Publications by authors named "S Z Shan"

Background And Purpose: This study aims to assess the disease burden and care quality along with cross-country inequalities for stroke at global, regional, and national levels from 1990 to 2021.

Methods: Data on stroke were extracted from the Global Burden of Disease (GBD) study 2021 for the globe, five sociodemographic index (SDI) regions, 21 GBD regions, and 204 countries/territories. The disease burden was quantified using the age-standardized disability-adjusted life years rate (ASDR).

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Background: With an increasing prevalence of frailty among older adults, effective classification and management strategies for frailty have become imperative. Voice biomarkers, offering insights into the overall health status of older adults, hold promise for enhancing the management of this multifaceted geriatric syndrome.

Objectives: This scoping review aims to consolidate existing knowledge regarding the relationship between frailty and voice biomarkers.

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Background: Nurses demonstrate a greater vulnerability to developing depressive and anxiety symptoms compared to the general population. Adverse Childhood Experiences (ACEs) are known risk factors for mental health issues, but impact of timing of these experiences remains unclear.

Objective: To investigate associations between timing of ACEs and depressive, anxiety, comorbid symptoms.

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Previous research has consistently shown that high-fat diet (HFD) consumption can lead to the development of colonic inflammation. Neohesperidin (NHP), a naturally occurring flavanone glycoside in citrus fruits, has anti-inflammatory properties. However, the efficacy and mechanism of NHP in countering prolonged HFD-induced inflammation remains unclear.

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Objective: To develop an original-mirror alignment associated deep learning algorithm for intelligent registration of three-dimensional maxillofacial point cloud data, by utilizing a dynamic graph-based registration network model (maxillofacial dynamic graph registration network, MDGR-Net), and to provide a valuable reference for digital design and analysis in clinical dental applications.

Methods: Four hundred clinical patients without significant deformities were recruited from Peking University School of Stomatology from October 2018 to October 2022. Through data augmentation, a total of 2 000 three-dimensional maxillofacial datasets were generated for training and testing the MDGR-Net algorithm.

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