Correction: Investigating ethical tradeoffs in crisis standards of care through simulation of ventilator allocation protocols.

[This corrects the article DOI: 10.1371/journal.pone.

Correction: Ventilation during COVID-19 in a school for students with intellectual and developmental disabilities (IDD).

[This corrects the article DOI: 10.1371/journal.pone.

Investigating Ethical Tradeoffs in Crisis Standards of Care through Simulation of Ventilator Allocation Protocols.

Introduction: Arguments over the appropriate Crisis Standards of Care (CSC) for public health emergencies often assume that there is a tradeoff between saving the most lives, saving the most life-years, and preventing racial disparities. However, these assumptions have rarely been explored empirically. To quantitatively characterize possible ethical tradeoffs, we aimed to simulate the implementation of five proposed CSC protocols for rationing ventilators in the context of the COVID-19 pandemic.

Ventilation during COVID-19 in a school for students with intellectual and developmental disabilities (IDD).

Background: This study examined the correlation of classroom ventilation (air exchanges per hour (ACH)) and exposure to CO ≥1,000 ppm with the incidence of SARS-CoV-2 over a 20-month period in a specialized school for students with intellectual and developmental disabilities (IDD). These students were at a higher risk of respiratory infection from SARS-CoV-2 due to challenges in tolerating mitigation measures (e.g.

Common Data Element Collection in Underserved School Communities: Challenges and Recommendations.

Objectives: To provide recommendations for future common data element (CDE) development and collection that increases community partnership, harmonizes data interpretation, and continues to reduce barriers of mistrust between researchers and underserved communities.

Methods: We conducted a cross-sectional qualitative and quantitative evaluation of mandatory CDE collection among Rapid Acceleration of Diagnostics-Underserved Populations Return to School project teams with various priority populations and geographic locations in the United States to: (1) compare racial and ethnic representativeness of participants completing CDE questions relative to participants enrolled in project-level testing initiatives and (2) identify the amount of missing CDE data by CDE domain. Additionally, we conducted analyses stratified by aim-level variables characterizing CDE collection strategies.

A systems genomics approach uncovers molecular associates of RSV severity.

Respiratory syncytial virus (RSV) infection results in millions of hospitalizations and thousands of deaths each year. Variations in the adaptive and innate immune response appear to be associated with RSV severity. To investigate the host response to RSV infection in infants, we performed a systems-level study of RSV pathophysiology, incorporating high-throughput measurements of the peripheral innate and adaptive immune systems and the airway epithelium and microbiota.

Gene Expression Risk Scores for COVID-19 Illness Severity.

Background: The correlates of coronavirus disease 2019 (COVID-19) illness severity following infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are incompletely understood.

Methods: We assessed peripheral blood gene expression in 53 adults with confirmed SARS-CoV-2 infection clinically adjudicated as having mild, moderate, or severe disease. Supervised principal components analysis was used to build a weighted gene expression risk score (WGERS) to discriminate between severe and nonsevere COVID-19.

Gene Expression Risk Scores for COVID-19 Illness Severity.

Background: The correlates of COVID-19 illness severity following infection with SARS-Coronavirus 2 (SARS-CoV-2) are incompletely understood.

Methods: We assessed peripheral blood gene expression in 53 adults with confirmed SARS-CoV-2-infection clinically adjudicated as having mild, moderate or severe disease. Supervised principal components analysis was used to build a weighted gene expression risk score (WGERS) to discriminate between severe and non-severe COVID.

Eight practices for data management to enable team data science.

Introduction: In clinical and translational research, data science is often and fortuitously integrated with data collection. This contrasts to the typical position of data scientists in other settings, where they are isolated from data collectors. Because of this, effective use of data science techniques to resolve translational questions requires innovation in the organization and management of these data.

Airway gene-expression classifiers for respiratory syncytial virus (RSV) disease severity in infants.

Background: A substantial number of infants infected with RSV develop severe symptoms requiring hospitalization. We currently lack accurate biomarkers that are associated with severe illness.

Method: We defined airway gene expression profiles based on RNA sequencing from nasal brush samples from 106 full-tem previously healthy RSV infected subjects during acute infection (day 1-10 of illness) and convalescence stage (day 28 of illness).

Airway Gene Expression Correlates of Respiratory Syncytial Virus Disease Severity and Microbiome Composition in Infants.

