dataesr/scientific-paragraphs-categorization huggingface dataset:
00 0. 25 0. 50 0. 75 1. 00 0. 00 0. 25 0. 50 0. 75 1. 00 Empirical Power A 0. 001% 0. 00 0. 25 0. 50 0. 75 1. 00 0. 00 0. 25 0. 50 0. 75 1. 00 Empirical Power B Sample size = 40 40 60 Asymptotic convergence 80 100 372/386 (total) reached for dearseq asymp Method dearseq asymp dearseq perm Wilcoxon dearseq asymp dearseq perm DESeq2 edgeR NOISeq limma-voom Wilcoxon Permutations first No normalization Normalization first 0. 001% 0. 01% 0. 1% 1% 5% 10% 20% 100% 0. 001% 0. 01% 0. 1% 1% 5% 10% 20% 100% 0. 01% 0. 1% 1% 5% 10% 20% 100% Empirical ('actual') FDR Nominal Rate: 1% Nominal Rate: 10% 0. 00 0. 25 0. 50 0. 75 1. 00 0. 00 0. 25 0. 50 0. 75 1. 00 Empirical Power A 0. 001% 0. 01% 0. 1% 1% 5% 10% 20% 100% 0. 00 Nominal ('claimed') FDR Figure S2: Empirical statistical power by method. Average over 50 semi-synthetic dataset No normalization Normalization first Sample size (per group) 4 5 6 7 8 9 10 12 15 20 40 60 80 100 372/386 (total) 5% nominal FDR No normalization Normalization first Sample size: 372/386 (total) Permutations first 0.
00 0. 25 0. 50 0. 75 1. 00 Figure 2: Supplementary materials 0. 001% 0. 01% 0. 1% 1% 5%10%20% 100% 0. 001% 0. 01% 0. 1% 1% 5%10%20% 100% 0. 001% 0. 01% 0. 1% 1% 5%10%20% 100% 0. 001% 0. 01% 0. 1% 1% 5%10%20% 100% 0. 001% 0. 01% 0. 1% 1% 5%10%20% 100% 0. 001% 0. 01% 0. 1% 1% 5%10%20% 100% 0. 001% 0. 01% 0. 1% 1% 5%10%20% 100% 0. 001% 4 5 6 7 8 9 10 12 15 20 40 60 80 100 372/386 (total) 4 5 6 7 8 9 10 12 15 20 40 60 80 100 372/386 (total) Method 1. 00 Nominal ('claimed') FDR Figure S1: Empirical FDR control against Nominal FDR level by method,. Average over 50 semi-synthetic dataset generated from the GTEx Heart atrial appendage VS Heart left ventricle data. 50% of the true Differentially Expressed (DE) genes are randomly sampled in each semi-synthetic dataset (i. e. 2,889 genes remains unpermuted as true positives) and considered as gold-standard DE genes.
50 0. 75 Empirical Power Method 0. 00 1. 00 No normalization 0. 75 1. 00 B Sample size = 40 Asymptotic convergence reached for dearseq asymp No normalization 0. 25 0. 50 0. 75 dearseq asymp dearseq perm DESeq2 edgeR NOISeq Empirical Power limma-voom generated from the GTEx Heart atrial appendage VS Heart left ventricle data. 50% of the true Differentially Expressed (DE) genes are randomly sampled in each semi-synthetic dataset (i. e. 2,889 genes remains unpermuted as true positives) and considered as gold-standard DE genes used as true positives. NB: for the empirical power to be interpretable, FDR control is warranted: thus edgeR and DESeq2 results should not be interpreted here. Panel A reproduces the results from Li et al. [1] Figure 2B as a function of sample size for both 1% and 10% nominal FDR levels, when all methods are applied to the same data (either without any normalization or when the data are first normalized before randomly swapping values to generate expressions under H 0 ).
Panel B study the impact of the nominal FDR level in both cases for the full sample size (372 and 386 samples in each group respectively). all three methods are applied to the same data (either without any normalization or when the data are first normalized before randomly swapping values to generate expressions under H 0 -i. e. the two cases for which the FDR is controlled and thus the empirical power is interpretable). Panel B study the impact of the nominal FDR level in both cases for the full sample size (372 and 386 samples in each group respectively). WilcoxonFigureS3: Empirical statistical power agianst empirical FDR by method. Average over 50 semi-synthetic dataset generated from the GTEx Heart atrial appendage VS Heart left ventricle data for the full sample size (372 and 386 samples in each group respectively).
