Multiple novel prostate cancer susceptibility signals identified by fine-mapping of known risk loci among Europeans

Genome-wide association studies (GWAS) have identified numerous common prostate cancer (PrCa) susceptibility loci. We have fine-mapped 64 GWAS regions known at the conclusion of the iCOGS study using large-scale genotyping and imputation in 25 723 PrCa cases and 26 274 controls of European ancestry. We detected evidence for multiple independent signals at 16 regions, 12 of which contained additional newly identified significant associations. A single signal comprising a spectrum of correlated variation was observed at 39 regions; 35 of which are now described by a novel more significantly associated lead SNP, while the originally reported variant remained as the lead SNP only in 4 regions. We also confirmed two association signals in Europeans that had been previously reported only in East-Asian GWAS. Based on statistical evidence and linkage disequilibrium (LD) structure, we have curated and narrowed down the list of the most likely candidate causal variants for each region. Functional annotation using data from ENCODE filtered for PrCa cell lines and eQTL analysis demonstrated significant enrichment for overlap with bio-features within this set. By incorporating the novel risk variants identified here alongside the refined data for existing association signals, we estimate that these loci now explain ∼38.9% of the familial relative risk of PrCa, an 8.9% improvement over the previously reported GWAS tag SNPs. This suggests that a significant fraction of the heritability of PrCa may have been hidden during the discovery phase of GWAS, in particular due to the presence of multiple independent signals within the same regio

COVIDTrach; a prospective cohort study of mechanically ventilated COVID-19 patients undergoing tracheostomy in the UK

Purpose COVIDTrach is a UK multi-centre prospective cohort study project evaluating the outcomes of tracheostomy in patients with COVID-19 receiving mechanical ventilation. It also examines the incidence of SARS-CoV-2 infection among healthcare workers involved in the procedure.Method An invitation to participate was sent to all UK NHS departments involved in tracheostomy in COVID-19 patients. Data was entered prospectively and clinical outcomes updated via an online database (REDCap). Clinical variables were compared with outcomes using multivariable regression analysis, with logistic regression used to develop a prediction model for mortality. Participants recorded whether any operators tested positive for SARS-CoV-2 within two weeks of the procedure.Results The cohort comprised 1605 tracheostomy cases from 126 UK hospitals. The median time from intubation to tracheostomy was 15 days (IQR 11, 21). 285 (18%) patients died following the procedure. 1229 (93%) of the survivors had been successfully weaned from mechanical ventilation at censoring and 1049 (81%) had been discharged from hospital. Age, inspired oxygen concentration, PEEP setting, pyrexia, number of days of ventilation before tracheostomy, C-reactive protein and the use of anticoagulation and inotropic support independently predicted mortality. Six reports were received of operators testing positive for SARS-CoV-2 within two weeks of the procedure.Conclusions Tracheostomy appears to be safe in mechanically ventilated patients with COVID-19 and to operators performing the procedure and we identified clinical indicators that are predictive of mortality

Acute undifferentiated fever in India: a multicentre study of aetiology and diagnostic accuracy

Abstract Background The objectives of this study were to determine the proportion of malaria, bacteraemia, scrub typhus, leptospirosis, chikungunya and dengue among hospitalized patients with acute undifferentiated fever in India, and to describe the performance of standard diagnostic methods. Methods During April 2011–November 2012, 1564 patients aged ≥5 years with febrile illness for 2–14 days were consecutively included in an observational study at seven community hospitals in six states in India. Malaria microscopy, blood culture, Dengue rapid NS1 antigen and IgM Combo test, Leptospira IgM ELISA, Scrub typhus IgM ELISA and Chikungunya IgM ELISA were routinely performed at the hospitals. Second line testing, Dengue IgM capture ELISA (MAC-ELISA), Scrub typhus immunofluorescence (IFA), Leptospira Microscopic Agglutination Test (MAT), malaria PCR and malaria immunochromatographic rapid diagnostic test (RDT) Parahit Total™ were performed at the coordinating centre. Convalescence samples were not available. Case definitions were as follows: Leptospirosis: Positive ELISA and positive MAT. Scrub typhus: Positive ELISA and positive IFA. Dengue: Positive RDT and/or positive MAC-ELISA. Chikungunya: Positive ELISA. Bacteraemia: Growth in blood culture excluding those defined as contaminants. Malaria: Positive genus-specific PCR. Results Malaria was diagnosed in 17% (268/1564) and among these 54% had P. falciparum. Dengue was diagnosed in 16% (244/1564). Bacteraemia was found in 8% (124/1564), and among these Salmonella typhi or S. paratyphi constituted 35%. Scrub typhus was diagnosed in 10%, leptospirosis in 7% and chikungunya in 6%. Fulfilling more than one case definition was common, most frequent in chikungunya where 26% (25/98) also had positive dengue test. Conclusions Malaria and dengue were the most common causes of fever in this study. A high overlap between case definitions probably reflects high prevalence of prior infections, cross reactivity and subclinical infections, rather than high prevalence of coinfections. Low accuracy of routine diagnostic tests should be taken into consideration when approaching the patient with acute undifferentiated fever in India

Correction: A High Malaria Prevalence Identified by PCR among Patients with Acute Undifferentiated Fever in India.

[This corrects the article DOI: 10.1371/journal.pone.0158816.]

Seasonal variation among malaria PCR positive cases.

<p>The sites have the following rainy seasons: Oddanchatram; June to December, with peak monsoon from October to December. Ambur; June to December, with a peak monsoon from October to December. Ratnagiri; June to November. Mungeli; June to September, or early October. Anantapur; Dry climate, but rainy season from May to October, with its peak in September. Tezpur; April to September, with peak monsoon in July and August. Raxaul; July to September, with peak monsoon in August.</p

Malaria species concordance between PCR and RDT results, (N = 1407).

<p>Malaria species concordance between PCR and RDT results, (N = 1407).</p

Performance of routine microscopy and RDT compared to PCR.

<p>Performance of routine microscopy and RDT compared to PCR.</p

Location of seven community hospitals in six states of India participating in the study.

<p>Location of seven community hospitals in six states of India participating in the study.</p