A cloud-based training module for efficient de novo transcriptome assembly using Nextflow and Google cloud.

This study describes the development of a resource module that is part of a learning platform named "NIGMS Sandbox for Cloud-based Learning" (https://github.com/NIGMS/NIGMS-Sandbox). The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox at the beginning of this Supplement. This module delivers learning materials on de novo transcriptome assembly using Nextflow in an interactive format that uses appropriate cloud resources for data access and analysis. Cloud computing is a powerful new means by which biomedical researchers can access resources and capacity that were previously either unattainable or prohibitively expensive. To take advantage of these resources, however, the biomedical research community needs new skills and knowledge. We present here a cloud-based training module, developed in conjunction with Google Cloud, Deloitte Consulting, and the NIH STRIDES Program, that uses the biological problem of de novo transcriptome assembly to demonstrate and teach the concepts of computational workflows (using Nextflow) and cost- and resource-efficient use of Cloud services (using Google Cloud Platform). Our work highlights the reduced necessity of on-site computing resources and the accessibility of cloud-based infrastructure for bioinformatics applications.

Cloud-based introduction to BASH programming for biologists.

This manuscript describes the development of a resource module that is part of a learning platform named 'NIGMS Sandbox for Cloud-based Learning', https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial authored by National Institute of General Medical Sciences: NIGMS Sandbox: A Learning Platform toward Democratizing Cloud Computing for Biomedical Research at the beginning of this supplement. This module delivers learning materials introducing the utility of the BASH (Bourne Again Shell) programming language for genomic data analysis in an interactive format that uses appropriate cloud resources for data access and analyses. The next-generation sequencing revolution has generated massive amounts of novel biological data from a multitude of platforms that survey an ever-growing list of genomic modalities. These data require significant downstream computational and statistical analyses to glean meaningful biological insights. However, the skill sets required to generate these data are vastly different from the skills required to analyze these data. Bench scientists that generate next-generation data often lack the training required to perform analysis of these datasets and require support from bioinformatics specialists. Dedicated computational training is required to empower biologists in the area of genomic data analysis, however, learning to efficiently leverage a command line interface is a significant barrier in learning how to leverage common analytical tools. Cloud platforms have the potential to democratize access to the technical tools and computational resources necessary to work with modern sequencing data, providing an effective framework for bioinformatics education. This module aims to provide an interactive platform that slowly builds technical skills and knowledge needed to interact with genomics data on the command line in the Cloud. The sandbox format of this module enables users to move through the material at their own pace and test their grasp of the material with knowledge self-checks before building on that material in the next sub-module. This manuscript describes the development of a resource module that is part of a learning platform named ``NIGMS Sandbox for Cloud-based Learning'' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [1] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses.

A cloud-based learning module for biomarker discovery.

This manuscript describes the development of a resource module that is part of a learning platform named "NIGMS Sandbox for Cloud-based Learning" https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox at the beginning of this Supplement. This module delivers learning materials on basic principles in biomarker discovery in an interactive format that uses appropriate cloud resources for data access and analyses. In collaboration with Google Cloud, Deloitte Consulting and NIGMS, the Rhode Island INBRE Molecular Informatics Core developed a cloud-based training module for biomarker discovery. The module consists of nine submodules covering various topics on biomarker discovery and assessment and is deployed on the Google Cloud Platform and available for public use through the NIGMS Sandbox. The submodules are written as a series of Jupyter Notebooks utilizing R and Bioconductor for biomarker and omics data analysis. The submodules cover the following topics: 1) introduction to biomarkers; 2) introduction to R data structures; 3) introduction to linear models; 4) introduction to exploratory analysis; 5) rat renal ischemia-reperfusion injury case study; (6) linear and logistic regression for comparison of quantitative biomarkers; 7) exploratory analysis of proteomics IRI data; 8) identification of IRI biomarkers from proteomic data; and 9) machine learning methods for biomarker discovery. Each notebook includes an in-line quiz for self-assessment on the submodule topic and an overview video is available on YouTube (https://www.youtube.com/watch?v=2-Q9Ax8EW84). This manuscript describes the development of a resource module that is part of a learning platform named ``NIGMS Sandbox for Cloud-based Learning'' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [1] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses.

