dataesr/scientific-paragraphs-categorization huggingface dataset:
Microbiologic analysis Positive blood culture was frequently reported in patients with AC (n = 252/373, 68%). Bile culture was only performed in 189/375 patients and was positive in 79% of the cases (149/189). When blood and bile were simultaneously collected (n = 189), both culture samples were positive in 56% of the cases (n = 106/189). Microbiological data are summarized in Table  3. In blood or bile culture, the most frequently identified bacteria were Gram-negative bacilli (68. 5%) and Gram-positive (24. 5%). Anaerobes (4. 5%) and fungi (2. 5%) were relatively uncommon. The most frequently identified bacteria were Escherichia coli (38%), Klebsiella pneumoniae (9%), Enterococcus faecalis (8%), Enterococcus faecium (7. 5%), Enterobacter cloacae (6%) and Pseudomonas aeruginosa (5%). Finally, an infection caused by Extended spectrum beta lactamase (ESBL)-producing bacteria was observed in 44 AC patients (12%).
Our study should be interpreted in the light of its strengths and limitations. We have included patients from 11 ICUs across several geographic regions in France over a 14-year period, making this study the largest to date on patients with acute cholangitis managed in ICUs. The observed results are thus widely generalizable. Regarding its limitations, this study being an observational retrospective study, only association and not causality can be inferred. Given that this study was observational, we cannot conclusively exclude sources of selection bias. Finally, some data are lacking such as the time between symptom onset and ICU admission and also the causes of delayed biliary decompression.
Baseline data We approximated the population baseline in our study year (2013) with the nearest census data. We obtained county-level demographic data, including population counts stratified by sex, age and mortality rate, from the 2010 population census of the People's Republic of China (2010 census). The community-based mortality counts were screened in the 2010 census data and mainly reflected the total deaths that occurred between 1 November, 2009, and 31 October, 2010. The raw mortality rates were available at the county level, and the specific mortality rates were reported by sex and by age at the national level. To calibrate the potential confounding effects of demographic factors on the baseline risk, we calculated the sex-and age-adjusted mortality rates using an indirect method  (Anderson and Rosenberg 1998)  in each county (Figure  1 ).
PM 2. 5 concentrations We estimated the daily mass concentrations of PM 2. 5 with a spatial resolution of 0. 1° across China in 2013. Briefly, the products combined the in-situ observed, satellite-derived and CAMQ-simulated PM 2. 5 using a data fusion approach. The details of the approach and the related datasets are presented in  Xue et al. (2017). The predicted daily PM 2. 5 concentrations were consistent (R 2 = 0. 78) with the out-of-sample cross-validated observations of in situ PM 2. 5 concentrations. Root mean squared error (RMSE) is an unbiased estimator for modelling error and was reported as 29. 5 μg/m 3. Figure  S1  presents more details of the cross-validation. The gridded daily maps of PM 2. 5 concentrations were further averaged by the county-level administrative boundaries to matched with the demographic data geographically.
In our previous study  (Xue et al. 2017) , we derived the optimal daily estimates of PM 2. 5 throughout China by combining monitoring, satellite remote sensing, and air quality modelling data, which reduced the uncertainty in the risk assessment. Our study quantified the mortality from PM 2. 5 mixtures by individual compositions for the first time; the results revealed risk distributions between populations that differed from conventional risk assessments that were based on PM 2. 5 mass alone. Nonetheless, our findings were weakened by the large uncertainty from the use of multiple datasets and complex models. First, because of the limited number of epidemiological studies on PM 2. 5 compositions, the 95% CIs were wide, especially for EC and OC. Second, although validation against the observation has been made in this study, uncertainties in emission inventories, meteorological predictions, and the formulations and parameterizations used in the chemistry transport model affect the accuracy of the results.
