OBJECTIVE To construct a prediction model for tacrolimus trough concentration based on population pharmacokinetics （PPK） information and machine learning， evaluate its predictive performance in clinical therapeutic drug monitoring （TDM） data， and provide evidence-based support for individualized dose adjustment of tacrolimus after renal transplantation. METHODS A retrospective cohort study design was adopted， and consecutive TDM records of kidney transplant recipients treated in the First Affiliated Hospital of University of Science and Technology of China （Anhui Provincial Hospital） from January to June 2025 were included to construct the final analysis set. Based on 29 input variables including previous TDM concentration， previous concentration/dose （ C / D ） ratio， and individual predicted exposure （IPRE）， 13 commonly used regression algorithm models and 1 weighted ensemble model were systematically compared. The dataset was split into training set and test set in an 80∶20 ratio by patient. Five-fold cross-validation based on patient grouping was performed on the training set， and the model performance was evaluated on the obtained evaluation points using the coefficient of determination （ R 2 ）， mean absolute error （MAE）， root mean square error （RMSE）， Pearson correlation coefficient （Corr） and ±20%/±30% accuracy. The SHapley Additive exPlanations （SHAP） method was used for model interpretability analysis to identify core predictive features. RESULTS A total of 809 consecutive TDM records from 83 kidney transplant recipients were included， with 66 cases in the training set and 17 cases in the test set. The SVR model showed the best performance， with R 2 of 0.649 8， MAE of 1.126 0 ng/mL， RMSE of 1.478 6 ng/mL， Corr of 0.819 1， ±20% accuracy of 59.68%， and ±30% accuracy of 79.03%. The Weighted Ensemble model and Extra Trees model ranked second and third， respectively. SHAP interpretability analysis showed that the top 6 features contributing to the prediction were IPRE， previous TDM concentration， previous C/D ratio， dose-normalized concentration， hematocrit （HCT）， and concentration-dose coefficient （ θ cd ）， among which IPRE was the core predictive feature （SHAP mean value 0.915 4）. CONCLUSIONS The machine learning model incorporating PPK information demonstrates good predictive ability for tacrolimus concentration in the current TDM cohort. SVR is the optimal model， IPRE is the core predictive feature of the model， and a few core PK-derived features （IPRE， dose-normalized concentration， θ cd ） together with clinical features （previous TDM concentration， HCT， etc.） constitute the main information sources for model prediction.

Objective: To investigate the trends in incidence of viral hepatitis in Yuxi City, Yunnan Province from 2004 to 2022, so as to provide insights into the management of viral hepatitis. Methods: Data of viral hepatitis cases in Yuxi City from 2004 to 2022 were retrieved from the China Disease Prevention and Control Information System. The temporal, human and spatial distribution of viral hepatitis cases were analyzed using a descriptive epidemiological method, and the trends in incidence of viral hepatitis were evaluated in Yuxi City from 2004 to 2022 using annual percent change (APC) and average APC (AAPC). Results: A total of 37 430 viral hepatitis cases were reported in Yuxi City from 2004 to 2022, with an average annual incidence rate of 91.27/105, and the incidence showed a tendency towards a rise (AAPC=12.686, P<0.05). There were 29 229 cases with hepatitis B, 5 796 cases with hepatitis C, 1 451 cases with hepatitis A, 520 cases with hepatitis E and 434 cases with unclassified hepatitis reported in Yuxi City, with average annual incidence rates of 71.27/105, 14.13/105, 3.54/105, 1.27/105 and 1.06/105, respectively. The average annual incidence rates of viral hepatitis were 98.24/105 and 78.94/105 among men and women, which both appeared a tendency towards a rise (AAPC=12.415% and 12.581%, both P<0.05). The highest average annual incidence of viral hepatitis was seen among residents at age of 25 to 29 years (139.89/105), with the lowest seen among residents at ages of 0 to 4 years (11.38/105), and the average annual incidence of viral hepatitis appeared a tendency towards a rise with age (P<0.05). The highest average annual incidence of viral hepatitis was seen in Huaning County (234.24/105), with the lowest seen in Chengjiang City (26.11/105), and the average annual incidence appeared a tendency towards a rise in Xinping County from 2004 to 2022 (AAPC=21.451%, P<0.05). Conclusions The reported incidence of viral hepatitis appeared a tendency towards a rise in Yuxi City from 2004 to 2022, with hepatitis B as the main type. High incidence was seen among men and adolescents. The viral hepatitis control strategy requires to be adjusted with adaptations to high-incidence hepatitis subgroups, residents and regions.

Objective: To investigate the antimicrobial susceptibility of Brucella and to provide a scientific basis for rational drug use and effective treatment of patients with brucellosis. Methods: A total of 41 Brucella strains were isolated from the blood of patients with brucellosis in 5 counties and 2 districts in Yuxi City, China from 2014 to 2016. The susceptibility to 23 antimicrobial drugs was tested using Kirby-Bauer (K-B) disk diffusion method and the sizes of antimicrobial rings were recorded. The susceptibility testing results were interpreted according to the Drug Susceptibility Testing Guideline (2009 version) . Results: The susceptibility rate of Brucella was 100.00% to ofloxacin, ciprofloxacin, levofloxacin, and amikacin and >90% to cefotaxime, cefepime, imipenem, doxycycline, cefoperazone, minocycline, tobramycin, rifampicin, cefoperazone/sulbactam, and chloramphenicol. The high resistance to aztreonam and ampicillin was observed (87.80% and 41.46%). Doxycycline-intermediate strains, rifampicin-intermediate strains, and rifampicin-resistant strains were identified. Conclusion Doxycycline and rifampicin are commonly used in the treatment of brucellosis, but doxycycline/rifampicin-intermediate and-resistant strains have been identified. The susceptibility of Brucella to fluoroquinolones and cephalosporins was high, so the two drugs can be considered in the treatment of brucellosis.