Effect of Direct-acting Antivirals Treatment on Renal Function Among HCV-infected Patients / 中山大学学报(医学科学版)

； ObjectiveTo explore the effect of direct-acting antiviral treatment on renal function in patients with chronic hepatitis C. MethodsA total of 123 HCV-infected patients receiving DAAs treatment at the Third Affiliated Hospital of Sun Yat-sen University from January 2017 to December 2021 were included in this study. To explore the renal function in patients with chronic hepatitis C treated with direct-acting antivirals， serum creatinine values were collected before， during and after the treatment， which were used to estimate the eGFR by the MDRD equation to assess the changes in renal function. ResultsOf the 123 patients enrolled， 67.5%（n=83）were male， and the mean age of participants was （50±11） years old. The mean follow-up period was 24 weeks . Comorbidities included cirrhosis in 26.8%， and diabetes in 10.6%. Meanwhile， 11.4% of the cohort had eGFR < 60 mL/（min ·1.73 m2）， 33.3% of the cohort had eGFR 60 to 90 mL/（min ·1.73 m2）， and 55.3% had eGFR≥90 mL/（min ·1.73 m2）. No decrease in renal function was seen among all the HCV-infected patients at the end of treatment or the follow-up period after treatment. However， compared with the eGFR at the baseline， eGFR in CKD2 patients in the follow-up period was improved 【（88.65±15.52） mL/（min ·1.73 m2）vs （78.12 ±7.60） mL/（min ·1.73 m2）， P< 0.001】. And 14.6% （n=18） of patients experienced progressive deterioration of renal function. Logistic regression analysis showed that diabetes could predict the deterioration of renal function （OR=4.663， P=0.016）. ConclusionsOur study shows renal function is not impair among HCV-infected patients following DAAs treatment， and renal function in CKD2 patients have improvements. However， HCV-infected patients with diabetes mellitus are at a high risk of renal impairment and closely monitoring of renal function is still needed.

Analysis of Ancient Literature and Modern Clinical Application of Tuoli Xiaodusan / 中国实验方剂学杂志

Tuoli Xiaodusan is the 65th formula in the Catalogue of Ancient Famous Classical Formulas（First Batch）. In this study， the bibliometric method was used to summarize and verify the ancient books about Tuoli Xiaodusan in terms of its historical origin， composition and dosage of the formula， indications， decoction and administration method， and processing， etc. According to the research， there is no definite date of the formation of Tuoli Xiaodusan， the earliest can be traced back to Lizhai Waike Fahui in Ming dynasty， which has been widely circulated， with many versions of prescription composition， and the modern influential version is from Waike Zhengzong in Ming dynasty， which is made up of 12 Chinese herbs including Ginseng Radix et Rhizoma（3.73 g）， Chuanxiong Rhizoma（3.73 g）， Paeoniae Radix Alba（3.73 g）， Astragali Radix（3.73 g）， Angelicae Sinensis Radix（3.73 g）， Atractylodis Macrocephalae Rhizoma（3.73 g）， Poria（3.73 g）， Lonicerae Japonicae Flos（3.73 g）， Angelicae Dahuricae Radix（1.87 g）， Glycyrrhizae Radix et Rhizoma（1.87 g）， Gleditsiae Spina（1.87 g）， Platycodonis Radix（1.87 g）. The herb origins almost follow the 2020 edition of Chinese Pharmacopoeia， except that Angelica dahurica var. formosana is only recommended as the origin of Angelicae Dahuricae Radix， and Glycryyhiza uralensis is only recommended as the origin of Glycyrrhizae Radix et Rhizoma. All the herbs are recommended to be used in the raw products. As for the preparation method， it is recommended to decoct with water， add 400 mL of water， boil until 160 mL， and take 2-3 times a day. The formula has the functions of nourishing Qi and nourishing blood， detoxifying and draining pus， and was mainly used to treat ulcerative diseases with the syndrome of syndrome of healthy Qi deficiency and pathogenic factors excess in ancient times， and in modern times， it is used for a wide range of treatment， involving the skin and soft tissues， bones， digestion and many other systemic diseases， and is also mainly used for syndrome of healthy Qi deficiency and pathogenic factors excess. In this study， the ancient and modern applications of Tuoli Xiaodusan were summarized， and its key information was identified， providing a basis for its wider clinical application， in-depth research and formulation development.

