Trust in government moderates the association between fear of COVID-19 as well as empathic concern and preventive behaviour.

With the COVID-19 pandemic, behavioural scientists aimed to illuminate reasons why people comply with (or not) large-scale cooperative activities. Here we investigated the motives that underlie support for COVID-19 preventive behaviours in a sample of 12,758 individuals from 34 countries. We hypothesized that the associations of empathic prosocial concern and fear of disease with support towards preventive COVID-19 behaviours would be moderated by trust in the government.

p53 Genetics and Biology in Lung Carcinomas: Insights, Implications and Clinical Applications.

The gene is renowned as a tumor suppressor, playing a pivotal role in overseeing the cell cycle, apoptosis, and maintaining genomic stability. Dysregulation of p53 often contributes to the initiation and progression of various cancers, including lung cancer (LC) subtypes. The review explores the intricate relationship between p53 and its role in the development and progression of LC.

Multifunctional Organometallic Compounds Active against Infective Trypanosomes: Ru(II) Ferrocenyl Derivatives with Two Different Bioactive Ligands.

Human African trypanosomiasis (HAT, sleeping sickness) and American trypanosomiasis (Chagas disease) are endemic zoonotic diseases caused by genomically related trypanosomatid protozoan parasites ( and , respectively). Just a few old drugs are available for their treatment, with most of them sharing poor safety, efficacy, and pharmacokinetic profiles. Only fexinidazole has been recently incorporated into the arsenal for the treatment of HAT.

DistilIQA: Distilling Vision Transformers for no-reference perceptual CT image quality assessment.

No-reference image quality assessment (IQA) is a critical step in medical image analysis, with the objective of predicting perceptual image quality without the need for a pristine reference image. The application of no-reference IQA to CT scans is valuable in providing an automated and objective approach to assessing scan quality, optimizing radiation dose, and improving overall healthcare efficiency. In this paper, we introduce DistilIQA, a novel distilled Vision Transformer network designed for no-reference CT image quality assessment.