Deep Learning Model for Predicting Immunotherapy Response in Advanced Non-Small Cell Lung Cancer.

Importance: Only a small fraction of patients with advanced non-small cell lung cancer (NSCLC) respond to immune checkpoint inhibitor (ICI) treatment. For optimal personalized NSCLC care, it is imperative to identify patients who are most likely to benefit from immunotherapy.

Objective: To develop a supervised deep learning-based ICI response prediction method; evaluate its performance alongside other known predictive biomarkers; and assess its association with clinical outcomes in patients with advanced NSCLC.

Drug detection in oral fluid and urine after single therapeutic doses of dexamphetamine, lisdexamphetamine, and methylphenidate in healthy volunteers.

Dexamphetamine, lisdexamphetamine, and methylphenidate are central stimulant drugs widely used to treat Attention-deficit/hyperactivity disorder (ADHD), but poor adherence may lead to treatment failure and the drugs are also subject to misuse and diversion. Drug analysis in oral fluid may thus be useful for monitoring adherence and misuse. We measured drug concentrations in oral fluid and urine after controlled dosing to investigate detection windows and evaluate the chosen cut-offs.

Serum cytokines as a biomarker for immune checkpoint inhibitor toxicity in patients with pleural mesothelioma.

Background: Pleural mesothelioma (PM) is a rare cancer with a dismal prognosis. Dual immune checkpoint inhibitors have improved overall survival, but the rate of immune-related adverse events (irAEs) is high. Serum cytokines reflect systemic immune reactions and may serve as biomarkers for irAEs.