Clinical and Imaging-Based Prognostic Models for Recurrence and Local Tumor Progression Following Thermal Ablation of Hepatocellular Carcinoma: A Systematic Review.

Early detection of patients at high risk for recurrence or local tumor progression (LTP) following thermal ablation of hepatocellular carcinoma (HCC) is essential for treatment selection and individualized follow-up. This systematic review aims to assess and compare the performance of prognostic models predicting recurrence or LTP in patients with HCC treated with thermal ablation. PubMed, Web of Science, Cochrane, and Embase were searched for studies developing models to predict recurrence after thermal ablation in treatment-naïve HCC patients, using imaging and clinical data with reported test set performance.

A chaperone-proteasome-based fragmentation machinery is essential for aggrephagy.

Perturbations in protein quality control lead to the accumulation of misfolded proteins and protein aggregates, which can compromise health and lifespan. One key mechanism eliminating protein aggregates is aggrephagy, a selective type of autophagy. Here we reveal that fragmentation is required before autophagic clearance of various types of amorphous aggregates.

Estimation of dietary metabolizable energy content in dairy cattle using the measured dietary content of digestible nutrients: A meta-analysis.

An accurate estimation of metabolizable energy (ME) content is critical for optimizing dairy cattle nutrition and improving feed efficiency. This meta-analysis used data from studies conducted in Wageningen (the Netherlands) using Holstein cows and in Nebraska using Jersey cows to refine predictive models of dietary ME content (kJ/kg DM) based on total-tract digestible nutrients. In the Wageningen studies (except for one study), apparent total-tract digestibility of nutrients was estimated using chromium oxide as an external marker included in the concentrate.

A prompt-aware knowledge-tuning framework for histopathology subtype classification with scarce annotation.

Artificial intelligence can assist pathologists in diagnosing histopathology subtypes, enabling precision medicine and improving survival rates. Many approaches employ multi-scale models or combine knowledge to implement subtype diagnosis. However, they fail to identify explicit features most relevant to subtypes adaptively, resulting model relying heavily on extensive annotation.