Wannan Medical College

Understanding the genetic evolutionary history of the Daur people is academically valuable for determining how the core lineages of Mongolic-speaking and East Asian populations were formed, as the Daur represent an ancient branch of the Mongolic-speaking population. Academic research and discussion in this field focused on the genetic relationships between the Daur and ancient Khitan populations. The resulting large amounts of deep sequencing data obtained have been used to deepen our understanding of the complexity of the genetic evolution and migration-fusion history of human populations. In this review, we systematically summarize the latest progress in research on the Y chromosome and mitochondrial DNA lineages of the Daur population from 2019 to 2025. We integrated the developmental history of the Daur ethnic group with the genealogical data of key families and determined the evolutionary trajectory of the paternal and maternal genetic histories of the Daur population. This review provides a reference for subsequent research on the genetic histories of related populations.

Severe Fever with Thrombocytopenia Syndrome (SFTS) is a severe tick-borne viral infection with high mortality, making the timely prediction of clinical deterioration critical. Current predictive models lack timeliness and generalizability. Therefore, this study aimed to develop and validate an interpretable machine learning model for the early prediction of deterioration in SFTS patients. We retrospectively analyzed 560 SFTS patients from two hospitals. Using clinical and laboratory data from a training set (n = 407), we developed eight ML models. An independent test set (n = 153) was used for external validation. Model performance was assessed via area under the ROC curve (AUC), calibration, decision curve analysis (DCA), and SHapley Additive exPlanations (SHAP). The RF model outperformed others with an external validation AUC of 0.825. Key predictors included viral load, serum creatinine, D-dimer, procalcitonin, platelets, blood urea nitrogen, age, and lymphocytes. SHAP analysis revealed significant interactions, especially between blood urea nitrogen and viral load. The RF model provided reliable risk stratification, with net benefits surpassing no-treatment or all-treatment strategies when the threshold probability was between 0.09 and 0.76. An interactive web-based application was developed for real-time individualized risk prediction. We successfully developed and validated a robust ML model using RF, integrating eight readily measurable clinical variables to predict early deterioration in SFTS patients. This model offers improved timeliness and interpretability, facilitating early clinical interventions. Future multi-center studies will further validate its robustness and reliability, enhancing its clinical utility.