Vericiguat and Cardiovascular Outcomes in Heart Failure by Baseline Diabetes Status: Insights From the VICTORIA Trial.

Background: Type 2 diabetes mellitus (T2DM) significantly worsens heart failure (HF) prognosis.

Objectives: This study sought to investigate the impact of T2DM on outcomes in patients enrolled in VICTORIA and assess the efficacy of vericiguat in patients with and without T2DM.

Methods: Patients with HF with reduced ejection fraction were randomized to receive vericiguat or placebo in addition to standard therapy.

"Future patient II" telerehabilitation for patients with heart failure: Protocol for a randomized controlled trial.

Background: Heart failure is a global problem affecting millions of people worldwide. Current care of heart failure patients follows standard protocols and often overlooks the patients' specific needs, which leads to low compliance in the rehabilitation phase. Telerehabilitation, where the patients communicate with health care professionals about their rehabilitation program and monitor their vital signs, aims to increase the degree of compliance as well as enhancing their quality of life.

DeepPeptide predicts cleaved peptides in proteins using conditional random fields.

Motivation: Peptides are ubiquitous throughout life and involved in a wide range of biological processes, ranging from neural signaling in higher organisms to antimicrobial peptides in bacteria. Many peptides are generated post-translationally by cleavage of precursor proteins and can thus not be detected directly from genomics data, as the specificities of the responsible proteases are often not completely understood.

Results: We present DeepPeptide, a deep learning model that predicts cleaved peptides directly from the amino acid sequence.

Deorphanizing Peptides Using Structure Prediction.

Many endogenous peptides rely on signaling pathways to exert their function, but identifying their cognate receptors remains a challenging problem. We investigate the use of AlphaFold-Multimer complex structure prediction together with transmembrane topology prediction for peptide deorphanization. We find that AlphaFold's confidence metrics have strong performance for prioritizing true peptide-receptor interactions.