Identifying and training deep learning neural networks on biomedical-related datasets.

This manuscript describes the development of a resources module that is part of a learning platform named 'NIGMS Sandbox for Cloud-based Learning' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox at the beginning of this Supplement. This module delivers learning materials on implementing deep learning algorithms for biomedical image data in an interactive format that uses appropriate cloud resources for data access and analyses. Biomedical-related datasets are widely used in both research and clinical settings, but the ability for professionally trained clinicians and researchers to interpret datasets becomes difficult as the size and breadth of these datasets increases. Artificial intelligence, and specifically deep learning neural networks, have recently become an important tool in novel biomedical research. However, use is limited due to their computational requirements and confusion regarding different neural network architectures. The goal of this learning module is to introduce types of deep learning neural networks and cover practices that are commonly used in biomedical research. This module is subdivided into four submodules that cover classification, augmentation, segmentation and regression. Each complementary submodule was written on the Google Cloud Platform and contains detailed code and explanations, as well as quizzes and challenges to facilitate user training. Overall, the goal of this learning module is to enable users to identify and integrate the correct type of neural network with their data while highlighting the ease-of-use of cloud computing for implementing neural networks. This manuscript describes the development of a resource module that is part of a learning platform named ``NIGMS Sandbox for Cloud-based Learning'' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [1] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses.

Biofilm marker discovery with cloud-based dockerized metagenomics analysis of microbial communities.

In an environment, microbes often work in communities to achieve most of their essential functions, including the production of essential nutrients. Microbial biofilms are communities of microbes that attach to a nonliving or living surface by embedding themselves into a self-secreted matrix of extracellular polymeric substances. These communities work together to enhance their colonization of surfaces, produce essential nutrients, and achieve their essential functions for growth and survival. They often consist of diverse microbes including bacteria, viruses, and fungi. Biofilms play a critical role in influencing plant phenotypes and human microbial infections. Understanding how these biofilms impact plant health, human health, and the environment is important for analyzing genotype-phenotype-driven rule-of-life functions. Such fundamental knowledge can be used to precisely control the growth of biofilms on a given surface. Metagenomics is a powerful tool for analyzing biofilm genomes through function-based gene and protein sequence identification (functional metagenomics) and sequence-based function identification (sequence metagenomics). Metagenomic sequencing enables a comprehensive sampling of all genes in all organisms present within a biofilm sample. However, the complexity of biofilm metagenomic study warrants the increasing need to follow the Findability, Accessibility, Interoperability, and Reusable (FAIR) Guiding Principles for scientific data management. This will ensure that scientific findings can be more easily validated by the research community. This study proposes a dockerized, self-learning bioinformatics workflow to increase the community adoption of metagenomics toolkits in a metagenomics and meta-transcriptomics investigation. Our biofilm metagenomics workflow self-learning module includes integrated learning resources with an interactive dockerized workflow. This module will allow learners to analyze resources that are beneficial for aggregating knowledge about biofilm marker genes, proteins, and metabolic pathways as they define the composition of specific microbial communities. Cloud and dockerized technology can allow novice learners-even those with minimal knowledge in computer science-to use complicated bioinformatics tools. Our cloud-based, dockerized workflow splits biofilm microbiome metagenomics analyses into four easy-to-follow submodules. A variety of tools are built into each submodule. As students navigate these submodules, they learn about each tool used to accomplish the task. The downstream analysis is conducted using processed data obtained from online resources or raw data processed via Nextflow pipelines. This analysis takes place within Vertex AI's Jupyter notebook instance with R and Python kernels. Subsequently, results are stored and visualized in Google Cloud storage buckets, alleviating the computational burden on local resources. The result is a comprehensive tutorial that guides bioinformaticians of any skill level through the entire workflow. It enables them to comprehend and implement the necessary processes involved in this integrated workflow from start to finish. This manuscript describes the development of a resource module that is part of a learning platform named "NIGMS Sandbox for Cloud-based Learning" https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [1] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses.

Endothelin-1, Outcomes in Patients With Heart Failure and Reduced Ejection Fraction, and Effects of Dapagliflozin: Findings From DAPA-HF.