Risk predictive models of healthcare-seeking delay among imported malaria patients in Jiangsu Province based on the machine learning / 中国血吸虫病防治杂志

Objective To create risk predictive models of healthcare-seeking delay among imported malaria patients in Jiangsu Province based on machine learning algorithms, so as to provide insights into early identification of imported malaria cases in Jiangsu Province. Methods Case investigation, first symptoms and time of initial diagnosis of imported malaria patients in Jiangsu Province in 2019 were captured from Infectious Disease Report Information Management System and Parasitic Disease Prevention and Control Information Management System of Chinese Center for Disease Control and Prevention. The risk predictive models of healthcare-seeking delay among imported malaria patients were created with the back propagation (BP) neural network model, logistic regression model, random forest model and Bayesian model using thirteen factors as independent variables, including occupation, species of malaria parasite, main clinical manifestations, presence of complications, severity of disease, age, duration of residing abroad, frequency of malaria parasite infections abroad, incubation period, level of institution at initial diagnosis, country of origin, number of individuals travelling with patients and way to go abroad, and time of healthcare-seeking delay as a dependent variable. Logistic regression model was visualized using a nomogram, and the nomogram was evaluated using calibration curves. In addition, the efficiency of the four models for prediction of risk of healthcare-seeking delay among imported malaria patients was evaluated using the area under curve (AUC) of receiver operating characteristic curve (ROC). The importance of each characteristic was quantified and attributed by using SHAP to examine the positive and negative effects of the value of each characteristic on the predictive efficiency. Results A total of 244 imported malaria patients were enrolled, including 100 cases (40.98%) with the duration from onset of first symptoms to time of initial diagnosis that exceeded 24 hours. Logistic regression analysis identified a history of malaria parasite infection [odds ratio (OR) = 3.075, 95% confidential interval (CI): (1.597, 5.923)], long incubation period [OR = 1.010, 95% CI: (1.001, 1.018)] and seeking healthcare in provincial or municipal medical facilities [OR = 12.550, 95% CI: (1.158, 135.963)] as risk factors for delay in seeking healthcare among imported malaria cases. BP neural network modeling showed that duration of residing abroad, incubation period and age posed great impacts on delay in healthcare-seek among imported malaria patients. Random forest modeling showed that the top five factors with the greatest impact on healthcare-seeking delay included main clinical manifestations, the way to go abroad, incubation period, duration of residing abroad and age among imported malaria patients, and Bayesian modeling revealed that the top five factors affecting healthcare-seeking delay among imported malaria patients included level of institutions at initial diagnosis, age, country of origin, history of malaria parasite infection and individuals travelling with imported malaria patients. ROC curve analysis showed higher overall performance of the BP neural network model and the logistic regression model for prediction of the risk of healthcare-seeking delay among imported malaria patients (Z = 2.700 to 4.641, all P values < 0.01), with no statistically significant difference in the AUC among four models (Z = 1.209, P > 0.05). The sensitivity (71.00%) and Youden index (43.92%) of the logistic regression model was higher than those of the BP neural network (63.00% and 36.61%, respectively), and the specificity of the BP neural network model (73.61%) was higher than that of the logistic regression model (72.92%). Conclusions Imported malaria cases with long duration of residing abroad, a history of malaria parasite infection, long incubation period, advanced age and seeking healthcare in provincial or municipal medical institutions have a high likelihood of delay in healthcare-seeking in Jiangsu Province. The models created based on the logistic regression and BP neural network show a high efficiency for prediction of the risk of healthcare-seeking among imported malaria patients in Jiangsu Province, which may provide insights into health management of imported malaria patients.