Elimination of nickel ions in a packed column using clamshell waste as an adsorbent.

The present investigation assessed the viability of utilizing a powdered clam shell in continuous adsorption to eliminate nickel ions from simulated wastewater. The breakthrough curves (BTC) were analyzed by altering the Q (inlet flow rate) in a glass column (ID 5 cm, H 35 cm) with a multi-port and filled with the powdered clamshell adsorbent (PCSA). The PCSA's nickel adsorption efficiency was maximum (87.

Social Contact Patterns in Rural and Urban Settings, Mozambique, 2021-2022.

Few sources have reported empirical social contact data from resource-poor settings. To address this shortfall, we recruited 1,363 participants from rural and urban areas of Mozambique during the COVID-19 pandemic, determining age, sex, and relation to the contact for each person. Participants reported a mean of 8.

Inhibiting de novo lipogenesis identifies a therapeutic vulnerability in therapy-resistant colorectal cancer.

A significant clinical challenge in patients with colorectal cancer (CRC), which adversely impacts patient survival, is the development of therapy resistance leading to a relapse. Therapy resistance and relapse in CRC is associated with the formation of lipid droplets (LD) by stimulating de novo lipogenesis (DNL). However, the molecular mechanisms underlying the increase in DNL and the susceptibility to DNL-targeted therapies remain unclear.

Barley a nutritional powerhouse for gut health and chronic disease defense.

Background: Digestive issues are recognized as significant contributors to various chronic diseases, including obesity, diabetes, and cardiovascular disease. Barley, a traditional grain, offers considerable promise in addressing these health challenges due to unique nutritional and bioactive compounds.

Objective: This review examines the therapeutic potential of various parts of barley, underutilized resource, for chronic disease prevention and management.

A hybrid cardiovascular arrhythmia disease detection using ConvNeXt-X models on electrocardiogram signals.

Cardiovascular arrhythmia, characterized by irregular heart rhythms, poses significant health risks, including stroke and heart failure, making accurate and early detection critical for effective treatment. Traditional detection methods often struggle with challenges such as imbalanced datasets, limiting their ability to identify rare arrhythmia types. This study proposes a novel hybrid approach that integrates ConvNeXt-X deep learning models with advanced data balancing techniques to improve arrhythmia classification accuracy.