BACKGROUND: ET-1 (endothelin-1) is implicated in the pathophysiology of heart failure and renal disease. Its prognostic importance and relationship with kidney function in patients with heart failure with reduced ejection fraction receiving contemporary treatment are uncertain. We investigated these and the efficacy of dapagliflozin according to ET-1 level in the DAPA-HF trial (Dapagliflozin and Prevention of Adverse Outcomes in Heart Failure). METHODS: We investigated the incidence of the primary outcome (cardiovascular death or worsening heart failure), change in kidney function, and the effect of dapagliflozin according to baseline ET-1 concentration, adjusting in Cox models for other recognized prognostic variables in heart failure including NT-proBNP (N-terminal pro-B-type natriuretic peptide). We also examined the effect of dapagliflozin on ET-1 level. RESULTS: Overall, 3048 participants had baseline ET-1 measurements: tertile 1 (T1; ≤3.28 pg/mL; n=1016); T2 (>3.28-4.41 pg/mL; n=1022); and T3 (>4.41 pg/mL; n=1010). Patients with higher ET-1 were more likely male, more likely obese, and had lower left ventricular ejection fraction, lower estimated glomerular filtration rate, worse functional status, and higher NT-proBNP and hs-TnT (high-sensitivity troponin-T). In the adjusted Cox models, higher baseline ET-1 was independently associated with worse outcomes and steeper decline in kidney function (adjusted hazard ratio for primary outcome of 1.95 [95% CI, 1.53-2.50] for T3 and 1.36 [95% CI, 1.06-1.75] for T2; both versus T1; estimated glomerular filtration rate slope: T3, -3.19 [95% CI, -3.66 to -2.72] mL/min per 1.73 m2 per y, T2, -2.08 [95% CI, -2.52 to -1.63] and T1 -2.35 [95% CI, -2.79 to -1.91]; P=0.002). The benefit of dapagliflozin was consistent regardless of baseline ET-1, and the placebo-corrected decrease in ET-1 with dapagliflozin was 0.13 pg/mL (95% CI, 0.25-0.01; P=0.029). CONCLUSIONS: Higher baseline ET-1 concentration was independently associated with worse clinical outcomes and more rapid decline in kidney function. The benefit of dapagliflozin was consistent across the range of ET-1 concentrations measured, and treatment with dapagliflozin led to a small decrease in serum ET-1 concentration. REGISTRATION: URL: https://www. CLINICALTRIALS: gov; Unique identifier: NCT03036124.

Dapagliflozin vs. metolazone in heart failure resistant to loop diuretics.

BACKGROUND AND AIMS: To examine the decongestive effect of the sodium-glucose cotransporter 2 inhibitor dapagliflozin compared to the thiazide-like diuretic metolazone in patients hospitalized for heart failure and resistant to treatment with intravenous furosemide. METHODS AND RESULTS: A multi-centre, open-label, randomized, and active-comparator trial. Patients were randomized to dapagliflozin 10 mg once daily or metolazone 5-10 mg once daily for a 3-day treatment period, with follow-up for primary and secondary endpoints until day 5 (96 h). The primary endpoint was a diuretic effect, assessed by change in weight (kg). Secondary endpoints included a change in pulmonary congestion (lung ultrasound), loop diuretic efficiency (weight change per 40 mg of furosemide), and a volume assessment score. 61 patients were randomized. The mean (±standard deviation) cumulative dose of furosemide at 96 h was 977 (±492) mg in the dapagliflozin group and 704 (±428) mg in patients assigned to metolazone. The mean (±standard deviation) decrease in weight at 96 h was 3.0 (2.5) kg with dapagliflozin compared to 3.6 (2.0) kg with metolazone [mean difference 0.65, 95% confidence interval (CI) -0.12,1.41 kg; P = 0.11]. Loop diuretic efficiency was less with dapagliflozin than with metolazone [mean 0.15 (0.12) vs. 0.25 (0.19); difference -0.08, 95% CI -0.17,0.01 kg; P = 0.10]. Changes in pulmonary congestion and volume assessment score were similar between treatments. Decreases in plasma sodium and potassium and increases in urea and creatinine were smaller with dapagliflozin than with metolazone. Serious adverse events were similar between treatments. CONCLUSION: In patients with heart failure and loop diuretic resistance, dapagliflozin was not more effective at relieving congestion than metolazone. Patients assigned to dapagliflozin received a larger cumulative dose of furosemide but experienced less biochemical upset than those assigned to metolazone. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT04860011.