Simulation Cohort and Treatment Effects The baseline cohort characteristics were sourced from a retrospective cohort study of insulin treated patients with T2D from the Kaiser Healthcare Delivery System and Diabetes Registry (Table  1 )  [12]. The cohort characteristics were computed using the weighted average of patients from each group (rt-CGM and SMBG). At baseline, all patients were receiving insulin treatment [including long acting, neutral protamin Hagedorn (NPH), rapid acting, short acting, and/or mixed insulin)  [12]. Additional risk factors were sourced from published data  [19] [20] [21]. The combined baseline cohort included 344 patients in the rt-CGM arm and 35,736 in the SMBG arm. The mean [standard deviation (SD)] age of the cohort was 64. 5 (12. 2) years, mean duration of diabetes was 15.
Data Availability. The datasets generated during and/or analyzed during the current study are not publicly available due confidentiality. Open Access. This article is licensed under a Creative Commons Attribution-NonCommercial 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. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
INTRODUCTION Hypertension is a worldwide health problem associated with mortality and cardiovascular (CV) morbidity, which are both further increased when diabetes is present. [3]  Contrary to ARBs, ACE inhibitors have been shown to reduce mortality and CV morbidity in placebo-controlled RCTs. 4  Metaanalyses indicated that ACE inhibitors and ARBs have potentially different effects on mortality reduction among hypertensive patients with/without diabetes. 5, 6 Another meta-analysis comparing ACE inhibitors to ARBs in a head-to-head manner  7  in patients with uncontrolled hypertension found no reliable difference for total deaths. However, data mostly derived from the ONTARGET trial. 8 E inhibitors and ARBs both inhibit the renin-angiotensin system (RAS) but have different sites of action. Whether clinically meaningful differences exist has been long debated.
Study design and setting We set up a nationwide population-based cohort with a 6-year followup period using French Health Insurance Data (SNDS). We identified through reimbursement claims a cohort of patients ≥50 years old who initiated in 2009 an ACE inhibitor or ARB regimen, free of overt cardiovascular disease and without ACE inhibitor or ARB in 2008. Patients were followed until the end of 2014, to collect information on major cardiovascular events or revascularization procedures or death. S3 ). Our goal was to identify a cohort patients treated in a primary prevention setting.
Bias Thanks to SNDS, no loss of follow-up and no missing data were anticipated. Exhaustive nationwide data also minimized selection bias. We identified first ACE inhibitor or ARB users and selected patients without clinically overt cardiovascular disease hence confounding by indication was minimized. In addition, we used propensity score to further reduce any difference in clinical characteristics. Cardiovascular events relied on hospital discharge diagnosis with detailed and unbiased information as ICD-10 codes were recorded timely and prospectively; in addition, we thought misclassification on outcome was minimized and at least nondifferential as previous exposure to ACE inhibitor or ARB was not taken in account in the coding process. Lastly, seeking for death used a thorough approach through a dedicated algorithm (Table  S9 ).
Of note, 25% had diabetes, 17% coronary artery disease, 4% stroke, and 2% heart failure. No subgroup analysis according to diabetes or history of cardiovascular disease was provided. Incidence rates of outcomes were higher than in our study, probably reflecting a higher baseline risk level. 11 sed on Taiwan's National Health Insurance Research Database for the period 2000-2010, patients with newly diagnosed diabetes (88% were hypertensive, 20% had CAD, 5% had HF, and 2% had PVD) and newly treated with ACEIs (n = 21 436) or ARBs (n = 30 777) were identified. 15 After one-to-one matching by propensity score, no significant differences in risk of myocardial infarction (hazard ratio [HR]. 92, 95%CI,. 80 to 1. 07), ischemic stroke (HR. 95, 95%CI,. 87 to 1. 04), or allcause mortality (HR. 95, 95%CI,.
