Efficiency and safety of automated label cleaning on multimodal retinal images.

Label noise is a common and important issue that would affect the model's performance in artificial intelligence. This study assessed the effectiveness and potential risks of automated label cleaning using an open-source framework, Cleanlab, in multi-category datasets of fundus photography and optical coherence tomography, with intentionally introduced label noise ranging from 0 to 70%. After six cycles of automatic cleaning, significant improvements are achieved in label accuracies (3.

Weight cycling exacerbates glucose intolerance and hepatic triglyceride storage in mice with a history of chronic high fat diet exposure.

Background: Obese subjects undergoing weight loss often fear the Yoyo dieting effect, which involves regaining or even surpassing their initial weight. To date, our understanding of such long-term obesity and weight cycling effects is still limited and often based on only short-term murine weight gain and loss studies. This study aimed to investigate the long-term impacts of weight cycling on glycemic control and metabolic health, focusing on adipose tissue, liver, and hypothalamus.

Anti-herpetic tau preserves neurons via the cGAS-STING-TBK1 pathway in Alzheimer's disease.

Alzheimer's disease (AD) diagnosis relies on the presence of extracellular β-amyloid (Aβ) and intracellular hyperphosphorylated tau (p-tau). Emerging evidence suggests a potential link between AD pathologies and infectious agents, with herpes simplex virus 1 (HSV-1) being a leading candidate. Our investigation, using metagenomics, mass spectrometry, western blotting, and decrowding expansion pathology, detects HSV-1-associated proteins in human brain samples.

Exploring machine learning tools in a retrospective case-study of patients with metastatic non-small cell lung cancer treated with first-line immunotherapy: A feasibility single-centre experience.

Background: Artificial intelligence (AI) models are emerging as promising tools to identify predictive features among data coming from health records. Their application in clinical routine is still challenging, due to technical limits and to explainability issues in this specific setting. Response to standard first-line immunotherapy (ICI) in metastatic Non-Small-Cell Lung Cancer (NSCLC) is an interesting population for machine learning (ML), since up to 30% of patients do not benefit.