Third, due to the limitations in extant epidemiological evidences, the toxicities for some key components of PM 2. 5 are still unknown. For instance, our analysis ignored the specific association between mortality and ammonium, the major component for the PM 2. 5 from agricultural sector. In our RAMP model, the exposure-response function for ammonium was approximated as equal to that derived from studies on total PM 2. 5. We estimated that agricultural sector contributed to 0. 026 (95% CI: 0. 002, 0. 049) million premature deaths, approximately accounting for 6. 2% of the total PM 2. 5 -related deaths, which might be biased due to the ignorance of ammonium-specific toxicity. Last but not least, RAMP analysis depends on the correlation matrix of PM 2. 5 constituents as input data, which should have been sourced from original epidemiological studies but was replaced by an estimate from our previous dataset.
To compensate for the residual stochastic noise, we develop a three-way haplotype method isolating true interactions. The fidelity of the method is confirmed analytically and on simulated genetic sequences evolved with a known epistatic network. The method is then applied to a large sequence database of neurominidase protein of influenza A H1N1 obtained from various geographic locations to infer the epistatic network responsible for the difference between the pre-pandemic virus and the pandemic strain of 2009. These results present a simple and reliable technique to measure epistatic interactions of any sign from sequence data.
Although they are not actually independent, but represents parts of one metapopulation, this is the best approximation one can obtain in any study working with world pandemics data. We have used about 8000 sequences found in the cited database for the period 2000-2010 from various geographic locations. Our goal was to understand the difference between the strains before and after pandemic of 2009, which have 80% of mutual homology. We wished to infer only the epistatic sub-network related to that difference. For this end, we compared worldwide samples of sequences from the two strains. We randomly sampled similar amounts of sequences from the first and second strains, and re-sampled them several hundred times. We also checked the robustness of the results to the exact sampling size (Fig  1F ).
Sequence preparation We have applied the three-way test to influenza virus. We performed a multiple progressive alignment for amino acid sequences of Neuraminidase protein of Influenza A virus strain H1N1 obtained from public database https://www. fludb. org. We focused on NA because of the massive amount of sequence data and because strong changes in NA are responsible for the higher infectivity of the pandemic strain. The amount of data must be sufficiently high to ensure that each three-site haplotype entering UFE be represented by a large number of sequences, with their inverse square root being the relative error. In our case, this condition was fulfilled by setting cutoff at allelic frequency f>5%. We downloaded 8440 sequences of NA from a public database (https://www. fludb. org). They were collected worldwide, from different geographic locations, from year 2005 and year 2010, and included both pre-pandemic and post-pandemic strain.
All sequences were aligned. We considered only the sites that were strongly polymorphic (>5%) sometimes in the window of 5 years, during which time they contribute to the calculated correlation measures. Then, we found the common consensus (majority) allele for each amino acid position. Note that we have chosen the common consensus of the entire set as the reference sequence for calculating allele frequencies. The choice of the reference sequence does not matter for the site-site correlations and the inferred network. Pairwise distances between sequences were computed using pairwise alignment. The obtained consensus, defined as the most frequent variant in the population, served as a universal reference to binarized data sequences. Before applying the detection algorithm, the protein sequences were binarized, by direct comparison of each sequence to the consensus.
we consider a haploid population with the initial frequency of deleterious alleles, f 0 = 0. 45, beneficial mutation rate, U = 0. 07, fixed selection coefficient, s = 0. 1, population of N = 1000 individuals, L = 40 sites, and fixed epistatic strength E = 0. 75. The core Monte-Carlo simulation code for Fig 1 is written in MATLAB and deposited at site https://github. com/rbatorsky/hiv-recombination. It can be modified for different types of epistasis and recombination. The code for Fig 2 (data analysis) has been uploaded to https://github. com/irouzine/Pedruzzi.
