Decoding the glycoproteome: a new frontier for biomarker discovery in cancer.

Cancer early detection and treatment response prediction continue to pose significant challenges. Cancer liquid biopsies focusing on detecting circulating tumor cells (CTCs) and DNA (ctDNA) have shown enormous potential due to their non-invasive nature and the implications in precision cancer management. Recently, liquid biopsy has been further expanded to profile glycoproteins, which are the products of post-translational modifications of proteins and play key roles in both normal and pathological processes, including cancers.

Warning Signs From the Crypt: Aberrant Protein Glycosylation Marks Opportunities for Early Colorectal Cancer Detection.

Colorectal cancer (CRC) remains a leading cause of cancer-related deaths despite being the most preventable and treatable forms of cancer when caught early through screening. There is an unmet need for novel screening approaches with improved accuracy, less invasiveness, and reduced costs. In recent years, evidence has accumulated around particular biological events that happen during the adenoma-to-carcinoma transition, especially focusing on precancerous immune responses in the colonic crypt.

The Drivers of Acceptance of Artificial Intelligence-Powered Care Pathways Among Medical Professionals: Web-Based Survey Study.

Background: The emergence of Artificial Intelligence (AI) has been proven beneficial in several health care areas. Nevertheless, the uptake of AI in health care delivery remains poor. Despite the fact that the acceptance of AI-based technologies among medical professionals is a key barrier to their implementation, knowledge about what informs such attitudes is scarce.

Artificial Intelligence for Inflammatory Bowel Diseases (IBD); Accurately Predicting Adverse Outcomes Using Machine Learning.

Background: Inflammatory Bowel Diseases with its complexity and heterogeneity could benefit from the increased application of Artificial Intelligence in clinical management.

Aim: To accurately predict adverse outcomes in patients with IBD using advanced computational models in a nationally representative dataset for potential use in clinical practice.

Methods: We built a training model cohort and validated our result in a separate cohort.