Using ecological momentary assessment and machine learning techniques to predict depressive symptoms in emerging adults.

The objective of this study was to predict the level of depressive symptoms in emerging adults by analyzing sociodemographic variables, affect, and emotion regulation strategies. Participants were 33 emerging adults (M = 24.43; SD = 2.

Wastewater analysis of Mpox virus in a city with low prevalence of Mpox disease: an environmental surveillance study.

Background: Tracking infectious diseases at the community level is challenging due to asymptomatic infections and the logistical complexities of mass surveillance. Wastewater surveillance has emerged as a valuable tool for monitoring infectious disease agents including SARS-CoV-2 and Mpox virus. However, detecting the Mpox virus in wastewater is particularly challenging due to its relatively low prevalence in the community.

Risk Assessment of Hip Fracture Based on Machine Learning.

Identifying patients with high risk of hip fracture is a great challenge in osteoporosis clinical assessment. Bone Mineral Density (BMD) measured by Dual-Energy X-Ray Absorptiometry (DXA) is the current gold standard in osteoporosis clinical assessment. However, its classification accuracy is only around 65%.

The impact of sex and cannabis on clinical features in first-admitted patients with psychosis.

There is a scarcity of studies investigating the effect of sex on the clinical and treatment characteristics of first-admitted patients with first-episode psychosis (FEP). The reasons for using cannabis and the effect of cannabis on clinical features have not received enough attention either. We aim therefore, to investigate sex differences in the reasons for cannabis use and to determine the effects of sex, cannabis use and their interaction on clinical variables at admission and at discharge from the inpatient unit.

Prediction of osteoporotic hip fracture in postmenopausal women through patient-specific FE analyses and machine learning.

A great challenge in osteoporosis clinical assessment is identifying patients at higher risk of hip fracture. Bone Mineral Density (BMD) measured by Dual-Energy X-Ray Absorptiometry (DXA) is the current gold-standard, but its classification accuracy is limited to 65%. DXA-based Finite Element (FE) models have been developed to predict the mechanical failure of the bone.