Mortality outcomes with hydroxychloroquine and chloroquine in COVID-19 from an international collaborative meta-analysis of randomized trials Substantial COVID-19 research investment has been allocated to randomized clinical trials (RCTs) on hydroxychloroquine/chloroquine, which currently face recruitment challenges or early discontinuation. We aim to estimate the effects of hydroxychloroquine and chloroquine on survival in COVID-19 from all currently available RCT evidence, published and unpublished. We present a rapid meta-analysis of ongoing, completed, or discontinued RCTs on hydroxychloroquine or chloroquine treatment for any COVID-19 patients (protocol: https:// osf. io/QESV4/). We systematically identified unpublished RCTs (ClinicalTrials. gov, WHO International Clinical Trials Registry Platform, Cochrane COVID-registry up to June 11, 2020), and published RCTs (PubMed, medRxiv and bioRxiv up to October 16, 2020).
While many trials are ongoing, additional published evidence of potential benefits or harms may be several months away, if they even reach completion. Given the lack of favorable results in the large RECOVERY trial and the revoked Emergency Use Authorization, recruitment into HCQ and CQ trials has become increasingly difficult and many trials may run the risk of ending in futility. A rapid examination of data on all-cause mortality from as many trials as possible may offer the best evidence on potential survival benefits and to ensure that patients are not exposed to unnecessary risks if benefit is lacking. We used the infrastructure established with COVID-evidence  15  , a comprehensive database of COVID-19 trials funded by the Swiss National Science Foundation, to invite all investigators of HCQ or CQ trials to participate in an international collaborative metaanalysis.
Similarly, the WHO SOLIDARITY trial indicated no mortality benefit  26. The RECOVERY and WHO SOLIDARITY trials used HCQ in comparatively higher doses than all other trials except REMAP-CAP. There was no evidence for an effect modification by dose (p for interaction = 0. 29), and the combined effect of all the trials with lower dose did not indicate a benefit of HCQ but tended to a null effect (OR 0. 97; 0. 73-1. 30) with wide CIs, compatible with the main effect estimate. This meta-analysis does not address prophylactic use nor other outcomes besides mortality. All but three trials excluded children and the majority excluded pregnant or breastfeeding women; generalizability remains unclear for those populations. Among the five studies on outpatients, there were three deaths, two occurring in the one trial of 491 relatively young patients with few comorbidities  17  and one occurring in a small trial with 27  Register) or April 9 (WHO Literature database, bioRxiv, medRxiv, SwissEthics).
In other words, the control group had to receive placebo or no treatment other than standard of care (we excluded comparisons of HCQ or CQ against an active treatment, e. g. , HCQ versus azithromycin, since active controls were too heterogeneous to pool together and reveal the pure benefits and harms of HCQ or CQ). Eligible ongoing trials had to provide data on all-cause mortality and randomize the first patient before June 1, 2020 (time point selected arbitrarily as we did not expect trials launched later to recruit enough patients to provide relevant additional information). Trials that were published or posted as preprint were not restricted by date. Prevention trials were not included. We included trials regardless of follow-up time and whether mortality was a primary outcome or not; moreover, we put no restrictions on trial status, language, geographical region, or healthcare setting.
Unpublished trial details. All unpublished trials were performed according to the principles of the Declaration of Helsinki and written informed consent was obtained from the study participants. The release of mortality outcome data was authorized by the respective data and safety monitoring boards and principal investigators. Ethical approval was granted by institutional review boards as follows: University of Pennsylvania, ref. #842838 (PATCH, NCT04329923); National Bioethics Committee (NBC) Pakistan, ref. 4-87/NBC-471-COVID-19-05/20/ (PROTECT, NCT04338698); Ethics Committee of the Capital Region of Denmark, ref. H-20025317 (CCAP-1, NCT04345289); Comité de Protection des Personnes du Sud-Ouest et Outre-Mer 4, ref. CPP2020-03-036/2020-001271-33/ 20. 03. 24. 72431, and the Agence Nationale de Sécurité du Médicament et des produits de santé (ANSM), ref.
