ALBI Grade Analyses of TACE Combined with Anti-Angiogenesis Therapies Plus PD-1 Inhibitors versus Anti-Angiogenesis Therapies Plus PD-1 Inhibitors in Advanced HCC.

Objective: To evaluate the baseline albumin-bilirubin (ALBI) grade's role in advanced hepatocellular carcinoma (HCC) receiving transarterial chemoembolization (TACE) plus anti-angiogenesis therapies and PD-1 inhibitors (TACE+TP) versus anti-angiogenesis therapies and PD-1 inhibitors (TP).

Methods: This multicenter retrospective study enrolled advanced HCC undergoing TACE+TP or TP from January 2019 to June 2023 at three hospitals in China. The primary outcomes were time to progression of the ALBI grade and change in ALBI score between the initial baseline and the final assessment point available, the secondary outcomes consisted of overall survival (OS) as well as progression-free survival (PFS).

Recent development and applications of differential electrochemical mass spectrometry in emerging energy conversion and storage solutions.

Electrochemical energy conversion and storage are playing an increasingly important role in shaping the sustainable future. Differential electrochemical mass spectrometry (DEMS) offers an and cost-effective tool to monitor the evolution of gaseous/volatile intermediates and products during these processes. It can deliver potential-, time-, mass- and space-resolved signals which facilitate the understanding of reaction kinetics.

Evaluation of Feature Selection for Alzheimer's Disease Diagnosis.

Accurate recognition of patients with Alzheimer's disease (AD) or mild cognitive impairment (MCI) is important for the subsequent treatment and rehabilitation. Recently, with the fast development of artificial intelligence (AI), AI-assisted diagnosis has been widely used. Feature selection as a key component is very important in AI-assisted diagnosis.

Interpretable Recognition for Dementia Using Brain Images.

Machine learning-based models are widely used for neuroimage-based dementia recognition and achieve great success. However, most models omit the interpretability that is a very important factor regarding the confidence of a model. Takagi-Sugeno-Kang (TSK) fuzzy classifiers as the high interpretability and promising classification performance have widely used in many scenarios.