Secondhand Smoke Exposure and Smoking Attitudes and Behaviors Among Adolescents in the Gaza Strip: Findings From the 2019 Global Youth Tobacco Survey.

Background: Secondhand smoke (SHS) exposure remains a critical public health concern, especially for adolescents, who are more susceptible to its harmful effects and may initiate smoking as a result. SHS, comprising both mainstream and sidestream smoke, contains over 7000 chemicals, many of which are carcinogenic. Adolescents exposed to SHS are more likely to experiment with smoking due to environmental influences, peer pressure, and familial smoking behavior.

Grouping of chemicals for safety assessment: the importance of toxicokinetic properties of salicylate esters.

Grouping of chemicals has been proposed as a strategy to speed up the screening and identification of potential substances of concern among the broad chemical universe under REACH. Such grouping is usually based on shared structural features and should only be used for the prioritization objectives. However, additional considerations (as well as structural similarity) are needed, e.

Exploring Differentially Methylated Genes amongst Preterm birth and Full-term birth.

Introduction: Preterm birth (PTB) is associated with newborn morbidity and mortality. DNA methylation plays an important role in the development of fetus, thus can also serve as an epigenetic biomarker. Limited epigenetic studies were conducted in regard to PTB.

Effect of Digoxin vs Beta-Blockers on Left Atrial Strain for Heart Rate-Controlled Atrial Fibrillation: The DIGOBET-AF Randomized Clinical Trial.

Unlabelled: BACKGROUND AND OBJECTIVE: Left atrial strain (LAS) has prognostic value in patients with atrial fibrillation (AF). Consequently, therapies that improve LAS may help reduce AF-related adverse cardiac events. We aimed to compare how digoxin and bisoprolol modulate LAS in patients with AF being treated with rate control.

Artificial intelligence in surgical pathology - Where do we stand, where do we go?

Surgical and neuropathologists continuously search for new and disease-specific features, such as independent predictors of tumor prognosis or determinants of tumor entities and sub-entities. This is a task where artificial intelligence (AI)/machine learning (ML) systems could significantly contribute to help with tumor outcome prediction and the search for new diagnostic or treatment stratification biomarkers. AI systems are increasingly integrated into routine pathology workflows to improve accuracy, reproducibility, productivity and to reveal difficult-to-see features in complicated histological slides, including the quantification of important markers for tumor grading and staging.