Liang Tong

Department of Psychiatry, Yunnan Provincial Mental Hospital, Affiliated Mental Health Center of Kunming Medical University, Kunming 650224, China.

2 papers in the library · 2 citations · publishing 2024-2026

Papers

Online mindfulness-based stress reduction improves anxiety and depression status and quality of life in caregivers of patients with severe mental disorders.

Zhejiang da xue xue bao. Yi xue ban = Journal of Zhejiang University. Medical sciences January 31, 2024 Deng'Ai Duan, Haiming Wang, Liang Tong et al. 2 citations

An eight-week online mindfulness-based stress reduction (MBSR) program lowered anxiety and depression and improved quality of life for caregivers of patients with severe mental disorders. In a randomized trial, 80 caregivers of patients with schizophrenia or bipolar disorder were assigned to either basic health education and rehabilitation training or the same plus online MBSR. After eight weeks, the MBSR group showed significant decreases in anxiety and depression scores and increases in overall quality-of-life scores, except for the physiological function dimension. The control group showed no significant changes. Online MBSR appears to reduce anxiety and depression and enhance quality of life in these caregivers.

DOI: 10.3724/zdxbyxb-2023-0555 Full text Anxiety Depression Mindfulness-based stress reduction Quality of life +1

Raman Activity Investigation and Utilization of Psilocybin

Journal of Raman Spectroscopy March 19, 2026 Mingyu Sun, Xiaoyu Zhao, Fandi Kong et al.

A Raman spectroscopy method combined with machine learning can rapidly and non-destructively identify psilocybin, the psychoactive compound in magic mushrooms. Theoretical Raman spectra predicted by density functional theory matched experimental spectra from fresh and heat-treated Psilocybe cubensis samples, establishing characteristic fingerprint features. Among feature extraction methods, competitive adaptive reweighted sampling (CARS) selected the most discriminative variables. An XGBoost model, optimized with Bayesian tuning and balanced via SMOTE, was integrated into a Bagging framework with KNN, SVM, and Decision Tree. The final model achieved 0.984 accuracy, 0.984 F1-score, and 0.976 ROC AUC, showing strong stability under storage conditions. This approach enables rapid, accurate, cost-effective, and contamination-free psilocybin detection for food safety and toxic mushroom screening.

DOI: 10.1002/jrs.70133 Psilocybin Raman spectroscopy Pattern recognition psychology Interpretability +14