12 research outputs found
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
SelfâPerceived 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 selfâperceived hearing status on pursuit of hearing treatment. The relationship between selfâperceived hearing status and HA adoption has not been reported in a nationally representative sample of United States (US) adults.
Study Design
Crossâsectional 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 selfâidentified 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. Selfâreported 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:E289âE295, 202
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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 B2âMMVD compared to normal dogs and dogs with ACVIM stage B1âMMVD.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 B2âMMVD 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, Pâ<â.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 B2âMMVD as compared to normal dogs. Equilibrium dialysis results are analogous to immediate protease inhibition in dogs
Significance of sector-specific corporate social responsibility initiatives: Status and role of CSR in different sectors
Characterization of missing human genome sequences and copy-number polymorphic insertions
Protocol of an ongoing randomized controlled trial of care management for comorbid depression and hypertension: the Chinese Older Adult Collaborations in Health (COACH) study
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