89 to 1. 01) were found over a mean follow-up period was 6. 2 years. Subgroup analyses stratified by heart failure and coronary artery disease were consistent. A population-based retrospective cohort study (US claims database) of more than 87 000 insured Americans with diabetes (70% were hypertensive, 14% had a history of CVD, mostly IHD, 9. 8% had diabetes-related complications) found that ARB use relative to ACE inhibitor use was associated with a reduced risk of all-cause mortality or hospitalization (composite primary outcome) with a maximum follow-up of 6 years. 16 This finding was driven exclusively by reduced risk of hospitalization: relative risk for all-cause mortality was. 95 (95%CI,. 65 to 1. 40). Results were similar in patients with a baseline history of cardiovascular disease but no data were provided for patients without a baseline history of cardiovascular disease.
Patient selection diagram 73 457 patients had a hospitalization for cardiovascular events before inclusion, 37 104 a hospitalization for cancer, 1962 a hospitalization for chronic kidney disease, and 11 657 a hospitalization for two or three of the above conditions 2. 8 Data access and cleaning methods Data access was operative in June 2021. Investigators had full access to the extracted database from SNDS, which was used to create the study population. Extracted database was stored locally in a dedicated and secured data center: extraction was performed by CNAMTS; csv data files were imported into MySQL database with a physical data model consistent to the SNDS original database design. Some metrics and visual tools were used to check data completeness and adequacy to expected data extraction: metrics included number of extracted patients (compared to expected number), stability of reimbursement frequencies over time in order to validate data completeness at a population level.
F I G U R E 1 1 Patient selection diagram 73 457 patients had a hospitalization for cardiovascular events before inclusion, 37 104 a hospitalization for cancer, 1962 a hospitalization for chronic kidney disease, and 11 657 a hospitalization for two or three of the above conditions 2. 8 Data access and cleaning methods Data access was operative in June 2021. Investigators had full access to the extracted database from SNDS, which was used to create the study population. Extracted database was stored locally in a dedicated and secured data center: extraction was performed by CNAMTS; csv data files were imported into MySQL database with a physical data model consistent to the SNDS original database design. Some metrics and visual tools were used to check data completeness and adequacy to expected data extraction: metrics included number of extracted patients (compared to expected number), stability of reimbursement frequencies over time in order to validate data completeness at a population level.
| MATERIAL S AND ME THODS The data were collected as part of a retrospective single-center, cross-sectional, observational study conducted at the maternity ward of the Regional University Hospital Center of Nancy (level III). This data collection was reported as required by French law to the National Data Protection Authority (CNIL, no. A2017-3). The study design and the presentation of the results meet the 2015 STARD (Standards for Reporting Diagnosis Accuracy Studies) guidelines. Under the STARD procedure, the 16-point score was regarded as a diagnostic tool to predict EFW validity. The data came from the obstetrical records of women who gave birth between February 16, 2019, and January 24, 2020. The inclusion criteria required the performance of an obstetrical ultrasound in the 7 days before delivery, with complete biometric measurements including head circumference (HC), abdominal circumference (AC), and femur length (FL).
The exclusion criteria were perinatal death, the absence of birth-weight data, and births with three or more fetuses. Cases were included consecutively from the birth register. The following data were compiled from the hospital files: date of examination and gestational age on that date, maternal body mass index, the ultrasound device model, quantity of amniotic fluid (oligohydramnios, physiologic, or polyhydramnios), examination conditions (satisfactory, intermediate, or difficult, assessed by the operator), and singleton or twin pregnancy (with fetuses identified as A or B for the latter). For each examination, the EFW was established with the Hadlock three-parameter formula. 28 Screen shots of the biometric images captured (HC, AC, and FL) were also collected. When more than one examination had been performed during the 7 days before delivery, the data for each were collected separately and independently.
Online Collection of Oral Nicotine Pouches Coders (YS and JZ) searched online stores in February 2021 to identify oral nicotine pouch products and brands on the market during the Reddit data collection period and to inform the hand coding of the Reddit posts. Information about available oral nicotine pouch products, including brand names, product names, and flavors, was collected from online stores like "nicokik. com" and "snusdirect. com. " The nicotine strengths, when available, were also included.
