Digitally transforming community mental healthcare: Real-world lessons from algorithmic workforce integration.

Community-based high intensity services for people living with severe and enduring mental illnesses face critical workforce shortages and workflow efficiency challenges. The expectation to monitor complex, dynamic patient data from ever-expanding electronic health records leads to information overload, a significant factor contributing to worker burnout and attrition. An algorithmic workforce, defined as a suite of algorithm-driven processes, can work alongside health professionals assisting with oversight tasks and augmenting human expertise.

A stratified treatment algorithm in psychiatry: a program on stratified pharmacogenomics in severe mental illness (Psych-STRATA): concept, objectives and methodologies of a multidisciplinary project funded by Horizon Europe.

Schizophrenia (SCZ), bipolar (BD) and major depression disorder (MDD) are severe psychiatric disorders that are challenging to treat, often leading to treatment resistance (TR). It is crucial to develop effective methods to identify and treat patients at risk of TR at an early stage in a personalized manner, considering their biological basis, their clinical and psychosocial characteristics. Effective translation of theoretical knowledge into clinical practice is essential for achieving this goal.

FGF21 and its underlying adipose tissue-liver axis inform cardiometabolic burden and improvement in obesity after metabolic surgery.

Background: This research investigates the determinants of circulating FGF21 levels in a cohort reflecting metabolic disease progression, examining the associations of circulating FGF21 with morphology and function of adipose tissue (AT), and with metabolic adjustments following metabolic surgery.

Methods: We measured serum FGF21 in 678 individuals cross-sectionally and in 189 undergoing metabolic surgery longitudinally. Relationships between FGF21 levels, AT histology, transcriptomes and proteomes, cardiometabolic risk factors, and post-surgery metabolic adjustments were assessed using univariate and multivariate analyses, causal mediation analysis, and network integration of AT transcriptomes and proteomes.

Proteomic analysis of extracellular vesicles derived from canine mammary tumour cell lines identifies protein signatures specific for disease state.

Background: Canine mammary tumours (CMT) are among the most common types of tumours in female dogs. Diagnosis currently requires invasive tissue biopsies and histological analysis. Tumour cells shed extracellular vesicles (EVs) containing RNAs and proteins with potential for liquid biopsy diagnostics.