Effects of long-term dehydration and quick rehydration on the camel kidney: pathological changes and modulation of the expression of solute carrier proteins and aquaporins.

Background: Recurrent dehydration causes chronic kidney disease in humans and animal models. The dromedary camel kidney has remarkable capacity to preserve water and solute during long-term dehydration. In this study, we investigated the effects of dehydration and subsequent rehydration in the camel's kidney histology/ultrastructure and changes in aquaporin/solute carrier proteins along with gene expression.

Pharmacological Approaches Using Diabetic Drugs Repurposed for Alzheimer's Disease.

Type 2 diabetes mellitus (T2DM) and Alzheimer's disease (AD) are chronic, progressive disorders affecting the elderly, which fosters global healthcare concern with the growing aging population. Both T2DM and AD have been linked with increasing age, advanced glycosylation end products, obesity, and insulin resistance. Insulin resistance in the periphery is significant in the development of T2DM and it has been posited that insulin resistance in the brain plays a key role in AD pathogenesis, earning AD the name "type 3 diabetes".

Effects of long-term dehydration on stress markers, blood parameters, and tissue morphology in the dromedary camel ().

Introduction: Dromedary camels robustly withstand dehydration, and the rough desert environment but the adaptation mechanisms are not well understood. One of these mechanisms is that the dromedary camel increases its body temperature to reduce the process of evaporative cooling during the hot weather. Stress in general, has deleterious effects in the body.

Observing many researchers using the same data and hypothesis reveals a hidden universe of uncertainty.

This study explores how researchers' analytical choices affect the reliability of scientific findings. Most discussions of reliability problems in science focus on systematic biases. We broaden the lens to emphasize the idiosyncrasy of conscious and unconscious decisions that researchers make during data analysis.

Measuring the predictability of life outcomes with a scientific mass collaboration.

How predictable are life trajectories? We investigated this question with a scientific mass collaboration using the common task method; 160 teams built predictive models for six life outcomes using data from the Fragile Families and Child Wellbeing Study, a high-quality birth cohort study. Despite using a rich dataset and applying machine-learning methods optimized for prediction, the best predictions were not very accurate and were only slightly better than those from a simple benchmark model. Within each outcome, prediction error was strongly associated with the family being predicted and weakly associated with the technique used to generate the prediction.