Creation of a non-Western humanized gnotobiotic mouse model through the transplantation of rural African fecal microbiota.

There is increasing evidence that microbes residing within the intestines (gut microbiota) play important roles in the well-being of humans. Yet, there are considerable challenges in determining the specific role of gut microbiota in human diseases owing to the complexity of diverse internal and environmental factors that can contribute to diseases. Mice devoid of all microorganisms (germ-free mice) can be colonized with human stool samples to examine the specific contribution of the gut microbiota to a disease.

RT education and COVID-19 pneumonia discharge quality.

Background: There is a lack of data assessing the influence of respiratory therapist (RT) education on clinical outcomes. The primary objective of this study was to evaluate the impact of RTs holding advanced degrees or completing adult critical care competencies on discharge outcomes of patients with COVID-19 pneumonia.

Study Design And Methods: This retrospective, cross-sectional study included adults with confirmed COVID-19 admitted to the hospital for at least three days between March-May 2020.

COVID-19 infection and mortality among non-pregnant indigenous adults in Mexico 2020-2022: Impact of marginalisation.

Background: Indigenous individuals have higher rates of mortality and poverty in Mexico and more than half are marginalised, and COVID-19 pandemic aggravated the existing burden of health disparities. We aimed to analyse the effects of being indigenous and marginalised on coronavirus (COVID-19) infection fatality in Mexico.

Methods: We identified 3 424 690 non-pregnant, COVID-19 positive adults ≥19 years in the Mexico national COVID-19 database with known date of symptom.

Prevalence and Treatment for Alcohol Use Disorders Based on Kentucky Medicaid 2012-2019 Datasets.

Alcohol use is the leading substance use in the United States. Persons with alcohol use disorder (AUD) face enormous health consequences and family problems. Analysis of Medicaid enrollee data is critical to understand different aspects of AUD and the treatment utilization for patients with AUD.

Development and Validation of Biosafety Climate Scale for Biological and Biomedical Science Laboratories in the United States.

Industry-specific safety climate scales that measure safety status have been published, however, nothing specific to biological laboratories has ever been established. This study aimed to develop and validate a biosafety climate (BSCL) scale unique for research professionals (RPs) and biosafety professionals (BPs) at teaching and research biological laboratories affiliated to public universities in the United States. BSCL scale was developed from literature review.

In-vivo platelet activation and aggregation during and after acute atherothrombotic myocardial infarction in patients with and without Type-2 diabetes mellitus treated with ticagrelor.

Introduction: Patients with type-2 diabetes are twice as likely to suffer from acute myocardial infarction (AMI) and have a higher incidence of recurrent events than their non-diabetic counterparts. Ticagrelor is a platelet inhibitor known to reduce major adverse cardiovascular events (MACE) in AMI patients. This study measures the level and change in platelet activation and aggregation at the time of and following an AMI in patients with and without diabetes treated with ticagrelor.

Applications of Bayesian shrinkage prior models in clinical research with categorical responses.

Background: Prediction and classification algorithms are commonly used in clinical research for identifying patients susceptible to clinical conditions such as diabetes, colon cancer, and Alzheimer's disease. Developing accurate prediction and classification methods benefits personalized medicine. Building an excellent predictive model involves selecting the features that are most significantly associated with the outcome.

Prevalence and Treatment Utilization of Patients Diagnosed with Depression and Anxiety Disorders Based on Kentucky Medicaid 2012-2019 Datasets.

Objectives: To examine the prevalence and treatment utilization of patients diagnosed with Depression and Anxiety Disorders (DAD) based on Kentucky Medicaid 2012-2019 datasets.

Methods: The study was based on Kentucky Medicaid claims data from 2012 through 2019 for patients 14 years and older. We constructed yearly patient-level databases using ICD_9 CM and ICD_10 CM codes to identify the patients with DAD, using the Current Procedure Terminology (CPT) codes to identify individual psychotherapy and group psychotherapy and using the National drug codes to categorize pharmacotherapy.

Prevalence of unmasked and improperly masked behavior in indoor public areas during the COVID-19 pandemic: Analysis of a stratified random sample from Louisville, Kentucky.

Wearing a facial mask can limit COVID-19 transmission. Measurements of communities' mask use behavior have mostly relied on self-report. This study's objective was to devise a method to measure the prevalence of improper mask use and no mask use in indoor public areas without relying on self-report.

County-level demographic, social, economic, and lifestyle correlates of COVID-19 infection and death trajectories during the first wave of the pandemic in the United States.

Background: The US COVID-19 epidemic impacted counties differently across space and time, though large-scale transmission dynamics are unclear. The study's objective was to group counties with similar trajectories of COVID-19 cases and deaths and identify county-level correlates of the distinct trajectory groups.

Methods: Daily COVID-19 cases and deaths were obtained from 3141 US counties from January through June 2020.

Bayesian Decision-Theoretic Methods for Survival Data using Stochastic Optimization.

