
Retrospective Analysis of the Relationship Between Time to Remission or Cumulative Dose of Rituximab Required to Achieve Remission and the Algorithm's Initial Prediction for Each Patient We analyzed the outcome of 45 patients from the training data set for whom we had at least 24 months of follow-up after the first course of rituximab to validate the clinical relevance of our algorithm, (Figures  1  and 3 ). The characteristics of the patients are presented in Table  4. Patients were retreated according to Kidney Disease: Improving Global Outcomes guidelines in case of persistent nephrotic syndrome and immunological activity 6 months after rituximab treatment. After 24 months of follow-up, 4 patients developed end-stage renal disease and 1 Figure  1. Study flow chart. Four patients were included in both data sets.
The highly transcrip@onally divergent/Lo-CBX5 pa@ents were all either RRMS, SPMS, or PPMS, while the Hi-CBX5 group was enriched in early-stage pa@ents (Figure  1D ). None of the other criterion proved relevant (pa@ent age, gender, or comorbidi@es). Pathway analysis on the genes differen@ally expressed between Hi-CBX5 pa@ents and Scs (38 upregulated genes and 109 down-regulated genes, 2-fold or more, adj. pVal<0. 05, baseMean>10 -Figure  1E ) revealed that down-regulated genes were enriched in Vitamin D regulated genes (analysis of the ChEA database, Sup. Figure  1A ). This observa@on may reflect the importance of Vitamin D in early phases of the disease. In contrast to the Hi-CBX5 pa@ents, Lo-CBX5 pa@ents displayed a drama@cally high number genes with modified expression when compared to the SCs, with a total of 1395 upregulated and 1350 down-regulated genes (2-fold or more, adj.
pVal<0. 05, baseMean>10 -Figure  1F ). As an@cipated from earlier studies, pathway analysis using the KEGG database on upregulated genes iden@fied ac@va@on of the IL17-, TGF-beta-, NFkappaB-, and MAPK-pathways as well as genes associated with hypoxia (Elkjaer et al. , 2022) (Sup. Figure  1B ). The pathway analysis also detected a significant enrichment in genes associated with ferroptosis. This form of regulated cell death characterized by the iron-dependent accumula@on of lipid peroxides, has previously been linked to neurodegenera@on in MS  (Luoqian et al. , 2022). It is induced either through.
Evidence for reduced Integrator ac%vity in Lo-CBX5 pa%ents To inves@gate the regulatory mechanism underlying the extensive transcriptome reprogramming observed in Lo-CBX5 pa@ents, we confronted the list of genes upregulated in Lo-CBX5 pa@ents (rela@ve to the SCs) with the ENCODE Transcrip@on Factor Data Bank, which serves as a comprehensive repository of informa@on about transcrip@on factors and their involvement in controlling gene expression. This approach very clearly designated NELFE targets as enriched among the upregulated genes (Figure  2A ). NELFE is a subunit of the Nega@ve Elonga@on Factor (NELF) complex, which controls the speed and efficiency of RNAPII movement along the DNA, and regulates gene expression by causing RNAPII pausing downstream of the transcrip@on start site (TSS).
Consistent with a possibly increased cell death by ferroptosis, we noted also that down-regulated genes (down 2-fold or more, Adj pVal<0. 05, 3248 genes) were enriched in genes involved in fady acid beta oxida@on (FAO -Sup. Figure  4F ). A down-regula@on of this pathway is an@cipated to favor ferroptosis, as FAO normally consumes the fady acids, leading to a reduc@on in the rate of lipid peroxida@on. Analysis of the down-regulated genes with either Reactome, or BioPlanet databases revealed that down-regulated genes were also enriched in genes involved in cell cycle, DNA replica@on, and mitochondrial ATP synthesis, consistent with a subset of the cells possibly withdrawing from the cell cycle (Sup. Figure  4G  and 4H ). The Cbx5-/-mouse model recapitulates the defect in Integrator ac%vity. Finally, we inves@gated whether Cbx5-/-mice recapitulated some of the transcrip@onal anomalies observed in Lo-CBX5 MS pa@ents.