Canadian Association of Radiologists Head and Neck Imaging Referral Guideline.

The Canadian Association of Radiologists (CAR) Head and Neck Expert Panel consists of radiologists, a laryngologist and laryngeal surgeon, a patient advisor, and an epidemiologist/guideline methodologist. After developing a list of 11 clinical/diagnostic scenarios, a systematic rapid scoping review was undertaken to identify systematically produced referral guidelines that provide recommendations for one or more of these clinical/diagnostic scenarios. Recommendations from 17 guidelines and contextualization criteria in the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) for guidelines framework were used to develop 26 recommendation statements across the 11 scenarios. This guideline presents the methods of development and the referral recommendations for sinus disease, tinnitus, thyroid and parathyroid disease, neck mass of unknown origin, acute sialadenitis, chronic salivary conditions, and temporomandibular joint dysfunction.

Optimising Cardiology Ambulatory Care Pathways: A Comprehensive Approach to Admission Avoidance and Timely Intervention in a Post-Pandemic Healthcare Landscape.

INTRODUCTION: Cardiovascular diseases are a substantial burden on healthcare systems, contributing significantly to avoidable hospital admissions. We propose a Cardiology Ambulatory Care Pathway. METHODS: Conducted a 1 month study redirecting admission streams from primary and emergency care, into a Cardiology Ambulatory Care Hub providing triage in Hot Clinic, and access to a Multi-Modal Testing Platform. RESULTS: 98 patients were referred to the Ambulatory Care Hub, 91 of which avoided admission. 52 patients received care in the cardiology hub, 38 of which required further testing. CONCLUSION: We successfully streamlined various service streams, reducing admissions, and improving patient outcomes. Outpatient CTCA, ambulatory ECG, and echocardiography proved instrumental. We project a cost saving of £53,379 per month in bed days (£640,556 annual saving).

Accelerating genomic workflows using NVIDIA Parabricks.

BACKGROUND: As genome sequencing becomes better integrated into scientific research, government policy, and personalized medicine, the primary challenge for researchers is shifting from generating raw data to analyzing these vast datasets. Although much work has been done to reduce compute times using various configurations of traditional CPU computing infrastructures, Graphics Processing Units (GPUs) offer opportunities to accelerate genomic workflows by orders of magnitude. Here we benchmark one GPU-accelerated software suite called NVIDIA Parabricks on Amazon Web Services (AWS), Google Cloud Platform (GCP), and an NVIDIA DGX cluster. We benchmarked six variant calling pipelines, including two germline callers (HaplotypeCaller and DeepVariant) and four somatic callers (Mutect2, Muse, LoFreq, SomaticSniper). RESULTS: We achieved up to 65 × acceleration with germline variant callers, bringing HaplotypeCaller runtimes down from 36 h to 33 min on AWS, 35 min on GCP, and 24 min on the NVIDIA DGX. Somatic callers exhibited more variation between the number of GPUs and computing platforms. On cloud platforms, GPU-accelerated germline callers resulted in cost savings compared with CPU runs, whereas some somatic callers were more expensive than CPU runs because their GPU acceleration was not sufficient to overcome the increased GPU cost. CONCLUSIONS: Germline variant callers scaled well with the number of GPUs across platforms, whereas somatic variant callers exhibited more variation in the number of GPUs with the fastest runtimes, suggesting that, at least with the version of Parabricks used here, these workflows are less GPU optimized and require benchmarking on the platform of choice before being deployed at production scales. Our study demonstrates that GPUs can be used to greatly accelerate genomic workflows, thus bringing closer to grasp urgent societal advances in the areas of biosurveillance and personalized medicine.

Preventing Recurrent Cardioembolic Stroke: Right Approach, Right Patient (PRECISE) Study Protocol.

