dataesr/scientific-paragraphs-categorization huggingface dataset:
Two months after a request for clarification, we received the randomisation list. A close inspection of this list confirmed that we were able to correctly identify the 297 randomised patients and that we included by mistake in our analysis population six out of 600 non-randomised patients. Such discrepancy had no consequences on the conclusion of the re-analysis. The results of the re-analysis exceeded the fixed threshold for the effect size of the primary endpoint of relapse of depressive symptoms (originally -0. 45 vs -0. 57 in the re-analysis) but this was considered a minor clinical difference and the study was considered as reproduced. In studies using mixed models with repeated measures, we used SAS instead of R, reaching similar conclusions (suggested by the sponsor). However, small numerical inconsistencies were present.
Interestingly, we intentionally adopted the registered report format for this paper, in order to pre-emptively address this potential concern: this publication process enabled a thorough and independent peer review of its "scientific merit" prior to data collection and analysis. If the scientific merit of any data re-use is surely important when it comes to responsible sharing of IPD, it is however a subjective and arbitrary notion. Furthermore, there was no agreement on this point for our request, as some sponsors, including those with independent procedures (e. g. those sharing on YODA), agreed to share their data. It is likely that sponsors are less inclined to share their data for the purpose of a reanalysis. A survey of trialists suggested that willingness to share data could depend on the intended reuse of the data, with 97% of respondents willing to share data for a meta-analysis vs 73% for a re-analysis  [48].
Acknowledgements This publication is based on research using data from data contributors Abbvie, which has been made available through Vivli,  Inc. Vivli  has not contributed to or approved, and is not in any way responsible for, the contents of this publication. This study, carried out under YODA Project # 2021-4637 , used data obtained from the  Yale University Open Data Access Project , which has an agreement with  Johnson & Johnson. The interpretation and reporting of research using these data are solely the responsibility of the authors and do not necessarily represent the official views of the  Yale University Open Data Access Project or Johnson & Johnson. The authors thank  Damien Bergeat  for providing the code for Fig. 3,  Alexandre Scanff  for his help with the code for Fig. 4 and  Anne Hespel  and  Frederic Rimattei  for treating the data requests.
Recruitment for four-arm (oral SHR0302 4 mg once daily, 8 mg once daily, 4 mg twice daily, and PBO) phase 2 RCT are completed in moderate to severe UC (AMBER2 or NCT03675477) and CD (NCT03677648). 71, 72 While data are not yet available in CD, SHR0302 resulted in a higher rate of clinical response (46. 3% vs. 26. 8% on PBO; p = 0. 059) and remission rate compared to PBO after 8 weeks of treatment in UC and was well tolerated. 73 A phase 3 study is currently underway to investigated the efficacy and safety in moderate to severe active UC. 74 udies show that using selective JAKs does not result in a loss of efficacy (compared to pan-JAK inhibitors), but there are still doubts about safety. 75 Indeed, not only very quickly effective (with symptoms improvements as early as day 1 with upadacitinib for example  76  ), two recent systematic review and network metaanalysis comparing a series of small molecules and biologics have shown that upadacitinib was the best treatment to induce clinical remission in patients with moderate to severe UC,  77, 78  whether or not the patient has been previously exposed to anti-TNFs.
95  Enrolment into subsequent induction cohorts and into the maintenance phase of BERGAMOT is ongoing as well as in the open-label extension and safety study for CD patients previously enrolled (JUNIPER). 96 AJM300 (carotegrast methyl) is a new small molecule, α4integrin antagonist, being evaluated in UC. 88  In the phase III study (NCT03531892), AJM300-treated patients (960 mg three times daily orally) had a significantly higher rate of clinical response (45%; p = 0. 00028) than PBO group (21%) at week 8  88, 89. Statistically significant improvements were also observed in the secondary endpoints including mucosal remission rate and rectal bleeding disappearance rate. 88 These results were consistent with those obtained in phase 2a in UC. 97  There was no difference in the incidence of adverse events between the groups.
