Determining the Agreement Between an Automated Respiratory Rate Counter and a Reference Standard for Detecting Symptoms of Pneumonia in Children: Protocol for a Cross-Sectional Study in Ethiopia.

BACKGROUND: Acute respiratory infections (ARIs), primarily pneumonia, are the leading infectious cause of under-5 mortality worldwide. Manually counting respiratory rate (RR) for 60 seconds using an ARI timer is commonly practiced by community health workers to detect fast breathing, an important sign of pneumonia. However, correctly counting breaths manually and classifying the RR is challenging, often leading to inappropriate treatment. A potential solution is to introduce RR counters, which count and classify RR automatically. OBJECTIVE: This study aims to determine how the RR count of an Automated Respiratory Infection Diagnostic Aid (ARIDA) agrees with the count of an expert panel of pediatricians counting RR by reviewing a video of the child's chest for 60 seconds (reference standard), for children aged younger than 5 years with cough and/or difficult breathing. METHODS: A cross-sectional study aiming to enroll 290 children aged 0 to 59 months presenting to pediatric in- and outpatient departments at a teaching hospital in Addis Ababa, Ethiopia, was conducted. Enrollment occurred between April and May 2017. Once enrolled, children participated in at least one of three types of RR evaluations: (1) agreement-measure the RR count of an ARIDA in comparison with the reference standard, (2) consistency-measure the agreement between two ARIDA devices strapped to one child, and (3) RR fluctuation-measure RR count variability over time after ARIDA attachment as measured by a manual count. The agreement and consistency of expert clinicians (ECs) counting RR for the same child with the Mark 2 ARI timer for 60 seconds was also measured in comparison with the reference standard. RESULTS: Primary outcomes were (1) mean difference between the ARIDA and reference standard RR count (agreement) and (2) mean difference between RR counts obtained by two ARIDA devices started simultaneously (consistency). CONCLUSIONS: Study strengths included the design allowing for comparison between both ARIDA and the EC with the reference standard RR count. A limitation is that exactly the same set of breaths were not compared between ARIDA and the reference standard since ARIDA can take longer than 60 seconds to count RR. Also, manual RR counting, even when aided by a video of the child's chest movements, is subject to human error and can result in low interrater reliability. Further work is needed to reach global consensus on the most appropriate reference standard and an acceptable level of agreement to provide ministries of health with evidence to make an informed decision on whether to scale up new automated RR counters. TRIAL REGISTRATION: ClinicalTrials.gov NCT03067558; https://clinicaltrials.gov/ct2/show/NCT03067558. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR1-10.2196/16531

Common genetic variants in the CLDN2 and PRSS1-PRSS2 loci alter risk for alcohol-related and sporadic pancreatitis

Pancreatitis is a complex, progressively destructive inflammatory disorder. Alcohol was long thought to be the primary causative agent, but genetic contributions have been of interest since the discovery that rare PRSS1, CFTR, and SPINK1 variants were associated with pancreatitis risk. We now report two significant genome-wide associations identified and replicated at PRSS1-PRSS2 (1×10-12) and x-linked CLDN2 (p < 1×10-21) through a two-stage genome-wide study (Stage 1, 676 cases and 4507 controls; Stage 2, 910 cases and 4170 controls). The PRSS1 variant affects susceptibility by altering expression of the primary trypsinogen gene. The CLDN2 risk allele is associated with atypical localization of claudin-2 in pancreatic acinar cells. The homozygous (or hemizygous male) CLDN2 genotype confers the greatest risk, and its alleles interact with alcohol consumption to amplify risk. These results could partially explain the high frequency of alcohol-related pancreatitis in men – male hemizygous frequency is 0.26, female homozygote is 0.07

New genetic loci link adipose and insulin biology to body fat distribution.

Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, here we conduct genome-wide association meta-analyses of traits related to waist and hip circumferences in up to 224,459 individuals. We identify 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (BMI), and an additional 19 loci newly associated with related waist and hip circumference measures (P < 5 × 10(-8)). In total, 20 of the 49 waist-to-hip ratio adjusted for BMI loci show significant sexual dimorphism, 19 of which display a stronger effect in women. The identified loci were enriched for genes expressed in adipose tissue and for putative regulatory elements in adipocytes. Pathway analyses implicated adipogenesis, angiogenesis, transcriptional regulation and insulin resistance as processes affecting fat distribution, providing insight into potential pathophysiological mechanisms

The genetic architecture of the human cerebral cortex

The cerebral cortex underlies our complex cognitive capabilities, yet little is known about the specific genetic loci that influence human cortical structure. To identify genetic variants that affect cortical structure, we conducted a genome-wide association meta-analysis of brain magnetic resonance imaging data from 51,665 individuals. We analyzed the surface area and average thickness of the whole cortex and 34 regions with known functional specializations. We identified 199 significant loci and found significant enrichment for loci influencing total surface area within regulatory elements that are active during prenatal cortical development, supporting the radial unit hypothesis. Loci that affect regional surface area cluster near genes in Wnt signaling pathways, which influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson's disease, insomnia, depression, neuroticism, and attention deficit hyperactivity disorder

Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity

The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole-exome sequencing data of about 4000 SARS-CoV-2-positive individuals were used to define an interpretable machine-learning model for predicting COVID-19 severity. First, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthily, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management. © 2021, The Author(s)

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe