dataesr/scientific-paragraphs-categorization huggingface dataset:
The study protocols, amendments, and patient informed consent were reviewed by a national, regional or independent ethics committee or institutional review board. BE VIVID (NCT03370133) and BE READY (NCT03410992) were conducted in accordance with the current version of the applicable regulatory and International Conference on Harmonisation (ICH)-Good Clinical Practice (GCP) requirements, the ethical principles that have their origin in the principles of the Declaration of Helsinki, and the local laws of the countries involved. Availability. Data sharing from nonclinical studies is outside of UCB's data sharing policy. However, underlying data from the BE VIVID (NCT03370133) and BE READY (NCT03410992) phase 3 clinical trials reported in this manuscript may be requested by qualified researchers six months after product approval in the US and/or Europe, or global development is discontinued, and 18 months after trial completion.
Investigators may request access to anonymised individual patient-level data and redacted trial documents which may include: analysis-ready datasets, study protocol, annotated case report form, statistical analysis plan, dataset specifications and clinical study report. Prior to use of the data, proposals need to be approved by an independent review panel at www. Vivli. org and a signed data sharing agreement will need to be executed. All documents are available in English only, for a prespecified time, typically 12 months, on a password protected portal. Open Access. This article is licensed under a Creative Commons Attribution-Non-Commercial 4. 0 International License, which permits any non-commercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
ACKNOWLEDGEMENTS The authors thank the patients, the investigators and their teams who took part in these studies. Funding. This study was sponsored by  UCB Pharma, Brussels, Belgium. This article was based on the original studies BE VIVID ( NCT03370133 ) and  BE READY  ( NCT03410992 ), sponsored by  UCB Pharma. The Rapid Service Fee was funded by  UCB Pharma. RBW is supported by the  Manchester NIHR Biomedical Research Centre. Authorship. All named authors meet the International Committee of Medical Journal Editors (ICMJE) criteria for authorship of this article, take responsibility for the integrity of the work as a whole, and have given their approval for this version to be published.
The same plasma pool was used for the standard curve for both cohorts. Data for KMZ83376. 1 and PVX_095055 were not analyzed for the Thai cohort due to a technical issue with the IgM standard curves for the Thai plates. The IgM standard curves had a low starting MFI at the 1/50 dilution of the positive control plasma pool and thus exhibited a plateau in signal from S7-S10, which is not ideal for the standard curve conversion. This issue was subsequently rectified (by generating a new batch of coupled beads), and thus these 2 proteins were assessed in the Brazilian samples. IgG antibody responses against the same P. vivax proteins had previously been measured in all samples as described  [3]. Individuals from the malaria-endemic cohorts were defined as either (i) infected with P. vivax within the 9 months before antibody measurements or (ii) not infected with P.
Acknowledgments We gratefully acknowledge the extensive field teams that contributed to sample collection and qPCR assays, including  Wang Nguitragool ,  Andrea Kuehn ,  Yi Wan Quah ,  Piyarat Sripoorote , and  Andrea Waltmann. We thank all the individuals who participated in each of the studies, and we thank the Australian and  Thai Red Cross  for donation of plasma samples. We thank the  Volunteer Blood Donor Registry at WEHI  for donation of plasma samples and  Lina Laskos  and  Jenni Harris  for their collection and advice. We thank  Christopher King  ( Case Western Reserve University ) for provision of the PNG control plasma pool. We thank  Fumie Matsuura  ( CellFree Sciences ) and  Christele Huon  ( Institut Pasteur ) for contributing to expression of proteins. Financial support. This work was supported by the  National Health and Medical Research Council Australia  (# 1092789 , # 1134989 , and # 1043345  to I.
M. , # 1143187  to W. -H. T. , and # 1173210  to R. J. L. ), the  National Institute of Allergy and Infectious Diseases (NIH  grant  5R01 AI 104822  to J. S. ), and the  Global Health Innovative Technology Fund  ( T2015-142  to I. M. ). The Brazilian team was partly funded by  Fundação de Amparo à Pesquisa do Estado do Amazonas-FAPEAM  ( PAPAC 005/2019  and  Pró-Estado ). M. L. and W. M. are research fellows from  CNPq. Additional funding directly supporting field studies was from the  TransEPI consortium  (supported by the  Bill and Melinda Gates Foundation ). We also acknowledge support from the  National Research Council of Thailand. This work was made possible through  Victorian State Government Operational Infrastructure Support and Australian Government NHMRC IRIISS. The  Walter and Eliza Hall Institute of Medical Research , through the  Page Betheras Award  to  R.
