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.

Proposing a novel molecular subtyping scheme for predicting distant recurrence-free survival in breast cancer post-neoadjuvant chemotherapy with close correlation to metabolism and senescence.

Background: High relapse rates remain a clinical challenge in the management of breast cancer (BC), with distant recurrence being a major driver of patient deterioration. To optimize the surveillance regimen for distant recurrence after neoadjuvant chemotherapy (NAC), we conducted a comprehensive analysis using bioinformatics and machine learning approaches.

Materials And Methods: Microarray data were retrieved from the GEO database, and differential expression analysis was performed with the R package 'Limma'.

Notoginsenoside R1 relieves the myocardial infarction via activating the JAK2/STAT3 signaling pathway in vivo and in vitro.

Myocardial infarction (MI), caused by continuous ischemia and hypoxia of the coronary artery, is one of the major causes of human mortality. This study aimed to investigate the role of notoginsenoside R1 (NGR1) in MI therapy. and models of MI were established by hypoxia/reoxygenation (H/R)-treatment of H9C2 cells and through the ligation of the left anterior descending coronary artery of rats, respectively.