Cardiac rhythm monitoring is performed to search for atrial fibrillation (AF) after ischaemic stroke or transient ischaemic attack (TIA). Prolonged cardiac rhythm monitoring increases AF detection but is challenging to implement in many healthcare settings and is not needed for all people after ischaemic stroke/TIA. We aimed to develop and validate a model that includes clinical, electrocardiogram (ECG), blood-based, and genetic biomarkers to identify people with a low probability of AF detection after ischaemic stroke or TIA. We will recruit 675 consenting participants who are aged over 18 years, who were admitted with ischaemic stroke or TIA in the 5 days prior, who are not known to have AF, and who would be suitable for anticoagulation if AF is found. We will collect baseline demographic and clinical data, a 12-lead ECG, and a venous blood sample for blood biomarkers (including midregional pro-atrial natriuretic peptide, MRproANP) and genetic data. We will perform up to 28 days of cardiac rhythm monitoring using an R-test or patch device to search for AF in all participants. The sample size of 675 participants is based on true sensitivity of 92.5%, null hypothesis sensitivity of 80%, 80% power, and 5% significance. The primary outcome is AF detection ≥30 s duration during 28 days of cardiac rhythm monitoring. Secondary outcomes are AF detection at 1-year, recurrent cardiovascular events, and mortality and will be identified by electronic linkage and telephone follow-up. The results will guide the development of a more personalized care pathway to search for AF after ischaemic stroke or TIA. This could help to reduce cardiac rhythm monitoring for people with a low probability of AF detection and allow more intensive cardiac monitoring to be focused on people who are more likely to have AF and benefit. Participants will be consented for their data to be used in future research studies, providing a rich resource for stroke and cardiovascular research communities.

Effect of Neprilysin Inhibition on Left Ventricular Remodeling in Patients With Asymptomatic Left Ventricular Systolic Dysfunction Late After Myocardial Infarction.

BACKGROUND: Patients with left ventricular (LV) systolic dysfunction after myocardial infarction are at a high risk of developing heart failure. The addition of neprilysin inhibition to renin angiotensin system inhibition may result in greater attenuation of adverse LV remodeling as a result of increased levels of substrates for neprilysin with vasodilatory, antihypertrophic, antifibrotic, and sympatholytic effects. METHODS: We performed a prospective, multicenter, randomized, double-blind, active-comparator trial comparing sacubitril/valsartan 97/103 mg twice daily with valsartan 160 mg twice daily in patients ≥3 months after myocardial infarction with a LV ejection fraction ≤40% who were taking a renin angiotensin system inhibitor (equivalent dose of ramipril ≥2.5 mg twice daily) and a ß-blocker unless contraindicated or intolerant. Patients in New York Heart Association class ≥II or with signs and symptoms of heart failure were excluded. The primary outcome was change from baseline to 52 weeks in LV end-systolic volume index measured using cardiac magnetic resonance imaging. Secondary outcomes included other magnetic resonance imaging measurements of LV remodeling, change in NT-proBNP (N-terminal pro-B-type natriuretic peptide) and high-sensitivity cardiac troponin I, and a patient global assessment of change questionnaire. RESULTS: From July 2018 to June 2019, we randomized 93 patients with the following characteristics: mean age, 60.7±10.4 years; median time from myocardial infarction, 3.6 years (interquartile range, 1.2-7.2); mean LV ejection fraction, 36.8%±7.1%; and median NT-proBNP, 230 pg/mL (interquartile range, 124-404). Sacubitril/valsartan, compared with valsartan, did not significantly reduce LV end-systolic volume index; adjusted between-group difference, -1.9 mL/m2 (95% CI, -4.9 to 1.0); P=0.19. There were no significant between-group differences in NT-proBNP, high-sensitivity cardiac troponin I, LV end-diastolic volume index, left atrial volume index, LV ejection fraction, LV mass index, or patient global assessment of change. CONCLUSIONS: In patients with asymptomatic LV systolic dysfunction late after myocardial infarction, treatment with sacubitril/valsartan did not have a significant reverse remodeling effect compared with valsartan. Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT03552575.