MEDAECNAT-2020-03-00045 (HYCOVID, NCT04325893); Ethik-Kommission an der Medizinischen Fakultät der Eberhard-Karls-Universität und am Universitätsklinikum Tübingen, ref. 190/2020AMG1 and ref. 225/2020AMG1 (COV-HCQ, NCT04342221; and COMIHY, NCT04340544, respectively); London-Surrey Borders Research Ethics Committee in the UK, Medisch Ethische Toetsingscommissie Utrecht (METC Utrecht) in the Netherlands, Sydney Local District ethics Review Committee (Royal Prince Alfred Hospital) in Australia, Northern A Health and Disability Ethics Committee in New Zealand, St. Vincent's Healthcare Group Ethics and Medical Research Committee in Ireland, King Abdullah International Medical Research Center Institutional Review Board in Saudi Arabia, University of Pittsburgh Institutional Review Board in the United States, Unity Health Research Ethics Board in Canada, National Ethics Committee for Clinical Research (CEIC) in Portugal, and the Romania Academy of Medical Sciences National Bioethics Committee for Medicines and Medical Devices (REMAP-CAP, NCT02735707); Comissão Nacional de Ética em Pesquisa (CONEP), ref.
3. 961. 681 (CloroCOVID19II A, NCT04323527, and CloroCOVID19II B, NCT04342650); a Single Ethics Committee from the Coordination of the National Institutes of Health and High Specialty Hospitals, ref. C13-20 (HYDRA, NCT04315896); the Ethics Committee of Beijing Youan Hospital, Capital Medical University, ref. JINYOUKELUN[2020]013 (ChiCTR2000031204); Partners Human Research Committee at the Brigham and Women's Hospital, Boston, Ethisch Comite in Belgium, and Stichting Beoordeling Ethiek Biomedisch in the Netherlands (NCT04333654); The Queen's Medical Center, ref. RA-2020-018 (OAHU-COVID19, NCT04345692); Duke University Medical Center Institutional Review Board, ref. Pro00105339, and UnityPoint Health Institutional Review Board (NCT04335552); and Medical Ethics Committee Utrecht (METC Utrecht), part of the Dutch Central Committee on Research Involving Human Subjects (ARCHAIC, NL8490).
42arch strategy. We searched for eligible trials registered at ClinicalTrials. gov and the WHO International Clinical Trials Registry Platform (ICTRP) by June 11, 2020 (COVID-evidence database; see Supplement 2)42. We additionally searched PubMed and the Cochrane COVID-19 trial registry (covering preprints, trial registries, and literature databases) by June 11, 2020, using terms related to HCQ and CQ combined with terms for COVID-19 and a standard RCT filter (Supplement 2).
Acknowledgements We wish to express our heartfelt gratitude to all patients volunteering for the trials involved. We furthermore thank  Wenyan Ma  and  Benjamin Kasenda  ( University Hospital Basel, University of Basel ) for kindly translating the emails to Chinese investigators. For valuable contributions to individual trials in this collaborative group, we sincerely thank:  Hannah Jin ,  Monica Feeley ,  Bruce Bausk ,  Jessica Cauley ,  Jane Kleinjan ,  Jon Gothing ,  Naeema Bangash ,  Heather Wroe ,  Claire Bigogne ,  Christelle Castell ,  Annelies Mottart ,  Lisette Cortenraad ,  Judith Medema-Muller ,  Katia Handelberg ,  Khalid Benhammou ,  Shaheen Kumar ,  Sophie Gribomont ,  Kim Kuehne ,  Cathia Markina ,  Julien Labeirie ,  Julie Pencole ,  Eva Crispyn ,  Cecile Le Breton ,  Kelli Horn ,  Tina Patel ,  Benjamin Harrois ,  Isabelle Collin ,  Vetheeswar Manivannan ,  Irma Slomp ,  Frederick Becue ,  Isabelle Godefroy ,  Lynne Guo ,  Lene Kollmorgen ,  Toluwalope Cole ,  Catherine Chene ,  Praveena Deenumsetti ,  Anne Doisy ,  Ariane Vialfont ,  Melissa Charbit ,  Christine Shu ,  Stephane Kirkesseli ,  Howard Surks ,  Magalie De Meyer ,  Edel Hendrickx , and  Paul Deutsch  ( Sanofi trial );  Ellie Carmody ,  Märtin Backer ,  Jaishvi Eapen ,  Jack A.