However, these studies mostly focused on ESBL-p Enterobacteriaceae in community or hospital-acquired infections, and based their estimation on a restricted number of cases, with monocentric designs  [9] [10] [11]. The importance of "real-world data", derived from registries, electronic health records or administrative databases, that complement data from conventional clinical trials in regulatory decision making process, has been recently emphasized  [19]. In France, the national health data system (SNDS) provides access to all hospitalizations, drug dispensing and medical care  [20]. Since 2014, antibiotic resistance is identifiable among infections in hospitalized patients, thus allowing to study resistant bacteria in hospital  [21, 22]. This study aims to assess the risk factors for infection due to ARB in patients hospitalized for a urinary tract infection, using data from the SNDS.
Data source and collection The SNDS is a comprehensive anonymized database composed of the national hospital discharge database (PMSI, Programme de médicalisation des systèmes d'information), chained with individual patient information and all French outpatient healthcare refund using a pseudomysed identification number, which enables to follow a patient and to identify some co-morbidities  [20]. Supplement S1 provides a detailed description of available information (Additional file 1). Incident hospital admissions for UTI in adults (≥18years-old), occurring between Jan 1st, 2015 and  Dec 31, 2017 , and due to an Escherichia coli, Klebsiella pneumoniae, Staphylococcus aureus, Pseudomonas aeruginosa or Enterococci were identified from the PMSI database, using codes lists established in collaboration with infectious diseases specialists (Additional file 1, Table  S1 ).
Risk factors The following potential risks factors were extracted from the linked databases: i) outpatient antibiotic exposure; ii) urinary tract conditions; iii) associated health conditions; and iv) for HCAI, previous hospitalization in an intensive care unit (ICU). Identification of several factors required using an algorithm combining different codes (diagnoses from hospitalizations, chronic disease, drug consumption, laboratory tests or surgical procedures) (See Additional file 1, Supplement S2). In the database, diagnoses and chronic diseases are coded using the 10th revision of the International Statistical Classification of Diseases and Related Health Problems (ICD-10)  [21]. Drugs are identified through two classification systems: anatomical therapeutic chemical (ATC) and French presentation identification code (CIP, Code Identifiant de Présentation) systems, which provides further details about the drug, such as conditioning.
The same methodology was applied for studying the impact of the class of antibiotics delivered in the previous 3 months. Because of the large size of the sample, crude (ORc) and adjusted (ORa) odds ratios are reported with their 95% confidence intervals (95% CI) without providing pvalues  [24]. Since risk factors might differ according to bacteria or infection site, interactions between these characteristics and risk factors were tested in the different models. Statistical analyses were computed with SAS Enterprise Guide (v 7. 13 software, SAS Institute Inc. , Cary, NC, USA). Data extraction and analysis were approved by the French Data Protection Agency (CNIL, approval DE-2016-176). All methods were performed in accordance with CNIL regulations and with REporting of studies Conducted using Observational Routinely-collected Data (RECORD) guideline.
Discussion This is the first time that risk factors of having UTI with antibiotic-resistant bacteria are examined using the exhaustive French medico-administrative database. Outpatient dispensing of antibiotics within the past 3 months and especially of broad-spectrum antibiotics were consistent risk factors of acquiring UTI caused by an ARB across all categories. The risk declined when the time lag between exposure and infection exceeded 3 months. A urinary tract procedure performed within the last 3 months also increased the risk for women with HCAIs and for men with CAIs. Lastly, long stays in ICU within the last 3 months also increased the risk for men with HCAIs. Prior exposure to antibiotics has been found to increase the risk of resistance in several studies, both for community and hospital infections  [8, 9, [11] [12] [13] [14] [15] [16] [25] [26] [27] [28].