Effect of Empagliflozin on Left Ventricular Volumes in Patients With Type 2 Diabetes, or Prediabetes, and Heart Failure With Reduced Ejection Fraction (SUGAR-DM-HF).

BACKGROUND: Sodium-glucose cotransporter 2 inhibitors reduce the risk of heart failure hospitalization and cardiovascular death in patients with heart failure and reduced ejection fraction (HFrEF). However, their effects on cardiac structure and function in HFrEF are uncertain. METHODS: We designed a multicenter, randomized, double-blind, placebo-controlled trial (the SUGAR-DM-HF trial [Studies of Empagliflozin and Its Cardiovascular, Renal and Metabolic Effects in Patients With Diabetes Mellitus, or Prediabetes, and Heart Failure]) to investigate the cardiac effects of empagliflozin in patients in New York Heart Association functional class II to IV with a left ventricular (LV) ejection fraction ≤40% and type 2 diabetes or prediabetes. Patients were randomly assigned 1:1 to empagliflozin 10 mg once daily or placebo, stratified by age (<65 and ≥65 years) and glycemic status (diabetes or prediabetes). The coprimary outcomes were change from baseline to 36 weeks in LV end-systolic volume indexed to body surface area and LV global longitudinal strain both measured using cardiovascular magnetic resonance. Secondary efficacy outcomes included other cardiovascular magnetic resonance measures (LV end-diastolic volume index, LV ejection fraction), diuretic intensification, symptoms (Kansas City Cardiomyopathy Questionnaire Total Symptom Score, 6-minute walk distance, B-lines on lung ultrasound, and biomarkers (including N-terminal pro-B-type natriuretic peptide). RESULTS: From April 2018 to August 2019, 105 patients were randomly assigned: mean age 68.7 (SD, 11.1) years, 77 (73.3%) male, 82 (78.1%) diabetes and 23 (21.9%) prediabetes, mean LV ejection fraction 32.5% (9.8%), and 81 (77.1%) New York Heart Association II and 24 (22.9%) New York Heart Association III. Patients received standard treatment for HFrEF. In comparison with placebo, empagliflozin reduced LV end-systolic volume index by 6.0 (95% CI, -10.8 to -1.2) mL/m2 (P=0.015). There was no difference in LV global longitudinal strain. Empagliflozin reduced LV end-diastolic volume index by 8.2 (95% CI, -13.7 to -2.6) mL/m2 (P=0.0042) and reduced N-terminal pro-B-type natriuretic peptide by 28% (2%-47%), P=0.038. There were no between-group differences in other cardiovascular magnetic resonance measures, diuretic intensification, Kansas City Cardiomyopathy Questionnaire Total Symptom Score, 6-minute walk distance, or B-lines. CONCLUSIONS: The sodium-glucose cotransporter 2 inhibitor empagliflozin reduced LV volumes in patients with HFrEF and type 2 diabetes or prediabetes. Favorable reverse LV remodeling may be a mechanism by which sodium-glucose cotransporter 2 inhibitors reduce heart failure hospitalization and mortality in HFrEF. Registration: URL: https://www.clinicaltrials.gov. Unique identifier: NCT03485092.

Laryngoscopic findings of age-related vocal fold atrophy are reliable but not specific.

OBJECTIVES: We aimed to evaluate the reliability of laryngoscopic features of vocal fold atrophy as assessed by novice otolaryngology trainees and expert laryngologists. DESIGN: Two expert fellowship-trained laryngologists and three non-expert otolaryngology resident trainees were recruited to view 50 anonymised laryngo-stroboscopic examinations of patients presenting with dysphonia and non-voice, laryngeal complaints. Reviewers were asked to stratify the patient's age, provide an opinion about the presence of age-related vocal fold atrophy and specify which laryngoscopy features were present to make the diagnosis. SETTING: Tertiary care laryngology practice. PARTICIPANTS: Two fellowship-trained laryngologists and three trainee otolaryngologists. MAIN OUTCOME MEASURES: Accuracy of age categorisation was determined and Kappa analysis was performed to assess inter-rater agreement. RESULTS: The mean age of patients was 54.9 years old with near equal male to female distribution. The overall accuracy of age category determination by raters was only 30.8%. Kappa analysis demonstrated fair agreement regarding the presence of vocal fold atrophy in non-expert reviewers, and moderate agreement amongst expert reviewers. Features of glottic gap, muscular atrophy of vocal folds and prominent vocal processes were all identified with high agreement (>80.0%). CONCLUSION: Our study illustrates that while raters can agree on the presence of age-related vocal fold atrophy, the findings may be non-specific and do not necessarily correlate with age.