103 Ozanimod is currently being studied in a phase 3 studies in UC  [104] [105] [106]  and CD,  [107] [108] [109] [110]  as well as in phase 2/3 in UC in pediatrics. 111 rasimod (APD334) is an oral S1P1, S1P4 and S1P5 receptor modulator. The efficacy and safety of etrasimod in patients with moderate to severe active UC was evaluated in the phase 2 OASIS trial (NCT02447302). 112 At week 12, etrasimod 2 mg was significantly more effective than PBO in producing clinical (p = 0. 009 for etrasimod 2 mg) and endoscopic improvements. 112 The results of subgroup analyses showed similar improvement in patients with prior exposure to anti-TNF-α therapy. 112 Regarding tolerance, three patients, known to have an atrioventricular block before initiation, presented a transient, asymptomatic, low-grade new episode that resolved spontaneously.
120, 121 ll like receptor agonists Cobitolimod (DIMS0150 or Kappaproct®) is an oligonucleotide currently under evaluation in UC. [122] [123] [124]  It is a single stranded DNAbased immunomodulatory sequence, containing an unmethylated CpG motif (mimicking DNA bacterial), which activate TLR-9 on target immune cells (including intestinal T and B lymphocytes and antigenpresenting cells). interferon). 125 In the COLLECT phase 3 study, the topical administration of cobitolimod, although not meeting the primary endpoint, induced PRO-based symptomatic remission, mucosal healing and histological improvement. 122, 123 No safety signals were detected. 122, 123 The efficacy and safety of cobitolimod as induction and maintenance therapy is currently under investigation (phase 3 CONCLUDE study or NCT04985968) in patients with moderate to severe active left-sided UC.
ANTI-FIBROTIC THERAPIES The humanized antagonistic monoclonal IgG1 antibody that blocks human IL36 R signalling, spesolimab (BI 655130), is a treatment with possible anti-fibrotic action that has recently been tested in a CT in patients with fibrostenotic CD (NCT05013385). IL-36 is a group of 3 cytokines (IL-36α, ß and γ) that are overexpressed in the gut mucosa from fibrostenotic CD patients and that are known to activate myofibroblasts, one of the key players of intestinal fibrosis. 169, 170 The purpose of this phase 2a study was to demonstrate that spesolimab was effective in maintaining symptomatic and/or inducing radiographic stenosis response in patients with symptomatic CD-related small bowel stricture, who have achieved symptomatic stenosis response after standard medical therapy. Patients received, in addition to this standard treatment, either an infusion of spesolimab 1200 mg (every 4 weeks until week 8 then every 8 weeks) or a PBO.
5 or 1 mg once daily) versus PBO Guselkumab (200 mg IV at wks 0,4,8) versus golimumab (200 mg SC at wk 0 then 100 mg SC Etolizumab SC at wks 2,6,10) (105 mg/4 weeks) versus a versus infliximab combination with guselkumab and Upadacitinib PO (15 golimumab (GUS or 30 mg) versus Etolizumab SC 200 mg IV + GOL Mirikizumab SC 200 mg SC at wk 200 mg/4 weeks 0, GOL 100 mg PBO once daily (105 mg/4 weeks) versus PBO Ozanimod 1 mg daily SC at wks2, 6, and 10, and GUS 200 mg IV at wks 4 and 8) Mirikizumab IV (50, 200 or 600 mg) versus PBO at AJM300 (960 mg wks 0,4 and 8 PO) versus PBO Ozanimod PO (1 mg 3 times daily per day) versus Etolizumab SC PBO (105 mg/4 weeks) versus PBO Upadacitinib PO (7. 5, Mirikizumab 200 mg daily (105 mg/4 weeks) versus PBO once Etolizumab SC 15, 30 or 45 mg) versus PBO SC every 4 or 12 weeks AJM300 (960 mg PO) versus PBO 3 times daily T A B L E 1 (Continued) Phase Study name Ivarmacitinib 2NCT03675477 AMBER2 3NCT02163759 HIBISCUS I Anti-integrins 3NCT02819635 U-ACHIEVE induction Etrolizumab 2NCT01336465 (UC1) EUCALYPTUS 3NCT03518086 LUCENT-1 3NCT02171429 HIBISCUS II 3NCT03653026 S1P modulators U-ACCOMPLISH Ozanimod 2NCT01647516 (UC2) TOUCHSTONE 2aNCT03662542 VEGA 3NCT02136069 GARDENIA 3NCT02819635 UC3 or U-ACHIEVE 3NCT02165215 2NCT02531126 maintenance LAUREL TOUCHSTONE OLE 3NCT03524092 LUCENT-2 Mirikizumab 2NCT02589665 I6T-MC-AMAC AJM300 2aJapicCTI- 132293 3NCT02435992 TRUE-NORTH 3NCT02100696 HICKORY JAK inhibitors Upadacitinib 2bNCT02819635 3NCT03531892 UNITED EUROPEAN GASTROENTEROLOGY JOURNALT A B L E 1(Continued).