RefSeq allows visualiza@on of splice variants of the two genes annotated in Hg19 version of the human genome. (D) Within each group of pa@ents defined by either high, low, or intermediate CBX5 levels, the percentage representa@on of each stage of the MS disease is indicated. (E-F) Vulcano plots illustra@ng differen@al gene expression between Hi-CBX5 pa@ents and SCs (E), or between Lo-CBX5 pa@ents and SCs (F). (H) Histograms represen@ng the fold-ac@va@on of the indicated genes between SCs and Lo-CBX5 pa@ents. (G) Schema@c representa@on of the KEGG ferroptosis pathway. Genes up-or downregulated between SC and Lo-CBX5 are indicated in red or blue respec@vely. (I) Analysis of genes down-regulated between SC and Lo-CBX5 pa@ents using the Reactome pathway databank revealing func@onal enrichment.
Sup. Figure  1  : Extensive transcrip%onal deregula%on in monocytes from pa%ents with reduced expression of CBX5 (A) Analysis of genes up-regulated between SC and Hi-CBX5 pa@ents using the ChEA (ChIP-Seq Enrichment Analysis) database providing informa@on on transcrip@on factors likely to regulate these genes. (B) Analysis of genes up-regulated between SC and Lo-CBX5 pa@ents using the KEGG database providing informa@on about the molecular func@ons and biological pathways of genes. (C) Bar graph repor@ng varia@on in expression of the indicated genes based on RNAseq data in from SCs and Lo-Cbx5 pa@ents. Levels in SCs set at 1. *: Adj. pVal<0. 05, **: Adj. pVal<0. 01, ***: Adj. pVal<0. 001. (D) Analysis of genes up-regulated between SC and Lo-CBX5 pa@ents using the WikiPathway database. (E) Analysis of genes down-regulated between SC and Lo-CBX5 using the Reactome, BioPlanet, or WikiPathway databases as indicated.
Molecular biology Preparative PCR for plasmid construction were performed with Q5 High-Fidelity DNA Polymerase (New England Biolabs cat. #M0491S) following the manufacturer's protocol. All constructs were verified by Sanger sequencing (Eurofins). Their annotated sequences are available at https://github. com/HKeyHKey/Busseau_ et_al_2023/tree/main/Sequences.
Data and script availability Small RNA-Seq data has been deposited at NCBI's SRA (https:// www. ncbi. nlm. nih. gov/sra/) under BioProject accession numbers PRJNA668265 (for Fig. 1 ) and PRJNA841589 (for Fig. 4 ). Scripts and intermediary files are accessible at https://github. com/HKeyHKey/ Busseau_et_al_2023 and https://www. igh. cnrs. fr/en/research/ departments/genetics-development/systemic-impact-of-smallregulatory-rnas#programmes-informatiques.
Acknowledgments We are grateful to  Simon Nicot  for guidance in cell culture,  Lara Demont  for help in the screening of edited flies,  Laly Robledo  for her implication in the setting of Luciferase assays, and  Guillaume Canal  for his help in optimizing spike-in oligo sequences. We acknowledge the  MRI imaging facility , member of the  France-BioImaging infrastructure , and the MGX sequencing facility, member of the France G enomique National Infrastructure, supported respectively by contracts  ANR-10-INBS-04  and  ANR-10-INBS-09  "Investments for the future" of  French National Research Agency , as well as the  Biocampus Drosophila facility. Stocks obtained from the  Bloomington Drosophila Stock Center  ( NIH P40OD018537 ) were used in this study. This work was funded by the  CNRS.
anti-SUN1 (1:200; HPA008346; Sigma-Aldrich), rabbit anti-SUN2 (1:200; HPA001209; placed on a synthetic quartz photomask (Delta Mask), previously activated with deep-UV light (PSD-UV, Novascan Technologies) for 5 min, using a 3uL of MilliQ water to seal it to the mask. The coverslips were then irradiated through the photomask with the UV lamp for 5 min and left to dry before being incubated with FBN μ ml; F1141; Sigma-Aldrich), in 100 mM NaHCO3 at pH 8. 6, for 30 minutes, at RT. Whenever possible, 5μ ml le a -conjugated fibrinogen (ThermoFisher Scientific) was added to the fibronectin mix in order to visualise the pattern surfaces. Cells were added to the freshly incubated coverslips and allowed to spread for 15 minutes, before removing excess cells and new culture medium added, and cells left to fully adhere for another 12-16 hours.