Functional Heatmap: an automated and interactive pattern recognition tool to integrate time with multi-omics assays.

BACKGROUND: Life science research is moving quickly towards large-scale experimental designs that are comprised of multiple tissues, time points, and samples. Omic time-series experiments offer answers to three big questions: what collective patterns do most analytes follow, which analytes follow an identical pattern or synchronize across multiple cohorts, and how do biological functions evolve over time. Existing tools fall short of robustly answering and visualizing all three questions in a unified interface. RESULTS: Functional Heatmap offers time-series data visualization through a Master Panel page, and Combined page to answer each of the three time-series questions. It dissects the complex multi-omics time-series readouts into patterned clusters with associated biological functions. It allows users to identify a cascade of functional changes over a time variable. Inversely, Functional Heatmap can compare a pattern with specific biology respond to multiple experimental conditions. All analyses are interactive, searchable, and exportable in a form of heatmap, line-chart, or text, and the results are easy to share, maintain, and reproduce on the web platform. CONCLUSIONS: Functional Heatmap is an automated and interactive tool that enables pattern recognition in time-series multi-omics assays. It significantly reduces the manual labour of pattern discovery and comparison by transferring statistical models into visual clues. The new pattern recognition feature will help researchers identify hidden trends driven by functional changes using multi-tissues/conditions on a time-series fashion from omic assays.

Molecular alterations induced by Yersinia pestis, dengue virus and Staphylococcal enterotoxin B under severe stress.

BACKGROUND: Severe stress can have drastic and systemic effects with dire implications on the health and wellbeing of exposed individuals. Particularly, the effect of stress on the immune response to infection is of interest to public health because of its implications for vaccine efficacy and treatment strategies during stressful scenarios. Severe stress has previously been shown to cause an anergic state in the immune system that persists following exposure to a potent mitogen. METHODS: Transcriptome and microRNA changes were characterized using blood samples collected from U.S. Army Ranger candidates immediately before and after training, followed by exposure to representative pathogenic agents: Yersinia pestis, dengue virus 2, and Staphylococcal enterotoxin B (SEB). We employed experimental and computational approaches to characterize altered gene expression, processes, pathways, and regulatory networks mediating the host's response towards severe stress; to assess the protective immunity status of the stressed host towards infection; and to identify pathogen-induced biomarkers under severe stress conditions. RESULTS: We observed predicted inhibition of pathways significantly associated with lymphopoiesis, wound healing, inflammatory response, lymphocyte activation, apoptosis, and predicted activation of oxidative stress. Using weighted correlation network analyses, we demonstrated preservation of these pathways across infection and stress combinations. Regulatory networks comprising a common set of upstream regulators: transcription factors, microRNAs and post-translational regulators (kinases and phosphatases) may be drivers of molecular alterations leading to compromised protective immunity. Other sets of transcripts were persistently altered in both the pre- and post-stress conditions due to the host's response to each pathogenic agent, forming specific molecular signatures with the potential to distinguish infection from that of severe stress. CONCLUSIONS: Our results suggest that severe stress alters molecules implicated in the development of leukopoietic stem cells, thereby leading to depletion of cellular and molecular repertoires of protective immunity. Suppressed molecules mediating membrane trafficking of recycling endosomes, membrane translocation and localization of the antigen processing mechanisms and cell adhesions indicate suboptimal antigen presentation, impaired formation of productive immunological synapses, and inhibited T-cell activations. These factors may collectively be responsible for compromised protective immunity (infection susceptibility, delayed wound healing, and poor vaccine response) observed in people under severe stress.

Hematologic malignancies of the larynx: A single institution review.