94 (0. 31); p = 0. 15 Etrasimod 2 mg: 2. 49 (0. 31); p = 0. 009 PBO: 1. 50 (0. 30) Clinical response at end of treatment: 64% Clinical remission at week 6: Cobitolimod 2 � 31 mg: 5/ 40 (13%); p = 0. 18 Cobitolimod 2 � 125 mg: 2/ 43 (5%); p = 0. 66 Cobitolimod 2 � 250 mg: 9/ 42 (21%); p = 0. 025 10 weeks 12 weeks 52 weeks 6 weeks Anti-TNF-naive and anti-TNF- experienced Anti-TNF-naive and anti-TNF- experienced Anti-TNF-naive and anti-TNF- experienced Anti-TNF-naive and anti-TNF- experienced = 30 = 156 = 112 = 211 I n I n OLE n I n Ozanimod 1 mg once daily Etrasimod PO (1 or 2 mg once daily) versus PBO Etrasimod 2 mg Cobitolimod versus PBO Real-world 2NCT02447302 OASIS 2NCT02536404 OASIS OPEN 2bNCT03178669 CONDUCT Etrasimod Anti-TLR9 Cobitolimod.
Cell Surface Staining HEK-293 cells-CRISPR for MRLC stably expressing 3xmyc-ABCB4-wt were transiently transfected with plasmids encoding MRLC-GFP. After 48 hours of transfection, cells were washed three times with HEPES-buffered (20 mmol/L, pH 7. 0) serum-free medium (HSFM). Cell surface antigens were labeled at 0°C for 60 minutes with monoclonal anti-myc antibody, diluted in HSFM/0. 2% BSA. After surface labeling, cells were extensively washed with HSFM/0. 2% BSA, fixed and ABCB4 was visualized with Alexa-Fluor 594-conjugated secondary antibodies. Fluorescence was examined by confocal microscopy and the amount of ABCB4 at the plasma membrane was quantified using ImageJ software.
Methods of natural language processing associated with machine learning or deep learning can support detection of adverse drug reactions in abstracts of case reports available on Pubmed. In 2012, Gurulingappa et al. proposed a training set for the recognition of named entities corresponding to drugs and adverse reactions on 3000 Pubmed abstracts. We implemented a classifier using deep learning with a Bi-LSTM and a CRF layer that achieves an F-measure of 87. 8%. Perspectives consist in using BERT for improving the classifier, and applying it to a large number of Pubmed abstract to build a database of case reports available in the literature.
Introduction In order to monitor drug safety with the medical literature, it is necessary to carry out bibliographic queries on a regular basis. However, pharmacovigilance teams have only Pubmed and its user interface which is intended for all users. Our aim is to build a database consisting of Pubmed abstracts related to adverse drug reactions (ADR). We employ the training set based on 3000 Pubmed abstracts on case reports likely to describe ADRs implemented by Gurulingappa et al. [1]. Most previous works have applied machine-learning-based methods, but not deep learning  [2, 3]. Our first step as described in this paper was to implement a classifier to detect named entities related to drugs and ADRs using a conditional random field (CRF) and a Long short-term memory (LSTM) deep learning layers.
