Prognostic Models Using Machine Learning Algorithms and Treatment Outcomes of Papillary Thyroid Carcinoma Variants.

Background: Hürthle cell (HCC) and columnar cell variants (CCV) are rare subtypes of thyroid cancer.

Aims: This study used machine learning (ML) to evaluate treatment effectiveness and develop prognostic models.

Methods: Chi-square tests, Kaplan-Meier curves, log-rank tests, and Cox regression were used.

Identification of Hypoxia Prognostic Signature in Glioblastoma Multiforme Based on Bulk and Single-Cell RNA-Seq.

Glioblastoma (GBM) represents a profoundly aggressive and heterogeneous brain neoplasm linked to a bleak prognosis. Hypoxia, a common feature in GBM, has been linked to tumor progression and therapy resistance. In this study, we aimed to identify hypoxia-related differentially expressed genes (DEGs) and construct a prognostic signature for GBM patients using multi-omics analysis.

Predicting the Stone-Free Status of Percutaneous Nephrolithotomy with the Machine Learning System.

Purpose: The study aimed to create a machine learning model (MLM) to predict the stone-free status (SFS) of patients undergoing percutaneous nephrolithotomy (PCNL) and compare its performance to the S.T.O.