CPA4 overexpression correlates with poor prognosis and tumor progression in endometrial cancer.

Background: The rise in endometrial cancer rates globally calls for advanced diagnostic methods and new biomarkers. CPA4, known for its role in cancer development, has not yet been studied in relation to endometrial cancer, making it a promising research avenue.

Methods: We analyzed CPA4's mRNA expression using data from TCGA and GEO databases and validated these findings with 116 clinical samples through immunohistochemical analysis.

Antioxidant and anticancer properties of fucoidan isolated from Saccharina Japonica brown algae.

Fucoidan is a fucose-rich sulfated polysaccharide that has gained attention owing to its various biological activities. In this study, fucoidan was isolated from Saccharina japonica using an enzyme-assisted method, and its antioxidant and anti-hepatocarcinoma effects were evaluated. The fucoidan was a 112.

Biosilicification-mimicking chiral nanostructures for targeted treatment of inflammatory bowel disease.

The cascade reaction of lipopolysaccharides (LPS), cell-free DNA (cfDNA), and reactive oxygen species (ROS), drives the development of inflammatory bowel disease (IBD). Herein, we construct polyethylenimide (PEI)-L/D-tartaric acid (L/D-TA) complexes templated mesoporous organosilica nanoparticles (MON) (PEI-L/D-TA@MON) by mimicking biosilicification under ambient conditions within seconds. The chiral nanomedicines include four functional moieties, wherein PEI electrostatically attracts cfDNA, tetrathulfide bonds reductively react with ROS, silanol groups adsorb LPS, and L/D-TA enables chiral recognition and inflammatory localization.

Task-augmented cross-view imputation network for partial multi-view incomplete multi-label classification.

In real-world scenarios, multi-view multi-label learning often encounters the challenge of incomplete training data due to limitations in data collection and unreliable annotation processes. The absence of multi-view features impairs the comprehensive understanding of samples, omitting crucial details essential for classification. To address this issue, we present a task-augmented cross-view imputation network (TACVI-Net) for the purpose of handling partial multi-view incomplete multi-label classification.

Scale-Adaptive viable tumor burden estimation via histopathological microscopy image segmentation.

Cancer segmentation in whole-slide images is a fundamental step for estimating tumor burden, which is crucial for cancer assessment. However, challenges such as vague boundaries and small regions dissociated from viable tumor areas make it a complex task. Considering the usefulness of multi-scale features in various vision-related tasks, we present a structure-aware, scale-adaptive feature selection method for efficient and accurate cancer segmentation.