Computational drug repositioning identifies niclosamide and tribromsalan as inhibitors of Mycobacterium tuberculosis and Mycobacterium abscessus.

Tuberculosis (TB) is still a major global health challenge, killing over 1.5 million people each year, and hence, there is a need to identify and develop novel treatments for Mycobacterium tuberculosis (M. tuberculosis). The prevalence of infections caused by nontuberculous mycobacteria (NTM) is also increasing and has overtaken TB cases in the United States and much of the developed world. Mycobacterium abscessus (M. abscessus) is one of the most frequently encountered NTM and is difficult to treat. We describe the use of drug-disease association using a semantic knowledge graph approach combined with machine learning models that has enabled the identification of several molecules for testing anti-mycobacterial activity. We established that niclosamide (M. tuberculosis IC90 2.95 µM; M. abscessus IC90 59.1 µM) and tribromsalan (M. tuberculosis IC90 76.92 µM; M. abscessus IC90 147.4 µM) inhibit M. tuberculosis and M. abscessus in vitro. To investigate the mode of action, we determined the transcriptional response of M. tuberculosis and M. abscessus to both compounds in axenic log phase, demonstrating a broad effect on gene expression that differed from known M. tuberculosis inhibitors. Both compounds elicited transcriptional responses indicative of respiratory pathway stress and the dysregulation of fatty acid metabolism.

Optimizing Virtual Health Technologies Through a Performance-based Readiness Model: Lessons From the Field Deployment of National Emergency Tele-Critical Care Network.

INTRODUCTION: The advancement of the Army's National Emergency Tele-Critical Care Network (NETCCN) and planned evolution to an Intelligent Medical System rest on a digital transformation characterized by the application of analytic rigor anchored and machine learning.The goal is an enduring capability for telecritical care in support of the Nation's warfighters and, more broadly, for emergency response, crisis management, and mass casualty situations as the number and intensity of disasters increase nationwide. That said, technology alone is unlikely to solve the most pressing issues in operational medicine and combat casualty care. MATERIALS AND METHODS: A total performance system (TPS) creates opportunities to address vulnerabilities and overcome barriers to success. As applied during the NETCCN project, the TPS captures the best performance-centric information and know-how, increasing the potential to save lives, improve readiness, and accomplish missions. RESULTS: The purpose of this project was to apply a performance-based readiness model to aid in the evaluation of Army telehealth technologies. Through various user-facing surveys, polls, and reporting techniques, the project aimed to measure the perceived value of telehealth technologies within a sample of the project team member population. By providing a detailed approach to the collection of lessons learned, researchers were able to determine the importance of information and methods versus a focus on technology alone. The use of an emoji-based feedback assessment indicated that most lessons learned were helpful to the project team. CONCLUSIONS: Through the NETCCN TPS, we have been able to address product-related measures, knowledge of product efficacy, project metrics, and many implementation considerations that can be further investigated by setting and engagement type. Through the Technology in Disaster Environments learning accelerator, it was possible to rapidly acquire, process, organize, and disseminate best practices and learnings in near real time, providing a critical feedback and improvement loop.

Project APRED: A web-based data analytics platform for supporting community disaster resilience.

In this paper, we introduce the Analysis Platform for Risk, Resilience, and Expenditure in Disasters (APRED)-a disaster-analytic platform developed for crisis practitioners and economic developers across the United States (US). APRED provides practitioners with a centralized platform for exploring disaster resilience and vulnerability profiles of all counties across the US. The platform comprises five sections including: (1) Disaster Resilience Index, (2) Business Vulnerability Index, (3) Disaster Declaration History, (4) County Profile, and (5) Storm History sections. We further describe our end-to-end human-centered design and engineering process that involved contextual inquiry, community-based participatory design, and rapid prototyping with the support of US Economic Development Administration representatives and regional economic developers across the US. Findings from our study revealed that distributed cognition, content heuristic, shareability, and human-centered systems are crucial considerations for developing data-intensive visualization platforms for resilience planning. We discuss the implications of these findings and inform future research on developing sociotechnical visualization platforms to support resilience planning.

Socially vulnerable populations adoption of technology to address lifestyle changes amid COVID-19 in the US.

