Artificial intelligence in prostate cancer.

Prostate cancer (PCa) ranks as the second most prevalent malignancy among men worldwide. Early diagnosis, personalized treatment, and prognosis prediction of PCa play a crucial role in improving patients' survival rates. The advancement of artificial intelligence (AI), particularly the utilization of deep learning (DL) algorithms, has brought about substantial progress in assisting the diagnosis, treatment, and prognosis prediction of PCa.

Function and mechanism of lactylation in health and diseases: a comprehensive review.

Lactylation, the post-translational modification of proteins by lactate, has emerged as a novel and dynamic regulatory mechanism in cellular metabolism and signaling. The lactylation proteome represents a frontier in the study of cellular regulation, offering new insights into the complex interplay between metabolism and signaling pathways. It not only plays a pivotal role in regulating cell fate, cell signaling, gene expression, and cellular metabolism, but also exhibits unique and profound biological implications in inflammation, immune regulation, construction of the tumor microenvironment, and maintenance of cardiovascular and cerebrovascular functions.

Arsenic exposure provoked prostatic PANoptosis by inducing mitochondrial dysfunction in mice and WPMY-1 cells.

Inorganic arsenic, a widespread environmental toxicant, significantly contributes to prostate injury. However, the exact cellular mechanisms remain unclear. This study explored the involvement of pyroptosis, apoptosis, and necroptosis (PANoptosis), and their interconnections in arsenic-induced prostate injury.

Harnessing artificial intelligence for prostate cancer management.

Prostate cancer (PCa) is a common malignancy in males. The pathology review of PCa is crucial for clinical decision-making, but traditional pathology review is labor intensive and subjective to some extent. Digital pathology and whole-slide imaging enable the application of artificial intelligence (AI) in pathology.

Integrating single-cell and bulk sequencing data to identify glycosylation-based genes in non-alcoholic fatty liver disease-associated hepatocellular carcinoma.

Background: The incidence of non-alcoholic fatty liver disease (NAFLD) associated hepatocellular carcinoma (HCC) has been increasing. However, the role of glycosylation, an important modification that alters cellular differentiation and immune regulation, in the progression of NAFLD to HCC is rare.

Methods: We used the NAFLD-HCC single-cell dataset to identify variation in the expression of glycosylation patterns between different cells and used the HCC bulk dataset to establish a link between these variations and the prognosis of HCC patients.

Artificial intelligence-aided detection for prostate cancer with multimodal routine health check-up data: an Asian multi-center study.

Background: The early detection of high-grade prostate cancer (HGPCa) is of great importance. However, the current detection strategies result in a high rate of negative biopsies and high medical costs. In this study, the authors aimed to establish an Asian Prostate Cancer Artificial intelligence (APCA) score with no extra cost other than routine health check-ups to predict the risk of HGPCa.