A study found renal disease and cancer as risk factors when comparing patients affected with ESBL-p Escherichia coli UTI with patients with no UTI  [13]. However, when comparing to patients with susceptible Escherichia coli, no difference was found regarding renal disease and cancer, and a slightly increased risk with immunosuppressive treatment. Another study reported an association between ARB and immunosuppression in communityacquired UTI, but not with diabetes or pregnancy status  [12]. Using a medico-administrative database involves some drawbacks  [22]. The two main limitations of the database used are the lack of information on residency in skilled nursing home and on drug administration during hospitalizations. First, because residency in a nursing home was not available, the potential role of this previously identified risk factor  [10, 14]  could not be examined.
Second, because antibiotic delivered during hospitalizations were not recorded in the database, prior antibiotic exposure was identified only through dispensing in the community. However, we tested the hypothesis that each hospitalization with an infection was associated with antibiotic administration, in addition to community exposure. A moderate increase of antibiotic exposures was noted, but no difference was found concerning odds ratios (data not shown). In addition, it is important to note that, in this work, antibiotic consumption was studied using dispensing. Thus, it was assumed that patients actually consumed the antibiotics delivered and that exposure started on the date of dispensing. Third, identification of some co-morbidities required to use algorithms, combining several types of information.
However, the risk of misclassification should be similar for cases and controls and most of the algorithms used were constructed by the Cnam organism, reviewed by experts of each field, and then improved by Cnam organism when necessary. They are freely available to all, which provides standardized definitions for SNDS users. A study estimated the sensitivity of the algorithm for identifying diabetic patients to range from 93 to 99%  [32]. Finally, it should be pointed out that CAIs were defined in patients not hospitalized in the past 3 months, which may incur some misclassification of patients. Indeed, a patient with CAI may have nevertheless been exposed to medical environment, ambulatory care surgical procedure, and home care or nursing facilities that were not easily identifiable in the database.
Conversely, a patient hospitalized within the past 3 months may still have a community-acquired infection. As expected however, HCAIs affected older patients, having longer stays than those with CAI, and were more frequently caused by nosocomial pathogens, such as Klebsiella pneumoniae, Pseudomonas aeruginosa or Staphylococcus aureus. Despite its limitations, the large nationwide and comprehensive SNDS database remains a remarkable tool for analysing risk factors. It covers all hospitalizations in France, and all reimbursed drugs dispensed by pharmacy (including antibiotics), as well as biological exams. Hospital diagnosis were validated by Afshar et al. , who showed that ICD-10 codes allow to analyze pathogens and resistance markers, and Salhi et al. showed a good coding of infection, with a positive predictive value between 0.
98 [0. 65-1. 00] and 0,93 [0. 88-0. 98] [33, 34]. Thanks to the access to a wide range of routinely collected detailed information, the SNDS database has been used by other authors to study incidence and prevalence of disease, as well as identify risk factors  [20, [35] [36] [37] [38] [39]. Our study is the first concerning risk factors of antibiotic resistance in SNDS. The large population included allowed us to stratify the analysis on location of acquisition and sex, and to include a variety of bacteria and resistance codes, unlike most of the studies that focused on ESBL-producing or MDR Enterobacteriaceae UTIs.
ACKNOWLEDGMENTS The author declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. J. S was supported by the  University of Montpellier , the  Centre Hospitalier Universitaire (CHU) de Montpellier and Sidaction  ( 2018-1-FJC-11994 ). Conceptualization of the article and writing of the original draft was done by J. S and N. C; writing, reviewing and editing the final version was done by JM. M and A. G.
3 (Clara vision, Ulis, France). Imaging and statistical analysis of individual lanes of the gel images were straightened using Image Quant TL software V. 2003 (Amersham Biosciences, USA). Banding patterns were standardized with two reference patterns included in all gels, the rDNA amplification products from the pure bacterial strains of Escherichia coli and Lactobacillus plantarum. This software helped to identify the bands and compare their positions with standard patterns. The DGGE fingerprints were scored by the presence and absence of co-migrating bands, independent of intensity. Pairwise community similarities were quantified using the Dice similarity coefficient (SD)  [14]  as follows: SD = 2Nc/Na + Nb  (1)  where Na represents the number of bands detected in sample A, Nb the number of bands in sample B, and Nc the number of bands common to both samples.