50 Netherlands None Suspected ≥18 years 0 Brazil Participant, caregiver Suspected ≥18 years 35. 37 Brazil Participant, caregiver Treatment schedule Control arm (n) No treatment (6) 800 mg/day on day 1, then 400 mg/day for 4 days Placebo (123) 800 mg on day 1, then 400 mg/ day for 8 days Placebo (14) 800 mg/day for day 1 and 600 mg/day for days 2-7 Placebo (247) 800 mg at 0 h, then 600 mg after 6-8 h, then 600 mg daily for 4 days No treatment (157) 800 mg on day 1, and 400 mg on days 2-7 Placebo (3) 800 mg at 0 h, then 400 mg 6-8 h later, then 600 mg/day for 9 days Placebo (8) 600 mg/d for 7 days No treatment (12) 1000 mg/day for 1 day, then 500 mg/day for 9 days No treatment (3) 600 mg at 0 h, then 300 mg after 12 h, then 600 mg/day for 4 days Placebo (74) 900 mg/day for 1 day, then 450 mg/day for 4 days Placebo (41) 900 mg/day for 1 day, then 450 mg/day for 4 days Placebo (17) 1000 mg on day 1, then 500 mg/ day on days 2-3, then 250 mg/ day until ≤14 days of total treatment Register ID Setting Treatment comparison Experimental arm (n) NCT04345692 Inpatient HCQ (10) NCT04325893 Inpatient HCQ (124) NCT04342221 Inpatient HCQ (13) NCT04308668 Outpatient HCQ (244) NCT04304053 Outpatient HCQ (136) NCT04333654 Outpatient HCQ (5) NCT04340544 Outpatient HCQ (8) ChiCTR2000030054 Inpatient CQ (18) NL8490 Inpatient CQ (5) NCT04342650 Outpatient CQ (78) NCT04323527 Inpatient CQ (41) ChiCTR2000031204 Inpatient CQ (18) Acronym OAHU-COVID19 HYCOVID COV-HCQ f COVID-PEP BCN PEP CoV-2 Study COMIHY Chloroquine ARCHAIC CloroCOVID19II CloroCOVID19II.
42arch strategy. We searched for eligible trials registered at ClinicalTrials. gov and the WHO International Clinical Trials Registry Platform (ICTRP) by June 11, 2020 (COVID-evidence database; see Supplement 2)42. We additionally searched PubMed and the Cochrane COVID-19 trial registry (covering preprints, trial registries, and literature databases) by June 11, 2020, using terms related to HCQ and CQ combined with terms for COVID-19 and a standard RCT filter (Supplement 2) weight, % 5. 09 [0. 04; 719. 11] 3. 3 1. 71 [0. 69; 4. 28] 96. 7 0. 0 0. 0 0. 0 1. 77 [0. 15; 21. 13] 100. 0.
1. 12 [1. 08; 1. 16] 1. 11 [0. 96; 1. 27] 1. 11 [0. 96; 1. 27] 1. 11 [1. 01; 1. 20] 1. 11 [1. 01; 1. 20] 1. 11 [1. 01; 1. 20] 1. 11 [0. 98; 1. 25] 1. 11 [1. 01; 1. 21] 1. 11 [0. 97; 1. 26] 1. 11 [0. 97; 1. 26] 1. 10 [0. 99; 1. 23] 1. 10 [0. 99; 1. 23] 1. 10 [1. 00; 1. 21] 1. 10 [1. 00; 1. 21] 1. 10 [1. 01; 1. 20] 1. 10 [1. 01; 1. 20] 1. 10 [1. 02; 1. 20] 1. 10 [1. 01; 1. 20] 1. 10 [1. 02; 1. 19] 1. 10 [1. 02; 1. 18] 1. 10 [1. 02; 1. 19] 1. 10 [1. 02; 1. 18] 1. 09 [1. 00; 1. 19] 1. 09 [1. 00; 1. 19] 1. 09 [1. 00; 1. 19] 1. 11 [1. 02; 1. 20] 1. 11 [1. 02; 1. 20] 1. 11 [1. 02; 1. 20] Source Favours HCQ Favours Control OR (95% CI) Adding ChiCTR2000029559 (k=1) Adding ChiCTR2000029868 (k=2) Adding NCT04261517 (k=3) Adding RECOVERY (k=4) 1. 11 [0. 96; 1. 27] Adding ChiCTR2000030054 (k=5) 1. 11 [0.