Background: Respiratory syncytial virus (RSV) is the leading cause of severe respiratory disease in infants. The causes and correlates of severe illness in the majority of infants are poorly defined.

Methods: We recruited a cohort of RSV-infected infants and simultaneously assayed the molecular status of their airways and the presence of airway microbiota.

Temporal Dysbiosis of Infant Nasal Microbiota Relative to Respiratory Syncytial Virus Infection.

Background: Respiratory syncytial virus (RSV) is a leading cause of infant respiratory disease. Infant airway microbiota has been associated with respiratory disease risk and severity. The extent to which interactions between RSV and microbiota occur in the airway, and their impact on respiratory disease susceptibility and severity, are unknown.

Aims, Study Design, and Enrollment Results From the Assessing Predictors of Infant Respiratory Syncytial Virus Effects and Severity Study.

Background: The majority of infants hospitalized with primary respiratory syncytial virus (RSV) infection have no obvious risk factors for severe disease.

Objective: The aim of this study (Assessing Predictors of Infant RSV Effects and Severity, AsPIRES) was to identify factors associated with severe disease in full-term healthy infants younger than 10 months with primary RSV infection.

Methods: RSV infected infants were enrolled from 3 cohorts during consecutive winters from August 2012 to April 2016 in Rochester, New York.

Virus-Specific Antibody, Viral Load, and Disease Severity in Respiratory Syncytial Virus Infection.

Background: Maternally derived serum antibody and viral load are thought to influence disease severity in primary respiratory syncytial virus (RSV) infection. As part of the AsPIRES study of RSV pathogenesis, we correlated various serum antibody concentrations and viral load with disease severity.

Methods: Serum neutralizing antibody titers and levels of immunoglobulin G (IgG) to RSV fusion protein (F), attachment proteins of RSV group A and B, the CX3C region of G, and nasal viral load were measured in 139 full-term previously healthy infants with primary RSV infection and correlated with illness severity.

Index case of acute myeloid leukemia in a family harboring a novel germ line mutation.

The persistence of a mutation at the time of complete remission warrants germ line analysis.Not all patients harboring germ line mutations have a family history of AML.

Association of Dynamic Changes in the CD4 T-Cell Transcriptome With Disease Severity During Primary Respiratory Syncytial Virus Infection in Young Infants.

Background: Nearly all children are infected with respiratory syncytial virus (RSV) within the first 2 years of life, with a minority developing severe disease (1%-3% hospitalized). We hypothesized that an assessment of the adaptive immune system, using CD4+ T-lymphocyte transcriptomics, would identify gene expression correlates of disease severity.

Methods: Infants infected with RSV representing extremes of clinical severity were studied.

Transcriptomic Biomarkers to Discriminate Bacterial from Nonbacterial Infection in Adults Hospitalized with Respiratory Illness.

Lower respiratory tract infection (LRTI) commonly causes hospitalization in adults. Because bacterial diagnostic tests are not accurate, antibiotics are frequently prescribed. Peripheral blood gene expression to identify subjects with bacterial infection is a promising strategy.

Development of a Global Respiratory Severity Score for Respiratory Syncytial Virus Infection in Infants.

Background: Respiratory syncytial virus (RSV) infection in infants has recognizable clinical signs and symptoms. However, quantification of disease severity is difficult, and published scores remain problematic. Thus, as part of a RSV pathogenesis study, we developed a global respiratory severity score (GRSS) as a research tool for evaluating infants with primary RSV infection.

The Healthy Infant Nasal Transcriptome: A Benchmark Study.

Responses by resident cells are likely to play a key role in determining the severity of respiratory disease. However, sampling of the airways poses a significant challenge, particularly in infants and children. Here, we report a reliable method for obtaining nasal epithelial cell RNA from infants for genome-wide transcriptomic analysis, and describe baseline expression characteristics in an asymptomatic cohort.

Plant MetGenMAP: an integrative analysis system for plant systems biology.

The information and resources generated from diverse "omics" technologies provide opportunities for producing novel biological knowledge. It is essential to integrate various kinds of biological information and large-scale omics data sets through systematic analysis in order to describe and understand complex biological phenomena. For this purpose, we have developed a Web-based system, Plant MetGenMAP, which can comprehensively integrate and analyze large-scale gene expression and metabolite profile data sets along with diverse biological information.