BACKGROUND: Primary hematologic malignancies of the larynx are rare diagnoses, accounting for less than 1% of all laryngeal tumors. They most commonly present as submucosal masses of the supraglottis, with symptoms including hoarseness, dysphagia, dyspnea and rarely cervical lymphadenopathy. PURPOSE: METHODS: Retrospective case series of patients in a tertiary academic laryngeal practice with hematologic malignancy of the larynx presenting over a 10 year period; charts were reviewed for diagnosis, symptoms, treatment, and outcomes. RESULTS: 12 patients were found to have primary presentation of a hematologic malignancy within the larynx between 2009 and 2019. A submucosal mass was the most common finding, and hoarseness was the most common symptom. Local control of disease was high. Airway obstruction was managed with tracheostomy. Several patients required tube feeding prior to disease control. Most patients underwent radiation therapy and chemotherapy, although surgery alone was effective in patients with isolated disease. CONCLUSIONS: Hematologic malignancies of the larynx are rare but treatable. Biopsy is the mainstay of diagnosis, and imaging may be helpful to exclude diseases with a similar physical presentation (i.e., laryngocele). Prognosis depends on diagnosis but is generally favorable.

Association is not causation: treatment effects cannot be estimated from observational data in heart failure.

Aims: Treatment 'effects' are often inferred from non-randomized and observational studies. These studies have inherent biases and limitations, which may make therapeutic inferences based on their results unreliable. We compared the conflicting findings of these studies to those of prospective randomized controlled trials (RCTs) in relation to pharmacological treatments for heart failure (HF). Methods and results: We searched Medline and Embase to identify studies of the association between non-randomized drug therapy and all-cause mortality in patients with HF until 31 December 2017. The treatments of interest were: angiotensin-converting enzyme inhibitors, angiotensin receptor blockers, beta-blockers, mineralocorticoid receptor antagonists (MRAs), statins, and digoxin. We compared the findings of these observational studies with those of relevant RCTs. We identified 92 publications, reporting 94 non-randomized studies, describing 158 estimates of the 'effect' of the six treatments of interest on all-cause mortality, i.e. some studies examined more than one treatment and/or HF phenotype. These six treatments had been tested in 25 RCTs. For example, two pivotal RCTs showed that MRAs reduced mortality in patients with HF with reduced ejection fraction. However, only one of 12 non-randomized studies found that MRAs were of benefit, with 10 finding a neutral effect, and one a harmful effect. Conclusion: This comprehensive comparison of studies of non-randomized data with the findings of RCTs in HF shows that it is not possible to make reliable therapeutic inferences from observational associations. While trials undoubtedly leave gaps in evidence and enrol selected participants, they clearly remain the best guide to the treatment of patients.

PanoromiX: a time-course network medicine platform integrating molecular assays and pathophenotypic data.

BACKGROUND: Network medicine aims to map molecular perturbations of any given diseases onto complex networks with functional interdependencies that underlie a pathological phenotype. Furthermore, investigating the time dimension of disease progression from a network perspective is key to gaining key insights to the disease process and to identify diagnostic or therapeutic targets. Existing platforms are ineffective to modularize the large complex systems into subgroups and consolidate heterogeneous data to web-based interactive animation. RESULTS: We have developed PanoromiX platform, a data-agnostic dynamic interactive visualization web application, enables the visualization of outputs from genome based molecular assays onto modular and interactive networks that are correlated with any pathophenotypic data (MRI, Xray, behavioral, etc.) over a time course all in one pane. As a result, PanoromiX reveals the complex organizing principles that orchestrate a disease-pathology from a gene regulatory network (nodes, edges, hubs, etc.) perspective instead of snap shots of assays. Without extensive programming experience, users can design, share, and interpret their dynamic networks through the PanoromiX platform with rich built-in functionalities. CONCLUSIONS: This emergent tool of network medicine is the first to visualize the interconnectedness of tailored genome assays to pathological networks and phenotypes for cells or organisms in a data-agnostic manner. As an advanced network medicine tool, PanoromiX allows monitoring of panel of biomarker perturbations over the progression of diseases, disease classification based on changing network modules that corresponds to specific patho-phenotype as opposed to clinical symptoms, systematic exploration of complex molecular interactions and distinct disease states via regulatory network changes, and the discovery of novel diagnostic and therapeutic targets.