The similarity index was expressed within a range of 0 (completely dissimilar) to 100 (perfect similarity). The data generated, obtained were exploited for the main component analysis (CPA) by Statistica software (Version 7. 1), and for the hierarchical ascending classification CAH by XLStat (Version 2014). For the identification of bacterial community, the DNA bands on DGGE gels were then excised with a sterile scalpel, eluted in 50 μL sterile ultrapure water and stored overnight at 4 °C. These extracted DNA were amplified again with non GC-clamp primers. The PCR products were sent to GATC Biotech (Germany) for a second DNA purification and sequencing. Research for similar sequences of 16S rDNA were performed using the basic local alignment search tool (BLAST) in GenBank ( http://www. ncbi.
Transcriptome datasets of FAB subtyped AML RHOH expression was analyzed in the transcriptomes of FAB subtyped de novo AML acquired by five microarray studies and one RNA-seq study. The microarray datasets were from the multi-center ALFA-0701 study  [43] [44] [45] , the Gene Expression Omnibus identifiers GDS1064  [46] , and GDS3057  [47]  for AML samples with all karyotypes and GDS3312 and GDS3329 for AML samples with a normal karyotype  [48]. All these microarray datasets were produced using Human Genome U133 Plus 2. 0 Affymetrix arrays containing the RHOH Affymetrix probe 204951_at and signal normalization either by robust multi-array averaging or application of a variance stabilizing algorithm. The RNA-seq datasets analyzed for RHOH expression spanned The Cancer Genome Atlas identifiers TCGA.
Flow cytometry Phenotypic validation of monocytes isolated from healthy donors was performed by flow cytometry analysis using the PE-conjugated anti-CD14 mouse monoclonal antibody TÜK4 (Miltenyi Biotec, SAS, Paris, France), the FITC-conjugated anti-CD3 mouse monoclonal antibody UCHT1 (Becton Dickinson France, SAS, Le Pont de Claix, France) and the PE-conjugated anti-CD19 mouse monoclonal antibody J3. 119 (Beckman Coulter France, SAS, Villepinte, France). All FACS analyses were performed using a Cyan ADP flow cytometer equipped with Summit 4. 3 software (Beckman Coulter France, SAS, Villepinte, France).
(A-D) Regression curves of the means of relative RHOH mRNA levels as a function of FAB subtype established from the NCBI GEO Datasets GDS1064  [46]  and GDS3057  [47]  composed of AML patients with all karyotypes and GDS3312 and GDS3329 composed only of AML patients with a normal karyotype  [48]. (E) Regression curves of the means of relative RHOH mRNA levels as a function of FAB subtype established from microarray analyses of AML patients recruited by the ALFA-0701 study (ALFA)  [43] [44] [45]. (F) Regression curve of the means of relative RHOH mRNA levels as a function of FAB subtype established from normalized RNA-seq data generated by The Cancer Genome Atlas (TCGA)  [45, 49]. The GDS DataSets as well as the ALFA-0701 and TCGA data all reveal a linear decrease in RHOH mRNA expression as differentiation stage progresses from AML FAB subtype M0 to M5 (R 2 values are presented at the bottom left of each panel in black text).
Further analysis demonstrated that this process of terminal differentiation is also characterized by the induction of RhoH protein expression (Figure  4A, B ). Consistent with this induction, expression of JunD that represses RHOH gene transcription is down-regulated (Figure  4A, B )  [14]. This same reciprocal change in expression of JUND and RHOH is found when monocytes are induced to differentiate into mature macrophages and subsequently M1 polarized (GEO Series accession number GSE5099)  [56]. Furthermore, when the monocytic cell line THP-1 is differentiated into mature cells with functions of macrophages the induction of RHOH and the concomitant repression of JUND are also observed (GEO Series accession number GSE53691)  [57].
RHOH induction is associated with acquisition of a mature myeloid phenotype and alters expression of genes controlling Cdc42 and Wnt signalling In order to gain insight into the molecular mechanisms by which the induction of RHOH might drive monocyte to macrophage differentiation, we compared the transcriptome of OCI-RhoH with that of OCI-Empty. This analysis identified 72 transcripts that were significantly altered in their expression upon RHOH induction (GEO Accession number GSE138479). Literature searches revealed that 42 of these 72 transcripts encode proteins or represent long non-coding RNAs implicated in regulating functions characteristic of mature myeloid cells (Table  1 ). These functions appear to center on two main areas. The first is the control of processes essential to the journey of mature myeloid cells to their tissue sites of action such as adhesion, directional move-ment, and remodeling of the extracellular matrix.
