Transcriptomic profiles in major depressive disorder: the role of immunometabolic and cell-cycle-related pathways in depression with different levels of inflammation.

Transcriptomic profiles are important indicators for molecular mechanisms and pathways involved in major depressive disorder (MDD) and its different phenotypes, such as immunometabolic depression. We performed whole-transcriptome and pathway analyses on 139 individuals from the observational, case-control, BIOmarkers in DEPression (BIODEP) study, 105 with MDD and 34 controls. We divided MDD participants based on levels of inflammation, as measured by serum high-sensitivity C-reactive protein (CRP), in n = 39 'not inflamed' (CRP < 1 mg/L), n = 31 with 'elevated CRP' (1-3 mg/L), and n = 35 with 'low-grade inflammation' (>3 mg/L).

Physically informed deep neural networks for metabolite-corrected plasma input function estimation in dynamic PET imaging.

Introduction: We propose a novel approach for the non-invasive quantification of dynamic PET imaging data, focusing on the arterial input function (AIF) without the need for invasive arterial cannulation.

Methods: Our method utilizes a combination of three-dimensional depth-wise separable convolutional layers and a physically informed deep neural network to incorporatea priori knowledge about the AIF's functional form and shape, enabling precise predictions of the concentrations of [C]PBR28 in whole blood and the free tracer in metabolite-corrected plasma.

Results: We found a robust linear correlation between our model's predicted AIF curves and those obtained through traditional, invasive measurements.

A blood-free modeling approach for the quantification of the blood-to-brain tracer exchange in TSPO PET imaging.

Introduction: Recent evidence suggests the blood-to-brain influx rate ( ) in imaging as a promising biomarker of blood-brain barrier () permeability alterations commonly associated with peripheral inflammation and heightened immune activity in the brain. However, standard compartmental modeling quantification is limited by the requirement of invasive and laborious procedures for extracting an arterial blood input function. In this study, we validate a simplified blood-free methodologic framework for estimation by fitting the early phase tracer dynamics using a single irreversible compartment model and an image-derived input function ().

Development and initial evaluation of a clinical prediction model for risk of treatment resistance in first-episode psychosis: Schizophrenia Prediction of Resistance to Treatment (SPIRIT).

Background: A clinical tool to estimate the risk of treatment-resistant schizophrenia (TRS) in people with first-episode psychosis (FEP) would inform early detection of TRS and overcome the delay of up to 5 years in starting TRS medication.

Aims: To develop and evaluate a model that could predict the risk of TRS in routine clinical practice.

Method: We used data from two UK-based FEP cohorts (GAP and AESOP-10) to develop and internally validate a prognostic model that supports identification of patients at high-risk of TRS soon after FEP diagnosis.

Effects of diazepam on hippocampal blood flow in people at clinical high risk for psychosis.

Elevated hippocampal perfusion has been observed in people at clinical high risk for psychosis (CHR-P). Preclinical evidence suggests that hippocampal hyperactivity is central to the pathophysiology of psychosis, and that peripubertal treatment with diazepam can prevent the development of psychosis-relevant phenotypes. The present experimental medicine study examined whether diazepam can normalize hippocampal perfusion in CHR-P individuals.