96; 1. 27] Adding NO COVID-19 (k=6) 1. 11 [1. 01; 1. 20] Adding NCT04384380 (k=7) 1. 11 [1. 01; 1. 20] Adding COVID-PEP (k=8) 1. 10 [1. 08; 1. 13] Adding BCN PEP CoV-2 Study (k=9) 1. 10 [1. 08; 1. 13] Adding NCT04353336 (k=10) 1. 11 [1. 08; 1. 13] Adding Coalition I (k=11) 1. 11 [1. 08; 1. 13] Adding TEACH (k=12) 1. 11 [1. 09; 1. 13] Adding NCT04491994 (k=13) 1. 11 [1. 09; 1. 13] Adding WHO SOLIDARITY (k=14) 1. 11 [1. 09; 1. 13] Random effects model 0. 8 1 1. 25 Odds Ratio (95% CI).
Here, we provide a descriptive analysis of the anti-SARS-CoV-2 vaccination thrombotic risk reported to the World Health Organization (WHO) Global Database for Individual Case Safety Reports (VigiBase). VigiBase is a databank developed and maintained by the Uppsala Monitoring Centre, Sweden. It is the world's largest pharmacovigilance database, with submissions from member states since the establishment of the WHO Program for International Drug Monitoring in 1968. Vigibase has been largely used in the past years to detect significant signals for adverse drug reactions  [5]. Some adverse drug reactions to vaccines may be identified only after their commercialisation, in particular when the events are very rare or have a delayed time to onset. Therefore, the safety monitoring of vaccines continues in post-marketing surveillance.
For example, during the mass vaccination campaign in 2009 for H1N1, several cases of narcolepsy were reported during the post-marketing period  [6]. In this context, our study aimed to assess clinical features of arterial and venous thrombosis after injection of three anti-COVID-19 vaccines (Comirnaty® from Pfizer-BioNtech; Moderna COVID-19 vaccine, and AZD1222 from Oxford-AstraZeneca)  [7] [8] [9]  until 16 March, 2021. Between 13 December, 2020 and 16 March, 2021 (94 days), 361 734 967 people received a vaccination according to the international COVID-19 vaccination dataset  [10]  and 2161 thrombotic events were reported in Vigibase by 16 March, 2021. Spontaneous reports of thrombotic events are shared in 1197 persons for Comirnaty, 325 for the Moderna COVID-19 vaccine and 639 for AZD1222 (table  1 ).
1% (7/639) of events reported; time to event: 2-16 days; age range: 19-59 years old; three women and four men) and with Comirnaty (0. 4% (4/1197) of events reported; time to event: 1-10 days; age range: 30-84 years-old; four women). Three patients out of four with Comirnaty, all with Moderna vaccine and six out of seven with AZ1222 had a particular form of CVT, called cerebral sinus vein thrombosis (CVST). Five out of seven CVT cases observed after AZD1222 were associated with thrombocytopenia. Moreover, we noticed thrombocytopenia associated with thrombotic events and/or disseminated intravascular coagulation and/or antiphospholipids antibodies for all three vaccines, and one thrombocytopenia associated with HIT-positive tests after AZD1222. Since we performed the data extraction from the WHO database, several other cases of HIT have been described by two groups after AZD1222 vaccination  [11, 12].