Feature-interactive Siamese graph encoder-based image analysis to predict STAS from histopathology images in lung cancer.

Spread through air spaces (STAS) is a distinct invasion pattern in lung cancer, crucial for prognosis assessment and guiding surgical decisions. Histopathology is the gold standard for STAS detection, yet traditional methods are subjective, time-consuming, and prone to misdiagnosis, limiting large-scale applications. We present VERN, an image analysis model utilizing a feature-interactive Siamese graph encoder to predict STAS from lung cancer histopathological images.

Generational Specific Recruitment of Arsenic- and Antimony-Reducing Microorganisms in Plant Root-Associated Niches for Adapting to Metalloid-Metal Pollution.

The recruitment of microorganisms by plants can enhance their adaptability to environmental stressors, but how root-associated niches recruit specific microorganisms for adapting to metalloid-metal contamination is not well-understood. This study investigated the generational recruitment of microorganisms in different root niches of () under arsenic (As) and antimony (Sb) stress. The was cultivated in As- and Sb-cocontaminated mine soils (MS) and artificial pollution soils (PS) over two generations in controlled conditions.

SELECTOR: Heterogeneous graph network with convolutional masked autoencoder for multimodal robust prediction of cancer survival.

Accurately predicting the survival rate of cancer patients is crucial for aiding clinicians in planning appropriate treatment, reducing cancer-related medical expenses, and significantly enhancing patients' quality of life. Multimodal prediction of cancer patient survival offers a more comprehensive and precise approach. However, existing methods still grapple with challenges related to missing multimodal data and information interaction within modalities.