Publications by authors named "Jun Xiong Yin"
Article Synopsis
- The study focused on using a machine-learning algorithm to detect asymptomatic carotid stenosis (ACS) in individuals at high risk for stroke, with the goal of improving early prevention strategies.
- Researchers analyzed data from 2,841 participants and identified the five key risk factors for ACS: family history of dyslipidemia, high LDL cholesterol, low HDL cholesterol, aging, and low BMI, which together accounted for a significant portion of the risk.
- The algorithm showed strong performance in identifying ACS, with area under the curve (AUC) values of 0.927 for the training dataset and 0.888 for the testing dataset, indicating its effectiveness.
Background: Anti-N-methyl-D-aspartate receptor (anti-NMDAR) encephalitis, which is the most common type of autoimmune encephalitis, is caused by the production of autoantibodies against NMDA receptor. Anti-NMDAR encephalitis patients present with various non-specific symptoms, such as abnormal psychiatric or behaviour, speech dysfunction, cognitive dysfunction, seizures, movement disorders, decreased level of consciousness, and central hypoventilation or autonomic dysfunction.
Case Presentation: A 67-year-old man presented with new-onset focal seizures.