Reproducible image-based profiling with Pycytominer

Technological advances in high-throughput microscopy have facilitated the acquisition of cell images at a rapid pace, and data pipelines can now extract and process thousands of image-based features from microscopy images. These features represent valuable single-cell phenotypes that contain information about cell state and biological processes. The use of these features for biological discovery is known as image-based or morphological profiling. However, these raw features need processing before use and image-based profiling lacks scalable and reproducible open-source software. Inconsistent processing across studies makes it difficult to compare datasets and processing steps, further delaying the development of optimal pipelines, methods, and analyses. To address these issues, we present Pycytominer, an open-source software package with a vibrant community that establishes an image-based profiling standard. Pycytominer has a simple, user-friendly Application Programming Interface (API) that implements image-based profiling functions for processing high-dimensional morphological features extracted from microscopy images of cells. Establishing Pycytominer as a standard image-based profiling toolkit ensures consistent data processing pipelines with data provenance, therefore minimizing potential inconsistencies and enabling researchers to confidently derive accurate conclusions and discover novel insights from their data, thus driving progress in our field.Comment: 13 pages, 4 figure

Building a Systematic Online Living Evidence Summary of COVID-19 Research

Throughout the global coronavirus pandemic, we have seen an unprecedented volume of COVID-19 researchpublications. This vast body of evidence continues to grow, making it difficult for research users to keep up with the pace of evolving research findings. To enable the synthesis of this evidence for timely use by researchers, policymakers, and other stakeholders, we developed an automated workflow to collect, categorise, and visualise the evidence from primary COVID-19 research studies. We trained a crowd of volunteer reviewers to annotate studies by relevance to COVID-19, study objectives, and methodological approaches. Using these human decisions, we are training machine learning classifiers and applying text-mining tools to continually categorise the findings and evaluate the quality of COVID-19 evidence

Self‐Perceived Hearing Status Creates an Unrealized Barrier to Hearing Healthcare Utilization

Objective To examine sociodemographic and audiometric factors associated with hearing aid (HA) uptake in adults with hearing loss (HL), and to investigate the role of self‐perceived hearing status on pursuit of hearing treatment. The relationship between self‐perceived hearing status and HA adoption has not been reported in a nationally representative sample of United States (US) adults. Study Design Cross‐sectional analysis of nationwide household health survey. Methods Audiometric and questionnaire data from the 2005 to 2012 National Health and Nutrition Examination Survey cycles were used to examine trends in untreated HL and HA adoption in US adults. Adjusted odds ratios for HA adoption were calculated for individuals with measured HL. Results Of 5230 respondents, 26.1% had measurable HL, of which only 16.0% correctly self‐identified their hearing status, and only 17.7% used an HA. Age, higher education, severe hearing impairments, and recent hearing evaluations, were positively associated with HA adoption. Conclusion Hearing loss is a global public health concern placing significant economic burden on both the individual and society. Self‐reported hearing status is not a reliable indicator for HL, and measured HL is not correlated with increased rates of treatment. Recent hearing evaluation is positively associated with increased rates of treatment. Routine hearing assessment will help to better identify those with HL and improve access to hearing treatment. Level of Evidence III Laryngoscope, 131:E289–E295, 202

The classical and alternative circulating renin-angiotensin system in normal dogs and dogs with stage B1 and B2 myxomatous mitral valve disease.

BackgroundThe behavior of the comprehensive circulating renin-angiotensin system (RAS) in dogs with myxomatous mitral valve disease (MMVD) before to the onset of congestive heart failure remains largely unexplored.Hypothesis/objectivesThe classical and alternative RAS activity and aldosterone concentrations will be significantly higher in dogs with American College of Veterinary Internal Medicine (ACVIM) stage B2 MMVD compared to normal dogs and dogs with ACVIM stage B1 MMVD.AnimalsOne-hundred seventeen client-owned dogs (normal = 60; B1 = 31; B2 = 26).MethodsProspective observational study. Angiotensin peptides (AP) and aldosterone concentrations were measured using liquid chromatography and mass spectrometry. Angiotensin converting enzymes 1 and 2 (ACE, ACE2) and renin activity surrogates were calculated from AP concentrations. Equilibrium dialysis (ED) and immediate protease inhibition (PI) methods of AP quantification were compared in 14 healthy dogs.ResultsCore RAS activity and aldosterone concentrations did not differ among the 3 groups. However, the balance between the alternative and classical RAS differed, with dogs with stage B2 MMVD having significantly higher ACE2 activity surrogate (ACE2surr ) when compared to normal dogs (adjusted P = .02; ratio of medians for ACE2surr [B2:normal], 1.89; 95% confidence interval [CI]: 1.4-2.6). The ED and PI methods of AP quantification were highly correlated (AngI, r = .9, P < .0001; AngII, r = .8, P = .001).Conclusions and clinical importanceCirculating alternative RAS activity, specifically the surrogate measure of ACE2 activity, was increased in dogs with stage B2 MMVD as compared to normal dogs. Equilibrium dialysis results are analogous to immediate protease inhibition in dogs

Building a Systematic Online Living Evidence Summary of COVID-19 Research

Throughout the global coronavirus pandemic, we have seen an unprecedented volume of COVID-19 researchpublications. This vast body of evidence continues to grow, making it difficult for research users to keep up with the pace of evolving research findings. To enable the synthesis of this evidence for timely use by researchers, policymakers, and other stakeholders, we developed an automated workflow to collect, categorise, and visualise the evidence from primary COVID-19 research studies. We trained a crowd of volunteer reviewers to annotate studies by relevance to COVID-19, study objectives, and methodological approaches. Using these human decisions, we are training machine learning classifiers and applying text-mining tools to continually categorise the findings and evaluate the quality of COVID-19 evidence