Results and Discussion Table  1  shows the results we obtained with our models (first three lines), and results obtained by other authors (next three lines). Our lightweight model is capable to extract quite efficiently named entities on drugs and adverse reactions from Pubmed abstracts. We plan to detect ADRs on a large number of case reports to build a reference database to improve queries for pharmacovigilance in the literature. A Bidirectional Encoder Representations from Transformers (BERT) model  [7]  should be implemented to improve the performances of our classifier.
Sequence Analysis After sequencing, the UPARSE 16S-protocol was used to merge the paired reads by their 3'-ends, lter reads, and cluster unique reads into OTUs using a 97% identity threshold, and quantify OTU abundances by mapping reads to the OTU sequences  [31]. OTUs with a relative abundance of less than 0. 01% were discarded. Each OTU sequence was searched in the NCBI Targeted Loci 16S database using NCBI BLAST  [32]  with an identity cut-off of 97% and E-value cut-off of 0. 01, and assigned to the annotated species. If no match was found, the sequence was also searched in the NCBI nt database using the same cut-offs but also excluding unclassi ed species or environmental sequences. If no match was found in either database, the OTU was labelled as unclassi ed. OTUs with multiple best matches were assigned to the least common ancestor of all alignments with the same BLAST bit score.
Microbiome analysis Data from 24 out of the 25 individuals were analysed, one individual was excluded because of missing samples. The number of sequencing reads per sample ranged from 1,117 to 168,807 (mean 20,7764, median 11356) and was on average higher in the treated samples and lowest in untreated samples after tape stripping (Supplement Figure  S1 ). A comparison between the fraction of numbers of operational taxonomy units (OTUs) that are classi ed to a taxon and the fraction of reads belonging to classi ed OTUs shows that on average 83% of OTUs are classi ed, but they comprise on average 95% of the reads, i. e. , most unclassi ed OTUs are of low abundance (Supplement Figure  S2 ). The rarefaction analysis (Supplement Figure  S3 ) shows the number of OTUs for the 11 samples per individual after subsampling to half read depth, one fourth read depth and further dilutions down to 1/1024.
Data availability The sequencing data for all 264 microbiome samples are available at NCBI Genbank under Bioproject accession number PRJNA713953. Availability of data and material: All data generated or analysed during this study are included in this published article [and its supplementary information les]. The sequencing data for all 264 microbiome samples are available at NCBI Genbank under Bioproject accession number PRJNA713953.
A total of five IBD patients met these conditions and were selected for testing. Treatment of BLECs with plasma from such patients in comparison to treatment with plasma from matched healthy donors increased BBB permeability and expression of claudin-5 and ICAM-1, with COX-2 suffering no alterations. While treatment with pioglitazone prior to treatment with IBD patient plasma led to no alterations in permeability and claudin-5 expression, ICAM-1 expression was decreased (Figure  7A-D ). These data suggest that pioglitazone can attenuate early inflammatory damage on the BBB elicited by peripheral inflammatory factors, not limited to TNFα, during progression of IBD, likely influencing ICAM-1 mediated effects.
Slides were analyzed in a Axio Imager A2 ® fluorescence microscope (Leica Microsystems, Wetzlar, Germany) and in a Zeiss LSM-780-NLO ® confocal fluorescence microscope (Carl Zeiss, Oberkochen, Germany). Representative images from five different fields were obtained at 100× magnification for each slide, for each different marker. Continuity of claudin-5 expression alongside cell-cell contact areas was assessed utilizing Cell Profiller 4. 2. 1 ® software (Stirling et al. , 2021). Pipelines for analysis were created adapting similar methods as described elsewhere  [66]. Primary antibody details for IF are shown in Supplementary Materials (Supplementary Table  S2 ).