The COVID-19 global pandemic has changed every facet of our lives overnight and has resulted in many challenges and opportunities. Utilizing the Lens of Vulnerability we investigate how disparities in technology adoption affect activities of daily living. In this paper, we analyze the existing literature and case studies regarding how the lifestyles of socially vulnerable populations have changed during the pandemic in terms of technology adoption. Socially vulnerable populations, such as racial and ethnic minorities, people with disabilities, older adults, children, and the socially isolated, are specifically addressed because they are groups of people who have been significantly and disproportionately affected by the pandemic. This paper emphasizes that despite seeing changes in and research on technology adoption across healthcare, employment, and education, the impact of COVID-19 in government and social services and activities of daily living is underdeveloped. The study concludes by offering practical and academic recommendations and future research directions. Lessons learned from the current pandemic and an understanding of the differential technology adoption for activities of daily living amid a disaster will help emergency managers, academics, and government officals prepare for and respond to future crises.

Knowledge graph analytics platform with LINCS and IDG for Parkinson's disease target illumination.

BACKGROUND: LINCS, "Library of Integrated Network-based Cellular Signatures", and IDG, "Illuminating the Druggable Genome", are both NIH projects and consortia that have generated rich datasets for the study of the molecular basis of human health and disease. LINCS L1000 expression signatures provide unbiased systems/omics experimental evidence. IDG provides compiled and curated knowledge for illumination and prioritization of novel drug target hypotheses. Together, these resources can support a powerful new approach to identifying novel drug targets for complex diseases, such as Parkinson's disease (PD), which continues to inflict severe harm on human health, and resist traditional research approaches. RESULTS: Integrating LINCS and IDG, we built the Knowledge Graph Analytics Platform (KGAP) to support an important use case: identification and prioritization of drug target hypotheses for associated diseases. The KGAP approach includes strong semantics interpretable by domain scientists and a robust, high performance implementation of a graph database and related analytical methods. Illustrating the value of our approach, we investigated results from queries relevant to PD. Approved PD drug indications from IDG's resource DrugCentral were used as starting points for evidence paths exploring chemogenomic space via LINCS expression signatures for associated genes, evaluated as target hypotheses by integration with IDG. The KG-analytic scoring function was validated against a gold standard dataset of genes associated with PD as elucidated, published mechanism-of-action drug targets, also from DrugCentral. IDG's resource TIN-X was used to rank and filter KGAP results for novel PD targets, and one, SYNGR3 (Synaptogyrin-3), was manually investigated further as a case study and plausible new drug target for PD. CONCLUSIONS: The synergy of LINCS and IDG, via KG methods, empowers graph analytics methods for the investigation of the molecular basis of complex diseases, and specifically for identification and prioritization of novel drug targets. The KGAP approach enables downstream applications via integration with resources similarly aligned with modern KG methodology. The generality of the approach indicates that KGAP is applicable to many disease areas, in addition to PD, the focus of this paper.

Predicting COVID-19-Related Health Care Resource Utilization Across a Statewide Patient Population: Model Development Study.

BACKGROUND: The COVID-19 pandemic has highlighted the inability of health systems to leverage existing system infrastructure in order to rapidly develop and apply broad analytical tools that could inform state- and national-level policymaking, as well as patient care delivery in hospital settings. The COVID-19 pandemic has also led to highlighted systemic disparities in health outcomes and access to care based on race or ethnicity, gender, income-level, and urban-rural divide. Although the United States seems to be recovering from the COVID-19 pandemic owing to widespread vaccination efforts and increased public awareness, there is an urgent need to address the aforementioned challenges. OBJECTIVE: This study aims to inform the feasibility of leveraging broad, statewide datasets for population health-driven decision-making by developing robust analytical models that predict COVID-19-related health care resource utilization across patients served by Indiana's statewide Health Information Exchange. METHODS: We leveraged comprehensive datasets obtained from the Indiana Network for Patient Care to train decision forest-based models that can predict patient-level need of health care resource utilization. To assess these models for potential biases, we tested model performance against subpopulations stratified by age, race or ethnicity, gender, and residence (urban vs rural). RESULTS: For model development, we identified a cohort of 96,026 patients from across 957 zip codes in Indiana, United States. We trained the decision models that predicted health care resource utilization by using approximately 100 of the most impactful features from a total of 1172 features created. Each model and stratified subpopulation under test reported precision scores >70%, accuracy and area under the receiver operating curve scores >80%, and sensitivity scores approximately >90%. We noted statistically significant variations in model performance across stratified subpopulations identified by age, race or ethnicity, gender, and residence (urban vs rural). CONCLUSIONS: This study presents the possibility of developing decision models capable of predicting patient-level health care resource utilization across a broad, statewide region with considerable predictive performance. However, our models present statistically significant variations in performance across stratified subpopulations of interest. Further efforts are necessary to identify root causes of these biases and to rectify them.

TIGA: target illumination GWAS analytics.

