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Yang Yang

3 papers in the library · 24 citations · publishing 2024-2025

Papers

Psilocybin for the treatment of Alzheimer’s disease

Frontiers in Neuroscience July 10, 2024 Siyi Zheng, Rong Ma, Yang Yang et al. 23 citations

Psilocybin, the psychoactive alkaloid in hallucinogenic mushrooms, may offer a novel treatment for Alzheimer's disease (AD). Its active metabolite, psilocin, modulates the 5-HT2A receptor, leading to heightened neural plasticity, reduced inflammation, and improvements in cognitive functions such as creativity, cognitive flexibility, and emotional facial recognition. Psilocybin also shows promise in alleviating anxiety and depression symptoms in AD patients. The review discusses strategies to mitigate hallucinogenic side effects and addresses ethical and legal considerations, proposing therapeutic potential for psilocybin in managing Alzheimer's disease.

Effectiveness and safety of ofatumumab in treatment-naive and oral DMT-switched multiple sclerosis patients: a multicenter observational study in China.

Multiple sclerosis and related disorders November 1, 2025 Yang Yang, Xinyi Duan, Jing Wang et al. 1 citation

Ofatumumab, a fully humanized anti-CD20 antibody, reduced disease activity and disability progression in Chinese patients with relapsing multiple sclerosis. Among 38 treatment-naïve patients, the annualized relapse rate fell from 0.27 to 0.05, and the median disability score dropped from 2.50 to 2.00 after 12 months. Among 59 patients who switched from oral therapies (teriflunomide, siponimod, fingolimod, dimethyl fumarate) due to disease progression, relapse, or MRI activity, the relapse rate decreased from 0.45 to 0.08 and disability scores improved from 2.00 to 1.00. No new MRI lesions, relapses, or serious adverse events occurred in either group.

DEED: A Multimodel Dataset for Dream Emotion Classification

Lei Zheng, Di Zhou, Meng Zhang et al.

A new open-source dataset, the Dream Emotion Evaluation Dataset (DEED), provides multimodal dream-related information including neural data collected from 38 participants over 82 nights using polysomnography. After each REM awakening, participants reported dream content and affective states. Using DEED, the first dream emotion classification algorithms were implemented, combining feature extraction methods (power spectral density, differential entropy, multi-frequency band common spatial pattern) with support vector machine and convolutional neural networks. The combination of multi-frequency band common spatial pattern and support vector machine achieved the highest accuracy of 83.6%. The dataset aims to accelerate research on decoding neural mechanisms of dream emotions.