Beta oscillations predict the envelope sharpness in a rhythmic beat sequence.

Periodic sensory inputs entrain oscillatory brain activity, reflecting a neural mechanism that might be fundamental to temporal prediction and perception. Most environmental rhythms and patterns in human behavior, such as walking, dancing, and speech do not, however, display strict isochrony but are instead quasi-periodic. Research has shown that neural tracking of speech is driven by modulations of the amplitude envelope, especially via sharp acoustic edges, which serve as prominent temporal landmarks.

High-dose glucocorticoids in the treatment of postoperative pain after video-assisted thoracoscopic surgery-protocol for systematic review and meta-analysis.

Background: Video-assisted thoracoscopic surgery (VATS) is widely used in lung cancer surgery, as this technique causes less pain and faster recovery than open thoracotomy. However, significant postoperative pain persists in a number of patients, often leading to increased opioid use and opioid-related adverse events in addition to prolonged admission times. Perioperatively administered glucocorticoids have been demonstrated effective in reducing pain after other types of surgeries, but the effect in VATS remains unclear.

Predicting cardiovascular disease in patients with mental illness using machine learning.

Background: Cardiovascular disease (CVD) is twice as prevalent among individuals with mental illness compared to the general population. Prevention strategies exist but require accurate risk prediction. This study aimed to develop and validate a machine learning model for predicting incident CVD among patients with mental illness using routine clinical data from electronic health records.

Predicting involuntary admission following inpatient psychiatric treatment using machine learning trained on electronic health record data.

Background: Involuntary admissions to psychiatric hospitals are on the rise. If patients at elevated risk of involuntary admission could be identified, prevention may be possible. Our aim was to develop and validate a prediction model for involuntary admission of patients receiving care within a psychiatric service system using machine learning trained on routine clinical data from electronic health records (EHRs).