Using an Interpretable Amino Acid-Based Machine Learning Method to Enhance the Diagnosis of Major Depressive Disorder.

Major depressive disorder (MDD) is a leading cause of disability worldwide. At present, however, there are no established biomarkers that have been validated for diagnosing and treating MDD. This study sought to assess the diagnostic and predictive potential of the differences in serum amino acid concentration levels between MDD patients and healthy controls (HCs), integrating them into interpretable machine learning models.

Interpretable deep learning model for major depressive disorder assessment based on functional near-infrared spectroscopy.

Background: Major depressive disorder (MDD) affects a substantial number of individuals worldwide. New approaches are required to improve the diagnosis of MDD, which relies heavily on subjective reports of depression-related symptoms.

Aim: Establish an objective measurement and evaluation of MDD.

The Utility of Amino Acid Metabolites in the Diagnosis of Major Depressive Disorder and Correlations with Depression Severity.

Major depressive disorder (MDD) is a highly prevalent and disabling condition with a high disease burden. There are currently no validated biomarkers for the diagnosis and treatment of MDD. This study assessed serum amino acid metabolite changes between MDD patients and healthy controls (HCs) and their association with disease severity and diagnostic utility.

The diagnostic utility of miRNA and elucidation of pathological mechanisms in major depressive disorder.

Aims: Our study aims to explore how miRNAs can elucidate the molecular mechanisms of major depressive disorder (MDD) by comparing the miRNA levels in the blood serum of patients with depression and healthy individuals. It also explores the potential of miRNAs to differentiate between depressed patients and healthy controls.

Methods: 60 healthy controls (n = 45 females) were matched to 60 depressed patients (n = 10 unmedicated) for age (±7), sex, ethnicity, and years of education.

Understanding children's perspectives of the influences on their dietary behaviours.

Objective: This study aimed to examine the intrapersonal, interpersonal, environmental and macrosystem influences on dietary behaviours among primary school children in Singapore.

Design: A qualitative interpretive approach was used in this study. Focus group discussions guided by the socio-ecological model (sem), of which transcripts were analysed deductively using the sem and inductively using thematic analysis to identify themes at each sem level.

Children's perceptions of factors influencing their physical activity: a focus group study on primary school children.

Purpose: Establishing healthy lifestyle behaviours in primary school children is important, as these behaviours are likely to track into adulthood. This study aimed to explore the factors influencing physical activity (PA) in primary school children through their perspectives.

Approach: Eleven focus group discussions and one interview were conducted with 52 children (n = 29 girls) aged 9-12 years from two primary schools in Singapore.