Bioinformatics For visualization of data generated after treatment of BLECs with plasma from both healthy donors and IBD patients in a comparative manner, bubble heat-maps were generated utilizing Morpheus software (Morpheus, https://software. broadinstitute. org/ morpheus, accessed on 1 March 2022). Results are shown as log2 fold-changes of data generated from treatment with patient plasma in comparison to data generated from treatment with healthy donor plasma. Exploratory analysis of parameters assessed after treatment of BLECs with plasma from IBD patients alongside plasma from healthy donors was carried out by principal component analysis (PCA). Loading vectors and PCA bi-plots were shown to indicate correlation of parameters with principal components and data clustering, respectively.
Statistical Analysis All data were analyzed with GraphPad Prism 7 ® software (Graphpad Software ® , San Diego, CA, USA). Normality of large patient datasets (n > 30) was assessed with the Kolmogorov-Smirnov test. Following comparisons between matched datasets were performed with Wilcoxon signed-rank test, and associations between two variables were assessed with Spearman's correlation test. All other analyses of smaller datasets (n < 6) of either matched or non-matched data were performed utilizing one-way analysis of variance tests (ANOVA) followed by Tukey's post hoc tests or repeated-measures ANOVA followed by Dunn's post hoc tests. Results of analyses of parametric data are shown as mean ± standard deviation, while results of analyses of non-parametric data are shown as median alongside 0.
This metric directly relies on the observed number of mutants towards a neutral arbitrary phenotype. It is calculated as the ratio of the observed number of mutants in treated populations to the observed number of mutants in untreated controls (see Methods for details). While the observed number of mutants is not exactly the mutation supply, because the same mutation event can give several clones in the final population, both variables are closely related and the former can be directly measured in practice. To assess the role of antibiotics on bacterial evolvability, we collected raw data from primary literature on the effect of antibiotic treatments on mutation rates, and conducted a meta-analysis with emphasis on the effect of treatments on both mutation rate and population size. Additionally, we performed our own set of experiments for the most commonly studied antibiotics, to mitigate the difficulty of comparing data from different labs in meta-analyses.
Meta-analysis of previous studies We focused on studies posterior to 2008 (no more than 10 years old at the beginning of this project) which quantified the effect of sub-inhibitory antibiotic treatments on bacterial mutagenesis. We were able to gather raw data for 10 studies (see Table  1 ). For each study, we extracted the raw data of the number of mutants (toward a chosen neutral phenotype) and total bacterial densities both after antibiotic exposure and in untreated controls, and the fraction of population plated on selective medium to score mutants. This list of variables reflects important features of mutation rate estimation using the standard fluctuation test devised by S. E. Lüria and M. Delbrück: the initial bacterial density of the founder population does not matter as long as it is small, and partial plating needs to be mathematically accounted for  [35].
We incubated the cultures at 37 • C shaken at 300 rpm for 24 hours. Estimation of mutant frequencies After 24 hours of growth in the presence of the different antibiotic treatments, we estimated the number of de novo mutants by plating 200 µL of each culture onto LB agar supplemented with 100 µg/mL rifampicin (i. e. rifampicin resistance is the neutral phenotype chosen to score de novo mutations). We further plated serial dilutions of six randomly chosen populations for each treatment onto LB agar to estimate total bacterial densities. Mutation rate We estimated mutation rate in the datasets from the literature and in our own experiments using Rsalvador v1. 7  [25] , with a correction for partial plating. This estimation does not account for potential bacterial death during the experiment.
This work was supported by the ERC (Grant number 268540, FP7-IDEAS-ERC program, attributed to S. B. ), SNF (Grant number 155866, attributed to S. B. ), and Marie Curie-ETH cofund Fellowship (Grant number 16-2 FEL-59, attributed to M. V. ). Author contributions: M. V. and A. F. performed the experiments and analyzed the data. A. F. , M. V. and S. B. conceived the research and wrote the paper. Competing interests: The authors declare no competing interests.
