Background: We conducted a systematic review and meta-analysis to compare effectiveness and safety of 9 months of isoniazid (9H) versus shorter rifamycin-containing regimens for treating latent tuberculosis infection (TBI) in children. Methods: We systematically searched MEDLINE, Embase, and Cochrane Central Register of Controlled Trials to June 2025 for randomized, controlled trials (RCTs) and cohort studies that compared regimens that were shorter than 9 months of isoniazid in children aged 1–18 years. Outcomes were development of TB disease, treatment completion, and adverse events. Risk of bias was assessed using RoB 2.0 and the Risk Of Bias In Non-Randomized Studies - of Interventions (ROBINS-I) tool; certainty of evidence was graded using Grading of Recommendations Assessment, Development, and Evaluation (GRADE). Results: Five RCTs and 7 nonrandomized studies that enrolled approximately 2950 children in trials and >25 000 in observational cohorts were included. In pooled analysis of 3 RCTs, shorter rifamycin-containing regimens resulted in little to no difference in development of TB disease compared with 9H (odds ratio [OR], 0.19; 95% confidence interval [CI], .03–1.12; moderate-certainty evidence). Treatment completion was probably higher with shorter regimens (OR, 0.51; 95% CI, .42–0.62; moderate-certainty evidence). Adverse events were similar between groups, but evidence is uncertain (low-certainty evidence). Observational data were consistent with these findings, showing higher completion rates and lower hepatotoxicity with shorter treatments. Conclusions: Shorter rifamycin-containing regimens for pediatric TBI probably increase treatment completion and have similar safety outcomes, with no important difference in development of TB disease compared with the standard regimen. These findings support current guideline recommendations that favor shorter regimens in children.

Background and Objectives: Given the non-specificity of symptoms and complex methods for diagnosing heart failure, which are not applicable in screening, it is of great importance to develop a simple screening method for identifying systolic dysfunction of the heart based on available biosignals, one of which is a single-channel electrocardiogram (ECG). The method does not require the participation of medical staff. Aim: To create a screening model for detecting left ventricular systolic dysfunction in a complex analysis of single-channel ECG parameters using machine learning algorithms Methods: We included 624 patients aged 18 to 90 years. All patients underwent echocardiography and single-channel I-lead ECG recording using a portable electrocardiograph. The left ventricle ejection fraction (LV EF) was determined in the apical 2-chamber and 4-chamber view using the BIPLANE Simpson method and confirmed by two independent experts. Single-channel ECG analysis was performed using advanced signal processing and machine learning techniques. Results: For identifying LV EF below 52% in men and below 54% in women, the best result was demonstrated by “Lasso regression”: sensitivity 79.2%, specificity 81.7%, AUC = 0.849. For detection of LVEF below 40%, the “Extra Trees” model was the best, with a sensitivity of 83.1% and a specificity of 82.7%, AUC = 0.972. External testing of the algorithm was conducted on a sample of 600 patients. The accuracy was 98%, specificity 98.4%, and sensitivity 93.5%. Conclusions: The results indicate quite high diagnostic accuracy of screening for left ventricular systolic dysfunction when analyzing single-channel ECG parameters using modern signal processing and machine learning technologies.