We introduce a principled method for Bayesian subgroup analysis. The approach is based on casting subgroup analysis as a Bayesian decision problem. The two main innovations are: (1) the explicit consideration of a "subgroup report," comprising multiple subpopulations; and (2) adapting an inhomogeneous Markov chain Monte Carlo simulation scheme to implement stochastic optimization.

Bayesian Hierarchical Model for Protein Identifications.

In proteomics, identification of proteins from complex mixtures of proteins extracted from biological samples is an important problem. Among the experimental technologies, Mass-Spectrometry (MS) is the most popular one. Protein identification from MS data typically relies on a "two-step" procedure of identifying the peptide first followed by the separate protein identification procedure next.

Zodiac: A Comprehensive Depiction of Genetic Interactions in Cancer by Integrating TCGA Data.

Background: Genetic interactions play a critical role in cancer development. Existing knowledge about cancer genetic interactions is incomplete, especially lacking evidences derived from large-scale cancer genomics data. The Cancer Genome Atlas (TCGA) produces multimodal measurements across genomics and features of thousands of tumors, which provide an unprecedented opportunity to investigate the interplays of genes in cancer.

Bayesian hierarchical models for protein networks in single-cell mass cytometry.

We propose a class of hierarchical models to investigate the protein functional network of cellular markers. We consider a novel data set from single-cell proteomics. The data are generated from single-cell mass cytometry experiments, in which protein expression is measured within an individual cell for multiple markers.

Characterizing Cancer-Specific Networks by Integrating TCGA Data.

The Cancer Genome Atlas (TCGA) generates comprehensive genomic data for thousands of patients over more than 20 cancer types. TCGA data are typically whole-genome measurements of multiple genomic features, such as DNA copy numbers, DNA methylation, and gene expression, providing unique opportunities for investigating cancer mechanism from multiple molecular and regulatory layers. We propose a Bayesian graphical model to systemically integrate multi-platform TCGA data for inference of the interactions between different genomic features either within a gene or between multiple genes.

A Bayesian hierarchical model for inference across related reverse phase protein arrays experiments.

We consider inference for functional proteomics experiments that record protein activation over time following perturbation under different dose levels of several drugs. The main inference goal is the dependence structure of the selected proteins. A critical challenge is the lack of sufficient data under any one drug and dose level to allow meaningful inference on dependence structure.

Bayesian Nonparametric Inference - Why and How.

We review inference under models with nonparametric Bayesian (BNP) priors. The discussion follows a set of examples for some common inference problems. The examples are chosen to highlight problems that are challenging for standard parametric inference.

Nonparametric Bayesian Bi-Clustering for Next Generation Sequencing Count Data.

Histone modifications (HMs) play important roles in transcription through post-translational modifications. Combinations of HMs, known as chromatin signatures, encode specific messages for gene regulation. We therefore expect that inference on possible clustering of HMs and an annotation of genomic locations on the basis of such clustering can contribute new insights about the functions of regulatory elements and their relationships to combinations of HMs.

Toward breaking the histone code: bayesian graphical models for histone modifications.

Background: Histones are proteins that wrap DNA around in small spherical structures called nucleosomes. Histone modifications (HMs) refer to the post-translational modifications to the histone tails. At a particular genomic locus, each of these HMs can either be present or absent, and the combinatory patterns of the presence or absence of multiple HMs, or the histone codes, are believed to coregulate important biological processes.

BM-BC: a Bayesian method of base calling for Solexa sequence data.

Base calling is a critical step in the Solexa next-generation sequencing procedure. It compares the position-specific intensity measurements that reflect the signal strength of four possible bases (A, C, G, T) at each genomic position, and outputs estimates of the true sequences for short reads of DNA or RNA. We present a Bayesian method of base calling, BM-BC, for Solexa-GA sequencing data.

Propriety conditions for the Bayesian autologistic model - Inference for histone modifications.

Motivated by inference for a set of histone modifications we consider an improper prior for an autologistic model. We state sufficient conditions for posterior propriety under a constant prior on the coefficients of an autologistic model. We use known results for a multinomial logistic regression to prove posterior propriety under the autologistic model.

A Bayesian Graphical Model for Integrative Analysis of TCGA Data.

We integrate three TCGA data sets including measurements on matched DNA copy numbers (C), DNA methylation (M), and mRNA expression (E) over 500+ ovarian cancer samples. The integrative analysis is based on a Bayesian graphical model treating the three types of measurements as three vertices in a network. The graph is used as a convenient way to parameterize and display the dependence structure.

Sparse Bayesian Graphical Models for RPPA Time Course Data.

Advances in functional proteomic technologies have significantly enriched our knowledge of protein functions and their interactions in bio-molecular pathways. We discuss inference for RPPA (reverse phase protein array) data that measure the expression of the protein markers over time. We exploit the dynamical nature of the experiment to build a directed network of protein interactions.