Predicting criminal and violent outcomes in psychiatry: a meta-analysis of diagnostic accuracy.

Although reducing criminal outcomes in individuals with mental illness have long been a priority for governments worldwide, there is still a lack of objective and highly accurate tools that can predict these events at an individual level. Predictive machine learning models may provide a unique opportunity to identify those at the highest risk of criminal activity and facilitate personalized rehabilitation strategies. Therefore, this systematic review and meta-analysis aims to describe the diagnostic accuracy of studies using machine learning techniques to predict criminal and violent outcomes in psychiatry.

Predicting treatment response using EEG in major depressive disorder: A machine-learning meta-analysis.

Selecting a course of treatment in psychiatry remains a trial-and-error process, and this long-standing clinical challenge has prompted an increased focus on predictive models of treatment response using machine learning techniques. Electroencephalography (EEG) represents a cost-effective and scalable potential measure to predict treatment response to major depressive disorder. We performed separate meta-analyses to determine the ability of models to distinguish between responders and non-responders using EEG across treatments, as well as a performed subgroup analysis of response to transcranial magnetic stimulation (rTMS), and antidepressants (Registration Number: CRD42021257477) in Major Depressive Disorder by searching PubMed, Scopus, and Web of Science for articles published between January 1960 and February 2022.

Wavelet-Based Muscle Artefact Noise Reduction for Short Latency rTMS Evoked Potentials.

This paper presents a new method of reducing the noise in the EEG response signal recorded during repetitive transcranial magnetic stimulation (rTMS). This noise is principally composed of the residual stimulus artefact and millivolt amplitude compound muscle action potentials (CMAP) recorded from the scalp muscles and precludes analysis of the cortical evoked potentials, especially during the first 20-ms post stimulus. The proposed method uses the wavelet transform with a fourth-order Daubechies mother wavelet and a novel coefficient reduction algorithm based on cortical amplitude thresholds.

Global health and transfusion medicine: education and training in developing countries.

Education and training in transfusion medicine have improved over the past decade in developing countries but are still generally deficient for the purpose of maintaining the safety of the global blood supply. In 2009, the World Health Organization global database on blood safety indicated that only 72% of countries in the world were able to meet their training needs necessary for maintaining the safety of their local blood supply. Educational approaches in transfusion medicine vary widely between continents and world regions.

Mast cells express IL-17A in rheumatoid arthritis synovium.

The proinflammatory cytokine IL-17A is considered a crucial player in rheumatoid arthritis (RA) pathogenesis. In experimental models of autoimmune arthritis, it has been suggested that the cellular source of IL-17A is CD4(+) T cells (Th17 cells). However, little is known about the source of IL-17 in human inflamed RA tissue.