Experimental data analyzed in this work: origin, protocol, reported variable and antibiotic treatment. The protocol can be 'FT', indicating a standard fluctuation test, or 'Regrowth', indicating that cultures recovered in fresh antibioticfree medium following antibiotic exposure. The reported variable indicates whether the original study reported mutation rates or mutation frequencies. In both cases, we use the raw data for mutant counts, population size and plating fraction to analyze all datasets with the same methods.
: This work was supported by the ERC (Grant number 268540, FP7-IDEAS-ERC program, attributed to S. B. ), SNF (Grant number 155866, attributed to S. B. ), and Marie Curie-ETH cofund Fellowship (Grant number 16-2 FEL-59, attributed to M. V. ). Author contributions: M. V. and A. F. performed the experiments and analyzed the data. A. F. , M. V. and S. B. conceived the research and wrote the paper. Competing interests: The authors declare no competing interests. Article Protocol Variable Antibiotic (dose µg/mL) Ampicillin (0. 05) Ciprofloxacin (0. 05) Chloramphenicol (0. 15) Gentamicin (0. 1) Kanamycin (0. 2) Baharoglu et al, 2011 [16] FT frequency Mitomycin C Neomycin (0. 1) Rifampin (0. 05) Spectinomycin (0. 2) Tetracycline (0. 15) Tobramycin (0. 1) Trimethoprim (0. 05) Cortes et al, 2008 [17] FT rate Chloramphenicol (3.
Chromatographic Analysis of Olaparib A reverse phase (RP) high-performance liquid chromatography (HPLC) with UVdetection was used for the quantification of Olaparib (OLA) by adopting the previously reported methods  [19] [20] [21]. Briefly, the HPLC system (Waters ® 1500-series controller, Milford, MA, USA) was equipped with a UV-detector (Waters ® 2489, dual absorbance detector, USA), a binary pump (Waters ® 1525, Milford, MA, USA), automated sampling system (Waters ® 2707 Autosampler, USA). The system was monitored by "Breeze software". A RP, C18 analytical column (Macherey-Nagel 250 mm × 4. 6 mm, 5 µm) at 35 • C was used to analyze the drug. The mobile phase was consisted of 50:50 (v/v) of 0. 02 M ammonium acetate solution and methanol, where the pH of the ammonium acetate solution was adjusted to 3.
Differential Scanning Calorimetry (DSC) The differential scanning calorimetry was performed on the pure OLA, Soluplus ® , mannitol and lyophilized OLA-NCs-2 (with and without mannitol) by DSC-8000 (Perkin Elmer Instruments, Shelton, CT, USA) equipped with software Pyris V-11. Approximately 2-5 mg of weighed samples were hermetically sealed in aluminum pans. The DSC was calibrated with pure Indium (melting point 156. 60 • C and heat of fusion 6. 80 Cal/g). The DSC thermograms were obtained at the scanning rate of 10 • C•min -1 over the temperature range 40-280 • C under an inert atmosphere of N2 (flow rate was 20 mL•min -1 ).
Chromatographic Analysis Previously reported RP-HPLC method was used for the analysis of OLA in the plasma samples  [7, 20]. In brief, the HPLC system (Waters ® 1500 Series, Milford, MA, USA) was fitted out with UV-dual absorbance detector (Waters ® Model-2489), Binary pump (Waters ® Moel-1525), Autosampler (Waters ® Model-2707 plus). The HPLC-UV system was monitored and controlled by the "Breeze software". A C18 Waters symmetry column (150 × 4. 6 mm, 5 µm, particle size) was used for the elution of OLA and Paclitaxel (used as internal standard, IS)  [7]. The mobile phase composed of ammonium acetate buffer (pH was adjusted to 3. 5 by Orthophosphoric acid) and Methanol at 50:50 (v/v) was pumped at isocratic mode at the flow rate of 1 mL•min -1. The UV-detection wavelength was 254 nm (for OLA) and 230 nm (for IS).