Limitations The generalizability of these results is subject to several limitations. First, the paucity of noncommercial posts limited the scope of this study. Due to the fact that oral nicotine pouches are emerging new tobacco products, only 653 posts were included in our analysis during our study period from February 15, 2019, to February 12, 2021. Thus far, there have been limited analyses of social media data on oral nicotine pouches. Our research provides a first and up-to-date insight into the current public perception and popular topics regarding oral nicotine pouches discussed on Reddit. Although this is a start, further research is needed due to the substantial growth of the oral nicotine pouch market. Our study only discusses the broad health symptoms mentioned in the Reddit posts.
Background: Based on PROfound, olaparib is approved for patients with metastatic castration-resistant prostate cancer following disease progression on at least enzalutamide or abiraterone and who carry relevant alterations in DNA repair genes. To facilitate continued olaparib treatment as long as the patient derives benefit, we describe further safety assessments from PROfound focusing on the four most common adverse events (AEs) and events of special interest. Methods: Patients were randomized (2:1) to olaparib tablets (300 mg bid) or control (enzalutamide or abiraterone) until disease progression or unacceptable toxicity. Safety was assessed through AE reporting and laboratory assessments. Safety data were also collected from all patients in the control group who experienced radiographic disease progression and subsequently crossed over to olaparib treatment.
The primary endpoint of radiographic progressionfree survival in Cohort A was assessed by blinded independent central review. Safety and tolerability in the overall population (Cohorts A and B) were assessed through adverse-event reporting according to the Common Terminology Criteria Adverse Event (CTCAE v4. 03). Patient safety assessments were scheduled on Day 1 of treatment and at weeks 4, 8, 12, 16, 20, 24 and then every 8 weeks and also on discontinuation. A safety follow-up was conducted 30 days after the last dose and any new event identified was followed to resolution. Safety data were also collected from all patients in the control group who experienced radiographic disease progression and subsequently crossed over to olaparib treatment. With specific relevance to the data we present, AEs related to olaparib could be managed by dose interruptions and reductions.
Statistical analysis The safety population comprised all patients in receipt of at least one dose of study treatment. Safety data were analysed descriptively (data cut-off, 20 March 2020). Anaemia is reported as a 'grouped term' based on Medical Dictionary for Regulatory Activities (MedDRA)-preferred terms and includes anaemia, decreased haemoglobin level, decreased red-cell count, decreased haematocrit level, erythropenia, macrocytic anaemia, normochromic anaemia, normochromic normocytic anaemia and normocytic anaemia. Similarly, pneumonitis is reported as a grouped term and includes pneumonitis, interstitial lung disease and radiation pneumonitis. For reporting of the number of patients with AEs, patients with multiple events in the same preferred term are counted only once in that preferred term.
SYSTEMATIC REVIEW OF THE LITTERATURE We performed a systematic review of the current medical literature (Figure  1 ). We searched NCBI's PubMed database on 14/04/2022 using the query ["belatacept" AND (kidney OR renal)] and identified 475 citations. We assessed all corresponding abstracts. We retrieved 404 articles on belatacept in kidney transplantation, among which 160/404 (39. 6%) were not original studies (i. e. , reviews, experts' opinions, comments, responses, etc. ). We retrieved 80 basic science studies and 164 clinical studies. Among clinical studies, 90/164 (54. 9%) were noncomparative studies, meaning that no comparison was made between belatacept and other treatments.
Rejection In 9 trials out of 11, belatacept was associated with a higher rate of rejection compared with CNI. Statistical comparisons were not systematically performed in these studies but, since this difference is consistently observed across trials in various settings, it most likely reflects a true difference. Based on data from BENEFIT studies, we can estimate that, in de novo KTRs receiving an induction with basiliximab, the risk for biopsy-proven rejection within 7 years following KT using a kidney from an SCD is approximatively 15% higher with belatacept than with cyclosporin A; and 60% higher when using a kidney from an ECD. Based on data from two RCTs  (7, 8) , we can estimate that in stable KTRs receiving CNI for more than 6 months, the risk for biopsy-proven rejection within 2-3 years following a switch from CNI to belatacept is increased by approximatively 100-150%, compared to remaining on CNI.
