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Tuberculosis is the second most common infectious cause of death globally. Low TB case detection remains a major challenge to achieve the global End TB targets. This systematic review and meta-analysis aimed to determine whether training of health professionals and volunteers increase TB case detection.
This study addresses a knowledge gap in the literature about early adolescent cyberbullying victimization and the related positive and negative emotional wellbeing and academic achievement outcomes experienced over time.
Nirsevimab is an extended half-life monoclonal antibody to the respiratory syncytial virus (RSV) fusion protein that has been developed to protect infants for an entire RSV season. Previous studies have shown that the nirsevimab binding site is highly conserved. However, investigations of the geotemporal evolution of potential escape variants in recent (ie, 2015–2021) RSV seasons have been minimal.
Preterm birth (PTB) and stillbirth remain two of the most important causes of death, morbidity, and disability in childhood. Despite efforts to reduce PTB and stillbirth worldwide, rates of these adverse outcomes remain persistently elevated, independent of income setting. There is an urgent need for more effective interventions to reduce associated neonatal and early childhood morbidity and mortality.
We aimed to evaluate the effect of undernutrition on sputum culture conversion and treatment outcomes among people with multidrug-resistant tuberculosis.
Tick-borne diseases are a growing global health concern. Despite extensive studies, ill-defined tick-associated pathologies remain with unknown aetiologies. Human immunological responses after tick bite, and inter-individual variations of immune-response phenotypes, are not well characterised.
Flow cytometry (FCM) and transcription profiling are the two widely used assays in translational immunology research. However, there is no data integration pipeline for analyzing these two types of assays together with experiment variables for biomarker inference.
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Here we present an approach for the DNA methylation-based classification of central nervous system tumors across all groups and demonstrate its application.