Effect of fenofibrate on residual beta cell function in adults and adolescents with newly diagnosed type 1 diabetes: a randomised clinical trial.

Aims/hypothesis: Fenofibrate, a peroxisome proliferator-activated receptor alpha agonist, shows some promise in alleviating beta cell stress and preserving beta cell function in preclinical studies of type 1 diabetes. The aim of this phase 2, placebo-controlled, double-blinded, randomised clinical trial was to investigate the efficacy and safety of fenofibrate in adults and adolescents with newly diagnosed type 1 diabetes.

Methods: We enrolled 58 individuals (aged 16 to 40 years old) with newly diagnosed type 1 diabetes and randomised them to daily oral treatment with fenofibrate 160 mg or placebo for 52 weeks (in a block design with a block size of 4, assigned in a 1:1 ratio).

Evaluating the Response of the Soil Bacterial Community and Lettuce Growth in a Fluorine and Cadmium Co-Contaminated Yellow Soil.

The impact of cadmium (Cd) and fluorine (F) on plant and human health has provoked significant public concern; however, their combined effects on plant and soil bacterial communities have yet to be determined. Here, a pot experiment was conducted to evaluate the effects of exogenous F, Cd, and their combination (FCd) on lettuce growth and soil bacterial communities. The results revealed that F and Cd concentrations in lettuce ranged from 63.

Functionality of wheat straw-derived biochar enhanced its efficiency for actively capping Cd and Pb in contaminated water and soil matrices: Insights through batch adsorption and flow-through experiments.

The impact of functionality of biochar on pressing environmental issue of cadmium (Cd) and lead (Pb) co-contamination in simultaneous soil and water systems has not sufficiently reported. This study investigated the impact of Fe- and Mg-functionalized wheat straw biochar (Fe-WSBC and Mg-WSBC) on Cd and Pb adsorption/immobilization through batch sorption and column leaching trials. Importantly, Fe-WSBC was more effective in adsorbing Cd and Pb (82.

Predicting the governing factors for the release of colloidal phosphorus using machine learning.

Predicting the parameters that influence colloidal phosphorus (CP) release from soils under different land uses is critical for managing the impact on water quality. Traditional modeling approaches, such as linear regression, may fail to represent the intricate relationships that exist between soil qualities and environmental influences. Therefore, in this study, we investigated the major determinants of CP release from different land use/types such as farmland, desert, forest soils, and rivers.

A Modified Model for Quantitative Heavy Metal Source Apportionment and Pollution Pathway Identification.

Current source apportionment models have successfully identified emission sources and quantified their contributions. However, when being utilized for heavy metal source apportion in soil, their accuracy needs to be improved, regarding migration patterns. Therefore, this work intended to improve the pre-existing principal component analysis and multiple linear regression with distance (PCA-MLRD) model to effectively locate pollution pathways (traffic emissions, irrigation water, atmospheric depositions, etc.