Otherwise, multiple group comparison was done using a nonparametric ANOVA (Kruskal-Wallis). Multiple comparisons were analyzed using either post-hoc Student-Newman-Keuls (parametric or unn's (nonparametric) tests. When comparing two experimental groups only, a parametric t test was used when the sample had a normal distribution, or a nonparametric Mann-Whitney test was used for samples without normal distribution. Comparison for multiple timecourse datasets was done using an ANOVA Repeated Measures, when the samples had a normal distribution. Otherwise, group comparison was done using Repeated Measures ANOVA on Ranks. No power calculations were used. All statistical analyses were performed using the GraphPad Prism (Dotmatics). When comparing proportions between 2 populations, a z-score was calculated.
Study design This is a secondary analysis of data from the AbSeS study: a multinational observational cohort study of ICU patients with intra-abdominal infection. [ 7 ]  The AbSeS cohort included 2621 patients from 309 ICUs in 42 countries. For the present study, only patients with pancreatic infection were considered ( n = 165, Figure  1  ). Approval was granted by established national, regional, or local institutional review boards. The study is registered at ClinicalTrials. gov (NCT03270345). More detailed descriptions of inclusion and exclusion criteria, definitions, methods, and collection of data are described elsewhere. [ 7 ].
e. , radiographic or direct surgical inspection with evidence suggestive of pancreatic abscess or other types of infection originating from the pancreas). The following data were selected from the AbSeS database: demographics (sex, age, and body mass index [BMI]), comorbidities (cardiovascular disorders, chronic pulmonary disease, neurologic disease, diabetes, acquired immunodeficiency syndrome [AIDS], liver disease, chronic renal failure, immunosuppression, malnutrition, and obesity), lifestyle risk factors (tobacco, drug, and alcohol abuse), risk factors for MDR pathogens (nursing home resident, out-of-hospital parenteral/nutrition or vascular access, chronic dialysis, recent hospitalization, and antibiotics use in the past 6 months), and components of the AbSeSclassification as mentioned above.
Consequently, we decided to keep the clinical evaluation on day 7 in the study analysis but did not define it as either successful or failed source control. The clinical evaluation on day 7 was then categorized as "stable , " "unstable as evidenced by persistent signs of inflammation , " or "unstable with need for recurrent interventions. " In this evaluation, having "persistent signs of inflammation " may reflect either source control failure or ongoing pancreatitis. Likewise, the "need for recurrent interventions " may reflect either source control failure or a complicated case with the need for a stepwise damage control strategy. For patients who died before day 7, the source control evaluation was carried out on the last day alive. Outcome data included ICU length of stay and ICU mortality within 28 days of observation for patients discharged earlier from the ICU.
15. The final model contained, preferably, a maximum of one independent covariate per ten outcome events (dead). Results are reported as odds ratio (OR) and 95% confidence interval (CI). The SOFA score was not considered as it substantially overlaps with the severity of disease expression in the AbSeS-classification. Survival analysis of patients according to source control outcome was assessed using the Kaplan-Meier method, and the log-rank test was used to compare the survival curves. Statistical significance was defined as P < 0. 05. Statistical analyses were performed using SPSS Statistics version 28, and all tests were two-tailed.
