Xu J
Predicting recurrence in osteosarcoma via a quantitative histological image classifier derived from tumour nuclear morphological features
Recurrence is the key factor affecting the prognosis of osteosarcoma. Currently, there is a lack of clinically useful tools to predict osteosarcoma recurrence. The application of pathological images for artificial intelligence-assisted accurate prediction of tumour outcomes is increasing.
ReadSafety, Tolerability, and Pharmacokinetics of β-Cryptoxanthin Supplementation in Healthy Women: A Double-Blind, Randomized, Placebo-Controlled Clinical Trial
Keywords: carotenoids; Asian women; gene expression; mood; physical activity; fecal microbiome
ReadProfiling Microbial Communities in Idiopathic Granulomatous Mastitis
Keywords: idiopathic granulomatous mastitis; metagenomic sequencing; microbiota; 16S rRNA; Corynebacterium; MaAsLin 2
ReadHybrid AI assistive diagnostic model permits rapid TBS classification of cervical liquid-based thin-layer cell smears
Technical advancements significantly improve earlier diagnosis of cervical cancer, but accurate diagnosis is still difficult due to various factors. We develop an artificial intelligence assistive diagnostic solution, AIATBS, to improve cervical liquid-based thin-layer cell smear diagnosis according to clinical TBS criteria.
Read