Molecular group and correlation guided structural learning for multi-phenotype prediction.

We propose a supervised learning bioinformatics tool, Biological gRoup guIded muLtivariate muLtiple lIneAr regression with peNalizaTion (Brilliant), designed for feature selection and outcome prediction in genomic data with multi-phenotypic responses. Brilliant specifically incorporates genome and/or phenotype grouping structures, as well as phenotype correlation structures, in feature selection, effect estimation, and outcome prediction under a penalized multi-response linear regression model. Extensive simulations demonstrate its superior performance compared to competing methods.

BLEND: Probabilistic Cellular Deconvolution with Automated Reference Selection.

Cellular deconvolution aims to estimate cell type fractions from bulk transcriptomic and other omics data. Most existing deconvolution methods fail to account for the heterogeneity in cell type-specific (CTS) expression across bulk samples, ignore discrepancies between CTS expression in bulk and cell type reference data, and provide no guidance on cell type reference selection or integration. To address these issues, we introduce BLEND, a hierarchical Bayesian method that leverages multiple reference datasets.

scMD facilitates cell type deconvolution using single-cell DNA methylation references.

The proliferation of single-cell RNA-sequencing data has led to the widespread use of cellular deconvolution, aiding the extraction of cell-type-specific information from extensive bulk data. However, those advances have been mostly limited to transcriptomic data. With recent developments in single-cell DNA methylation (scDNAm), there are emerging opportunities for deconvolving bulk DNAm data, particularly for solid tissues like brain that lack cell-type references.

scMD: cell type deconvolution using single-cell DNA methylation references.

The proliferation of single-cell RNA sequencing data has led to the widespread use of cellular deconvolution, aiding the extraction of cell type-specific information from extensive bulk data. However, those advances have been mostly limited to transcriptomic data. With recent development in single-cell DNA methylation (scDNAm), new avenues have been opened for deconvolving bulk DNAm data, particularly for solid tissues like the brain that lack cell-type references.

Effects of digital psychological interventions on physical symptoms in cancer patients: A systematic review and meta-analysis.

Purpose: This meta-analysis was to assess the efficacy of digital psychological interventions to improve physical symptoms (i.e., fatigue, pain, disturbed sleep, and physical well-being) among cancer patients, as well as to evaluate the variables that possibly moderate intervention effects.

Hydrogel scaffolds in the treatment of spinal cord injury: a review.

Spinal cord injury (SCI) is a disease of the central nervous system often caused by accidents, and its prognosis is unsatisfactory, with long-term adverse effects on patients' lives. The key to its treatment lies in the improvement of the microenvironment at the injury and the reconstruction of axons, and tissue repair is a promising therapeutic strategy. Hydrogel is a three-dimensional mesh structure with high water content, which has the advantages of biocompatibility, degradability, and adjustability, and can be used to fill pathological defects by injectable flowing hydrophilic material to accurately adapt to the size and shape of the injury.

Accurate estimation of rare cell type fractions from tissue omics data via hierarchical deconvolution.

Bulk transcriptomics in tissue samples reflects the average expression levels across different cell types and is highly influenced by cellular fractions. As such, it is critical to estimate cellular fractions to both deconfound differential expression analyses and infer cell type-specific differential expression. Since experimentally counting cells is infeasible in most tissues and studies, cellular deconvolution methods have been developed as an alternative.

A comparison of the role of aggression in the association between hostile interpretation bias and antisocial personality features between young offenders and university students.

Background: Antisocial personality features in adolescents are frequently associated with delinquency and constitute the problem that most concerns the criminal justice system and the public. Hostile interpretation bias has been identified as a candidate for explaining emergent adolescent antisocial personality problems and aggression, but it is unclear whether offenders and non-offenders show differences in the relationships between hostile interpretation bias, aggression and antisocial personality features.

Aims: To compare relationships between hostile interpretation bias and a personality measure between incarcerated teenagers and first year university students and to explore aggression and criminal history as mediating or moderating variables.

Robust and accurate estimation of cellular fraction from tissue omics data via ensemble deconvolution.

Motivation: Tissue-level omics data such as transcriptomics and epigenomics are an average across diverse cell types. To extract cell-type-specific (CTS) signals, dozens of cellular deconvolution methods have been proposed to infer cell-type fractions from tissue-level data. However, these methods produce vastly different results under various real data settings.

Implementation of Surprise Question Assessments using the Electronic Health Record in Older Adults with Advanced CKD.

Background: The Surprise Question (SQ; "Would you be surprised if this patient died in the next 12 months?") is a validated prognostication tool for mortality and hospitalization among patients with advanced CKD. Barriers in clinical workflows have slowed SQ implementation in practice.

Objectives: The aims of this study were: () to evaluate implementation outcomes after the use of electronic health record (EHR) decision support to automate the collection of the SQ; and () to assess the prognostic utility of the SQ for mortality and hospitalization/emergency room (ER) visits.

Association of Self-Rated Health With Functional Limitations in Patients With CKD.

Rationale & Objective: In patients with chronic kidney disease (CKD), self-rated health ("In general, how do you rate your health?") is associated with mortality. The association of self-rated health with functional status is unknown. We evaluated the association of limitations in activities of daily living (ADLs) with self-rated health and clinical correlates in a cohort of patients with CKD stages 1-5.

A practical oxidative C-H functionalization of N-carbamoyl tetrahydro-β-carbolines with diverse potassium trifluoroborates.

A practical and mild metal-free oxidative C-H functionalization of N-carbamoyl tetrahydro-β-carbolines has been reported. This reaction has excellent functional group tolerance, and exhibits a broad range of potassium trifluoroborate components, allowing for the facile C-H functionalization of electronically varied N-carbamoyl THCs in high efficiency with excellent regioselectivity.