NRAS, TP53, and NPM1 genes. These findings suggest that targeting abnormal RHOH expression might have therapeutic efficacy in a broad range of AML patients. Apparently contradictory to our findings, Iwasaki et al found no significant differences in RHOH transcript levels between FAB subtypes. However, further scrutiny of their results in light of ours indicates we are in agreement. Iwasaki et al analyzed 1 patient classified as FAB F I G U R E 5 Regulators of Cdc42 and Wnt signalling controlled by RHOH. Differential microarray analysis identified 72 transcripts that are significantly changed in their expression when RHOH is reconstituted in the M4 AML cell line OCI-AML3 (GEO Accession number GSE138479). Of these transcripts, 13 encode proteins and 1 the long non-coding RNA RMRP known to regulate Cdc42 and/or Wnt signalling (Supplemental References 2).
RHOH reconstitution inhibits the growth of AML tumours in vivo. The AML cell line OCI-RhoH that stably expresses the RHOH vector pMEP-RhoH and its sister line OCI-Empty that stably expresses the same vector empty of RHOH were injected subcutaneously into SHrN ™ Hairless NOD. SCID mice (Harlan Laboratories, Inc. , Indianapolis, Indiana, USA). In one experiment 5 million OCI-Empty cells were injected into 8 mice and in parallel 5 million OCI-RhoH cells were injected into 7 mice. In a second experiment 10 million OCI-Empty or OCI-RhoH cells were each injected into 5 mice. The volumes of palpable tumours were measured 3 weeks after injection. Tumour volumes beyond 1. 5 times the interquartile ranges of the ordered OCI-Empty and OCI-RhoH datasets were removed (Supplemental Table 1). Histograms represent the means ± SEM of the remaining volumes (OCI-Empty n = 12; OCI-RhoH n = 11).
In addition, our microarray data indicate that low RHOH expression results in high expression of the long non-coding RNA BASP1-AS1 (lnc-MYO10-1) and low expression of the long non-coding RNA SNHG5 (GEO Accession number GSE138479). These expression patterns could cause BASP1 repression through increased sponging of BASP1 mRNA by BASP1-AS1 and increased expression of microRNA 23a, 154, 182, and 377 through decreased sponging by SNHG5. Repression of BASP1 and induction of each of these microRNAs have been linked to the pathogenesis of AML [63-69].
ACKNOWLEDGMENTS S. G. Z. and C. S. S. performed the research, designed the research study, contributed essential reagents, analyzed the data, and wrote the paper; Q. F. , C. C. L. , N. P. , F. L. , M. F. , and H. E. B. performed the research; M. C. and B. Q. contributed essential tools and analyzed the data. We thank  Elisabeth Bertrand ,  Nathalie Jouy ,  Emilie Floquet , and  Anne Trinh  for technical support. We also thank  Celine Villenet  for excellent assistance in performing differential microarray experiments. This work was supported by the  Ligue Nationale Française Contre le Cancer Comité du Pas de Calais, SIRIC Onco Lille  grant number  INCa-DGOS-Inserm 6041aa , the  Kabara Cancer Research Institute , the  Institut pour la Recherche sur le Cancer de Lille , and the  Institut National de la Santé et de la Recherche Médicale.
FLT3, NRAS, TP53, and NPM1 genes. These findings suggest that targeting abnormal RHOH expression might have therapeutic efficacy in a broad range of AML patients. Apparently contradictory to our findings, Iwasaki et al found no significant differences in RHOH transcript levels between FAB subtypes. However, further scrutiny of their results in light of ours indicates we are in agreement. Iwasaki et al analyzed 1 patient classified as FAB F I G U R E 5 Regulators of Cdc42 and Wnt signalling controlled by RHOH. Differential microarray analysis identified 72 transcripts that are significantly changed in their expression when RHOH is reconstituted in the M4 AML cell line OCI-AML3 (GEO Accession number GSE138479). Of these transcripts, 13 encode proteins and 1 the long non-coding RNA RMRP known to regulate Cdc42 and/or Wnt signalling (Supplemental References 2).