[ 62 ]  As our cohort exclusively contains ICU patients with pancreatic infection, they are considered at high risk for extra-pancreatic complications. [ 5 , 55 ]  As previously shown, age has a significant impact on mortality in acute pancreatitis. [ 63 ]  Similar to the study by Tian et al. ,  [ 55 ]  the mean age of patients in the non-survival group was significantly higher than in the survival group. Compared to the overall cohort of the AbSeS study, the mean age in the present cohort is significantly lower (66 years and 58 years, respectively), indicating that acute pancreatitis in the ICU is associated with high mortality rates irrespective of age. First, per protocol, the AbSeS database only included patients with infection, making it impossible to assess the impact of infection in a subset of patients with acute pancreatitis.
Second, some observations are counterintuitive, indicating that our study might be underpowered. In fact, with only 58 deceased patients, the number of covariates allowed in a logistic regression model to avoid overfitting is quite low. Therefore, larger studies are necessary to elucidate further the impact of the variables in the AbSeS-classification on mortality in ICU patients with pancreatic infection. Third, variables potentially influencing the outcome might not have been collected (e. g. , the etiology of pancreatitis and location and extent of necrosis). [ 64 ]  Fourth, no specific data were collected regarding the classification of acute pancreatitis, according to the revised Atlanta classification by Banks et al. [ 65 ]  Yet, we assume that the included cases had severe pancreatic infections according to the definitions of the study protocol.
Conclusions Older age, localized peritonitis, and a clinical condition reflecting persistent inflammation at 7 days, as well as the need for recurrent interventions within a week in pursuit of source or damage control, were the strongest predictors of mortality in critically ill patients with pancreatic infection. The AbSeSclassification system did not predict mortality in this sample of patients. ization, data collection, investigation, review and editing. Kostoula Arvaniti: data collection, investigation, review and editing. Koen Blot: database management, investigation, review and editing. Ben Creagh-Brown: data collection, investigation, review and editing. Dylan W. de Lange: data collection, investigation, review and editing. Jan De Waele: conceptualization, data collection, investigation, review and editing.
Mieke Deschepper: database management and statistical analysis (advanced), investigation, review and editing. Yalim Dikmen: data collection, investigation, review and editing. George Dimopoulos: conceptualization, data collection, investigation, review and editing. Christian Eckmann: conceptualization, data collection, investigation, review and editing. Guy Francois: conceptualization, project administration, interpretation data, review and editing. Massimo Girardis: data collection, investigation, review and editing. Despoina Koulenti: conceptualization, data collection, investigation, review and editing. Sonia Labeau: project administration, database management, investigation, review and editing. Jeffrey Lipman: conceptualization, data collection, investigation, review and editing. Fernando Lipovestky: data collection, investigation, review and editing.
Funding AbSeS is a Trials Group Study of the  European Society of Intensive Care Medicine  and was supported by a  Pfizer   investigator-initiated research grant. Received grants related to the submitted work:  S. Blot  ( Pfizer ). Received honoraria or grants outside the submitted work:  M. Antonelli  ( Fresenius ,  Pfizer, Toray );  J. De Waele  ( Research Foundation Flanders ,  Pfizer ,  Bayer, MSD );  C. Eckmann  ( Merck, Pfizer );  J. Lipman  ( MSD ,  Pfizer );  E. Maseda  ( Astellas Pharma, Pfizer, MSD ).
Material and methods pCND has been de ned by the American Society of Head and Neck Surgery and successively by ATA as a comprehensive, compartment-oriented removal of the prelaryngeal (along the recurrent nerve) and pretracheal lymph nodes even if nodal metastasis is not detected clinically or radiologically before or during surgery. The data of adult patients with PTC who underwent total thyroidectomy (TT) with or without pCND (patients who underwent TT for a non-malignant disease but were then diagnosed with PTC on nal pathology) in nine French departments of endocrine surgery from January 2015 to June 2021 were retrieved from the prospectively maintained EUROCRINE©database and retrospectively analyzed. pCND was performed in all patients who were preoperatively (via ultrasound-guided ne-needle aspiration cytology) and/or intraoperatively (via intraoperative frozen section) classi ed as cN0.