Molecular phenotypes of critical illness confer prognostic and biological enrichment in sub-Saharan Africa: a prospective cohort study from Uganda.

The generalisability of critical illness molecular phenotypes to low- and middle-income countries (LMICs) is unknown. We show that molecular phenotypes derived in high-income countries (hyperinflammatory and hypoinflammatory, reactive and uninflamed) stratify sepsis patients in Uganda by physiological severity, mortality risk and dysregulation of key pathobiological domains. A classifier model including data available at the LMIC bedside modestly discriminated phenotype assignment (area under the receiver operating characteristic curve (AUROC) 0.

Unsupervised Classification of the Host Response Identifies Dominant Pathobiological Signatures of Sepsis in Sub-Saharan Africa.

Rationale: The global burden of sepsis is concentrated in sub-Saharan Africa, where inciting pathogens are diverse and HIV co-infection is a major driver of poor outcomes. Biological heterogeneity inherent to sepsis in this setting is poorly defined.

Objectives: To identify dominant pathobiological signatures of sepsis in sub-Saharan Africa and their relationship to clinical phenotypes, patient outcomes, and biological classifications of sepsis identified in high-income-countries (HICs).

Correlations between Mental Health, Physical Activity, and Body Composition in American College Students after the COVID-19 Pandemic Lockdown.

Restrictions associated with the COVID-19 pandemic had forced American college students to significantly reduce their daily energy expenditure and increase their sedentary behaviors, thus presumably increasing mental health symptoms, decreasing physical activity levels, and enhancing the promotion of unhealthy eating habits. This study aimed to explore the correlations between mental health symptoms, physical activity levels, and body composition in college students in the years following the pandemic, focusing on the lingering effects of lockdown measures. American college students completed pre-existing, well-validated surveys for both mental health (Hospital Anxiety and Depression Scale) and physical activity (International Physical Activity Questionnaire-Long Form).

A Transcriptomic Classifier Model Identifies High-Risk Endotypes in a Prospective Study of Sepsis in Uganda.

Objectives: In high-income countries (HICs), sepsis endotypes defined by distinct pathobiological mechanisms, mortality risks, and responses to corticosteroid treatment have been identified using blood transcriptomics. The generalizability of these endotypes to low-income and middle-income countries (LMICs), where the global sepsis burden is concentrated, is unknown. We sought to determine the prevalence, prognostic relevance, and immunopathological features of HIC-derived transcriptomic sepsis endotypes in sub-Saharan Africa.

Broad genic repression domains signify enhanced silencing of oncogenes.

Cancers result from a set of genetic and epigenetic alterations. Most known oncogenes were identified by gain-of-function mutations in cancer, yet little is known about their epigenetic features. Through integrative analysis of 11,596 epigenomic profiles and mutations from >8200 tumor-normal pairs, we discover broad genic repression domains (BGRD) on chromatin as an epigenetic signature for oncogenes.

Machine learning uncovers cell identity regulator by histone code.

Conversion between cell types, e.g., by induced expression of master transcription factors, holds great promise for cellular therapy.

Ssd1 and Gcn2 suppress global translation efficiency in replicatively aged yeast while their activation extends lifespan.

Translational efficiency correlates with longevity, yet its role in lifespan determination remains unclear. Using ribosome profiling, translation efficiency is globally reduced during replicative aging in budding yeast by at least two mechanisms: Firstly, Ssd1 is induced during aging, sequestering mRNAs to P-bodies. Furthermore, Ssd1 overexpression in young cells reduced translation and extended lifespan, while loss of Ssd1 reduced the translational deficit of old cells and shortened lifespan.

Pathway crosstalk effects: Shrinkage and disentanglement using a Bayesian hierarchical model.

Identifying the biological pathways that are related to various clinical phenotypes is an important concern in biomedical research. Based on estimated expression levels and/or p-values, over-representation analysis (ORA) methods provide rankings of pathways, but they are tainted because pathways overlap. This crosstalk phenomenon has not been rigorously studied and classical ORA does not take into consideration: (i) that crosstalk effects in cases of overlapping pathways can cause incorrect rankings of pathways, (ii) that crosstalk effects can cause both excess type I errors and type II errors, (iii) that rankings of small pathways are unreliable and (iv) that type I error rates can be inflated due to multiple comparisons of pathways.

Analysis and correction of crosstalk effects in pathway analysis.

Identifying the pathways that are significantly impacted in a given condition is a crucial step in understanding the underlying biological phenomena. All approaches currently available for this purpose calculate a P-value that aims to quantify the significance of the involvement of each pathway in the given phenotype. These P-values were previously thought to be independent.

Clinical trials simulation: a statistical approach.

A generic template for clinical trials simulations that are typically required by statisticians is developed. Realistic clinical trials data sets are created using a unifying model that allows general correlation structures for endpoint*timepoint data and nonnormal distributions (including time-to-event), and computationally efficient algorithms are presented. The model allows for patient dropout and noncompliance.