MOTIVATION: Genome-wide association studies can reveal important genotype-phenotype associations; however, data quality and interpretability issues must be addressed. For drug discovery scientists seeking to prioritize targets based on the available evidence, these issues go beyond the single study. RESULTS: Here, we describe rational ranking, filtering and interpretation of inferred gene-trait associations and data aggregation across studies by leveraging existing curation and harmonization efforts. Each gene-trait association is evaluated for confidence, with scores derived solely from aggregated statistics, linking a protein-coding gene and phenotype. We propose a method for assessing confidence in gene-trait associations from evidence aggregated across studies, including a bibliometric assessment of scientific consensus based on the iCite relative citation ratio, and meanRank scores, to aggregate multivariate evidence.This method, intended for drug target hypothesis generation, scoring and ranking, has been implemented as an analytical pipeline, available as open source, with public datasets of results, and a web application designed for usability by drug discovery scientists. AVAILABILITY AND IMPLEMENTATION: Web application, datasets and source code via https://unmtid-shinyapps.net/tiga/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Time-series transcriptomics reveals a BBX32-directed control of acclimation to high light in mature Arabidopsis leaves.

The photosynthetic capacity of mature leaves increases after several days' exposure to constant or intermittent episodes of high light (HL) and is manifested primarily as changes in chloroplast physiology. How this chloroplast-level acclimation to HL is initiated and controlled is unknown. From expanded Arabidopsis leaves, we determined HL-dependent changes in transcript abundance of 3844 genes in a 0-6 h time-series transcriptomics experiment. It was hypothesized that among such genes were those that contribute to the initiation of HL acclimation. By focusing on differentially expressed transcription (co-)factor genes and applying dynamic statistical modelling to the temporal transcriptomics data, a regulatory network of 47 predominantly photoreceptor-regulated transcription (co-)factor genes was inferred. The most connected gene in this network was B-BOX DOMAIN CONTAINING PROTEIN32 (BBX32). Plants overexpressing BBX32 were strongly impaired in acclimation to HL and displayed perturbed expression of photosynthesis-associated genes under LL and after exposure to HL. These observations led to demonstrating that as well as regulation of chloroplast-level acclimation by BBX32, CRYPTOCHROME1, LONG HYPOCOTYL5, CONSTITUTIVELY PHOTOMORPHOGENIC1 and SUPPRESSOR OF PHYA-105 are important. In addition, the BBX32-centric gene regulatory network provides a view of the transcriptional control of acclimation in mature leaves distinct from other photoreceptor-regulated processes, such as seedling photomorphogenesis.

Acceptance of COVID-19 Vaccination Among Health System Personnel.

INTRODUCTION: One-third of the general public will not accept Coronavirus disease 2019 (COVID-19) vaccination but factors influencing vaccine acceptance among health care personnel (HCP) are not known. We investigated barriers and facilitators to vaccine acceptance within 3 months of regulatory approval (primary outcome) among adult employees and students at a tertiary-care, academic medical center. METHODS: We used a cross-sectional survey design with multivariable logistic regression. Covariates included age, gender, educational attainment, self-reported health status, concern about COVID-19, direct patient interaction, and prior influenza immunization. RESULTS: Of 18,250 eligible persons, 3,347 participated. Two in 5 (40.5%) HCP intend to delay (n = 1020; 30.6%) or forgo (n = 331; 9.9%) vaccination. Male sex (adjusted OR [aOR], 2.43; 95% confidence interval [CI], 2.00-2.95; P < .001), prior influenza vaccination (aOR, 2.35; 95% CI, 1.75-3.18; P < .001), increased concern about COVID-19 (aOR, 2.40; 95% CI, 2.07-2.79; P < .001), and postgraduate education (aOR, 1.41; 95% CI, 1.21-1.65; P < .001) - but not age, direct patient interaction, or self-reported overall health - were associated with vaccine acceptance in multivariable analysis. Barriers to vaccination included concerns about long-term side effects (n = 1197, 57.1%), safety (n = 1152, 55.0%), efficacy (n = 777, 37.1%), risk-to-benefit ratio (n = 650, 31.0%), and cost (n = 255, 12.2%).Subgroup analysis of Black respondents indicates greater hesitancy to accept vaccination (only 24.8% within 3 months; aOR 0.13; 95% CI, 0.08-0.21; P < .001). CONCLUSIONS: Many HCP intend to delay or refuse COVID-19 vaccination. Policymakers should impartially address concerns about safety, efficacy, side effects, risk-to-benefit ratio, and cost. Further research with minority subgroups is urgently needed.