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%.

SINERGIA laparoscopic virtual reality simulator: didactic design and technical development.

VR laparoscopic simulators have demonstrated its validity in recent studies, and research should be directed towards a high training effectiveness and efficacy. In this direction, an insight into simulators' didactic design and technical development is provided, by describing the methodology followed in the building of the SINERGIA simulator. It departs from a clear analysis of training needs driven by a surgical training curriculum.