Machine learning-based prediction of gastroparesis risk following complete mesocolic excision.

Background: Gastroparesis is a major complication following complete mesocolic excision (CME) and significantly impacts patient outcomes. This study aimed to create a machine learning model to pinpoint key risk factors before, during, and after surgery, effectively predicting the risk of gastroparesis after CME.

Methods: The study involved 1146 patients with colon cancer, out of which 95 developed gastroparesis.

Multicenter cohort analysis of anoikis and EMT: implications for prognosis and therapy in lung adenocarcinoma.

Background: Anoikis and epithelial-mesenchymal transition (EMT) are pivotal in the distant metastasis of lung adenocarcinoma (LUAD). A detailed understanding of their interplay and the identification of key genes is vital for effective therapeutic strategies against LUAD metastasis.

Methods: Key prognostic genes related to anoikis and EMT were identified through univariate Cox regression analysis.

Assessment of new-onset heart failure prediction in a diabetic population using left ventricular global strain: a prospective cohort study based on UK Biobank.

Background: Impaired glucose utilization influences myocardial contractile function. However, the prognostic importance of left ventricular global radial strain (LV-GRS), left ventricular global circumferential strain (LV-GCS), and left ventricular global longitudinal strain (LV-GLS) in predicting new-onset heart failure (HF) in a population with diabetes is unclear.

Methods: The study design is prospective cohort from the UK Biobank.

Advancing predictive markers in lung adenocarcinoma: A machine learning-based immunotherapy prognostic prediction signature.

The prognosis of lung adenocarcinoma (LUAD) is generally poor. Immunotherapy has emerged as a promising therapeutic modality, demonstrating remarkable potential for substantially prolonging the overall survival of individuals afflicted with LUAD. However, there is currently a lack of reliable signatures for identifying patients who would benefit from immunotherapy.

Differentiation of closely-related species within Acinetobacter baumannii-calcoaceticus complex via Raman spectroscopy: a comparative machine learning analysis.

Bacterial species within the Acinetobacter baumannii-calcoaceticus (Acb) complex are very similar and are difficult to discriminate. Misidentification of these species in human infection may lead to severe consequences in clinical settings. Therefore, it is important to accurately discriminate these pathogens within the Acb complex.

Increased susceptibility to new-onset atrial fibrillation in diabetic women with poor sleep behaviour traits: findings from the prospective cohort study in the UK Biobank.

Background: Diabetic individuals often encounter various sleep-related challenges. Although the association between sleep duration and atrial fibrillation (AF) have been explored, the association of other sleep traits with the incidence of AF remains unclear. A comprehensive understanding of these traits is essential for a more accurate assessment of sleep conditions in patients with diabetes and the development of novel AF prevention strategies.