Vitamin D Receptor Gene Polymorphisms Differentiated Between Tuberculosis Disease and Infection: Causal Association Study.

Purpose: Latent tuberculosis infection (LTBI) is a critical stage in tuberculosis (TB)control, and few studies have addressed the role of vitamin D receptor(VDR) gene polymorphisms in differentiating between TB and late-onset TB from an immunogenetic perspective.

Patients And Methods: Recruitment of tuberculosis patients and latently infected population in Urumqi, Xinjiang, and use of propensity score matching(PSM) to match the two groups and control confounding to further construct a Bayesian network to analyze causal associations between VDR polymorphisms and tuberculosis disease status.

Results: 137 LTBI and 237 TB were obtained through PSM.

Association Studies of Serum Levels of TNF- α, IL-10, IFN-γ and CXCL 5 with Latent Tuberculosis Infection in Close Contacts.

Purpose: Early recognition and treatment of latent tuberculosis infection(LTBI) is key to tuberculosis(TB) prevention. However, the emergence of LTBI is influenced by a combination of factors, of which the role of individual immune cytokines remains controversial. The aim of this study is to explore the influencing factors of LTBI and their effects with cytokines on LTBI.

Causal association study of the dynamic development of the metabolic syndrome based on longitudinal data.

The dynamic progression of metabolic syndrome (MetS) includes developmental deterioration and reverse recovery; however, the key factors in this bidirectional progression have not been identified. Our study aimed to use the data obtained from the China Health and Retirement Longitudinal Study (CHARLS) and construct a Bayesian network to explore the causal relationship between influential factor and the development and recovery of MetS. Followed up at 4 years, forward progression of MetS occurred in 1543 and reverse recovery of MetS occurred in 1319 of 5581 subjects.

Opening the black box: interpretable machine learning for predictor finding of metabolic syndrome.

Objective: The internal workings ofmachine learning algorithms are complex and considered as low-interpretation "black box" models, making it difficult for domain experts to understand and trust these complex models. The study uses metabolic syndrome (MetS) as the entry point to analyze and evaluate the application value of model interpretability methods in dealing with difficult interpretation of predictive models.

Methods: The study collects data from a chain of health examination institution in Urumqi from 2017 ~ 2019, and performs 39,134 remaining data after preprocessing such as deletion and filling.

Study on Dynamic Progression and Risk Assessment of Metabolic Syndrome Based on Multi-State Markov Model.

Aim: Metabolic syndrome (MetS) coexists with the occurrence and even death of cardiovascular disease and diabetes mellitus. It is essential to study the factors in the dynamic progression of MetS in the interest of prevention and control.

Purpose: The aim of this study was to analyze the dynamic progression of Mets and explore the potential factors influencing the progression or reversal of MetS.

Construction of Xinjiang metabolic syndrome risk prediction model based on interpretable models.

Background: We aimed to construct simple and practical metabolic syndrome (MetS) risk prediction models based on the data of inhabitants of Urumqi and to provide a methodological reference for the prevention and control of MetS.

Methods: This is a cross-sectional study conducted in the Xinjiang Uygur Autonomous Region of China. We collected data from inhabitants of Urumqi from 2018 to 2019, including demographic characteristics, anthropometric indicators, living habits and family history.