Flaxseed powder supplementation in non-alcoholic fatty liver disease: a randomized controlled clinical trial.

: Non-alcoholic fatty liver disease (NAFLD) has become a growing public health problem worldwide, and dietary interventions have important potential in the prevention and treatment of NAFLD. Moreover, previous animal studies have shown that flaxseed has a good improvement effect in animal NAFLD models. : Assess whether flaxseed powder could improve the liver lipid content in patients with NAFLD.

Surface immobilization of single atoms on heteroatom-doped carbon nanospheres through phenolic-mediated interfacial anchoring for highly efficient biocatalysis.

Single-atom catalysts (SACs) dispersed on support materials exhibit exceptional catalytic properties that can be fine-tuned through interactions between the single atoms and the support. However, selectively controlling the spatial location of single metal atoms while simultaneously harmonizing their coordination environment remains a significant challenge. Here, we present a phenolic-mediated interfacial anchoring (PIA) strategy to prepare SACs with Fe single atoms anchored on the surface of heteroatom-doped carbon nanospheres.

Increased sodium consumption is associated with psoriasis: A population-based cohort study.

Background: Sodium is stored in skin and may trigger or perpetuate autoimmune diseases including psoriasis. One previous study found skin sodium was elevated in a small group of patients with severe psoriasis compared to healthy controls, but the relationship between sodium intake and psoriasis within a population has not been investigated.

Objectives: To identify whether dietary sodium intake is associated with psoriasis and whether there are subgroups of individuals more likely to have salt-sensitive psoriasis.

DO-GMA: An End-to-End Drug-Target Interaction Identification Framework with a Depthwise Overparameterized Convolutional Network and the Gated Multihead Attention Mechanism.

Identification of potential drug-target interactions (DTIs) is a crucial step in drug discovery and repurposing. Although deep learning effectively deciphers DTIs, most deep learning-based methods represent drug features from only a single perspective. Moreover, the fusion method of drug and protein features needs further refinement.