NON-RANDOMIZED STUDIES Hereafter, we will review non-randomized clinical studies involving belatacept in KT. We will only consider studies that add significant contribution to data from RCTs, either because they strongly comfort their findings or because they make new ones. We will distinguish between comparative (i. e. , where there is a control group of CNI-treated patients) and non-comparative studies. Among comparative studies, we will distinguish between those providing an adjusted analysis (i. e. , with statistical methods to consider selection bias between groups) and those providing none (i. e. , crude comparison between groups).
There is evidence that belatacept is associated with an increase in risk for CMV-disease. Due to this increased risk, patients on belatacept should be closely monitored, especially within the first year after initiation. Belatacept is associated with a reduced response rate to anti-SARS-CoV2 mRNA-based vaccination. Most patients included in control groups in RCTs on belatacept received cyclosporin A, which is not the current gold standard for KTRs. Consequently, belatacept has not been routinely used in KTRs since its approval. Therefore, since few KTRs received belatacept since its approval, data from observational post-approval studies are of poor quality, with small sample sizes and are mostly non comparative, adding very few significant information. As of 2022, most questions on belatacept (and all the important ones) are unanswered from an evidence-based medicine perspective.
Material And Methods Bibliographic research: an extensive search for studies published before April 15th, 2022, was performed using four databases: PubMed, Web of Science (WoS), Science Direct. Keys words of interest were "knowlesi" and "indonesia". Search details are as follows: "knowlesi"[All Fields] AND ("indonesia"[MeSH Terms] OR "indonesia"[All Fields] OR "indonesia s"[All Fields] OR "indonesias"[All Fields]) for PUBMED; knowlesi (All Fields) and Indonesia (All Fields) for Web of Science; knowlesi AND Indonesia for Science Direct. No lter or date limitations were used. To increase the completeness of the search, a snowballing approach was used and duplicates were removed. The literature research was complemented using google scholar which several articles written in Bahasa Indonesia found through Indonesian journal portals.
The remaining references were assessed for eligibility based on their title, abstract, and full text. Inclusion and exclusion criteria were established a priori. All records relative to Plasmodium knowlesi (P. knowlesi), its hosts (human and non-human), and related malaria cases were included. Records with the following exclusion criteria were not included: P. knowlesi studies not related to Indonesia (cases, hots, vectors), studies with data redundancy, experimental studies not related to P. knowlesi malaria cases, malaria studies involving only other Plasmodium species, studies not speci cally related to malaria, studies written in a language other than English, French and Bahasa Indonesia, and studies with unavailable abstract and/or full text. Data management: epidemiological, clinical and biological data were extracted from the collected studies by four authors (IBS, YIK, RO, and SP).
Diagnosis of P. knowlesi malaria The rst case of natural infection of P. knowlesi in Indonesia reported in 2010 was diagnosed by nested PCR  [16]. Plasmodium knowlesi infections reported in 2015 were initially diagnosed as P. vivax by microscopy, before PCR analysis revealed them to be P. knowlesi  [17]. In the same sampling area and at the same data collection time, one case of P. knowlesi malaria was reported in North Barito Regency, and was diagnosed by PCR  [18]. In Jambi province, one of 34 cases was con rmed as P. knowlesi using nested PCR targeting the Small Subunit ribosomal RNA (SSU rRNA) gene  [19]. In Aceh province, 20 positive cases of P. knowlesi, initially diagnosed as P. falciparum, P. vivax, or P. malariae, were identi ed by nested PCR in 2016. Two years later, 19 cases of knowlesi malaria were reported after detection by PCR in the same place and the same year of data collection  [20, 21].
Acknowledgements The authors thank the  Institut Français d'Indonésie (IFI)  and the  Institut National des Sciences Appliquées (INSA) de Lyon. The authors thank  Prof. Nicolas GASCOIN ,  Head of the Cooperation O ce for Science & Technology, French Embassy in Indonesia , and  Prof. Abdoulaye Djimdé ,  Malaria Research and Training Center, University of Bamako, Mali. This article was produced by WANECAM 2 which is part of the EDCTP2 programme supported by the  European Union  grant number  RIA2017T-2018 WANECAM-2. The views and opinions of authors expressed herein do not necessarily state or re ect those of EDCTP.