Objective:To construct a hypoglycemia random forest prediction model for older adults with type 2 diabetes, and assess the model′s prognostication performance through internal and external verification.Methods:From August 2022 to January 2023, 300 older adults with type 2 diabetes in Beijing Hospital were selected. The demographic characteristics, medical history, laboratory tests, and other data of the patients were collected, and the data set was randomly divided into the training set and verification set in a ratio of 7∶3. The hypoglycemia prediction model for older adults with type 2 diabetes was constructed and optimized based on the random forest algorithm. The calibration curve was used to evaluate the model′s calibration, and the ROC was used to evaluate the model′s discrimination. The clinical applicability of the model was assessed by the decision curve analysis. The risk factors for hypoglycemia in the older adults were explored by prioritizing the contributions of variables in prediction. The Bootstrap method was used for internal validation, and the validation set was used for external validation.Results:Among the 300 older adults with type 2 diabetes, 128 cases (42.67%) experienced hypoglycemia within one week. The predictive contributions of risk factors in the model were ranked as follows: the number of episodes of hypoglycemia in one month, HDL-C, heart disease, diabetes knowledge and education, combination therapy, age, duration of diabetes, staple food restriction, glycosylated hemoglobin, and gender. The internal and external calibration curves of the hypoglycemia random forest model for the older adults with type 2 diabetes fluctuated around the diagonal, indicating that the calibration degree of the predictive model is good. The AUROC of internal verification was 0.823 (95% CI 0.752-0.894), the sensitivity and specificity were 0.867 and 0.698, respectively. The external verification was 0.859 (95% CI 0.817 - 0.902), and sensitivity and specificity were 0.789 and 0.804, respectively, showing that the overall discrimination of the prediction model was good. The DCA curves were far from the all-positive line and all-negative line, which indicated that the prediction model had good clinical applicability. Conclusions:The predictive effect of this model is good, and it is suitable for predicting the risk of hypoglycemia in older adults with type 2 diabetes, and it provides a reference for early hypoglycemia screening and predictive intervention for this kind of patients.

Objective:To explore the current status of medication adherence to immunosuppressants in lung transplant recipients and to analyze its correlation with medication beliefs and perceived social support.Methods:This was a cross-sectional study. From June 2022 to April 2023, totally 233 lung transplant recipients who were followed up in the Department of Lung Transplantation at China-Japan Friendship Hospital were selected by convenience sampling. The Basel Assessment of Adherence to Immunosuppressive Medications Scale (BAASIS), the Chinese version of the Beliefs about Medicines Questionnaire, and the Perceived Social Support Scale (PSSS) were used for the survey. Multiple linear regression analysis was conducted to explore the correlation between medication adherence to immunosuppressants, medication beliefs, and perceived social support in these lung transplant recipients.Results:A total of 233 questionnaires were distributed, with 213 valid responses received (91.42%). The incidence of non-adherence to immunosuppressants among the 213 transplant recipients was 41.78% (89/213), with the most common issue being not taking medication on time (27.23%, 58/213). Multiple linear regression analysis showed that age and perceived social support were influencing factors of medication adherence ( P<0.05) . Conclusions:The current level of medication adherence to immunosuppressants in lung transplant recipients is relatively poor. Medication adherence is correlated with age and the level of perceived social support. Healthcare providers should pay attention to medication adherence in younger patients and enhance their perceived social support to increase adherence to immunosuppressive medications.

OBJECTIVE To explore the distribution pattern of traditional Chinese medicine(TCM)syndrome types of primary o-varian insufficiency(POI)sleep disorders and the differences in the distribution of sleep quality index among different syndrome types,in order to provide a basis for syndrome differentiation treatment and prevention of POI associated with sleep disorders.METHODS 600 POI patients who met the inclusion criteria were collected for epidemiological investigation,and 405 patients who met the diagnosis of sleep disorders were selected as the research group.The patients'general information,TCM four diagnosis and sex hormone level in-formation were collected,and the Pittsburgh sleep quality index(PSQI)scale was used to evaluate patients'sleep conditions,and ana-lyze the characteristics and influencing factors of TCM syndrome types of POI associated with sleep disorders.RESULTS The main TCM syndrome types of POI accompanied by sleep disorders were heart and kidney disharmony syndrome(41.98%),spleen and kid-ney yang deficiency syndrome(22.22%),kidney deficiency and liver stagnation syndrome(20.99%),and kidney deficiency and blood stasis syndrome(14.81%).The heart and kidney disharmony syndrome had the longest sleep latency and shortest sleep time,relied more on hypnotic drugs,and had the highest PSQI total score;the heart and kidney disharmony syndrome and kidney deficiency and liver stagnation syndrome had the worst sleep quality;the spleen kidney yang deficiency syndrome had the highest daytime dysfunc-tion score.There was no significant difference in FSH levels among different TCM syndrome types;the distribution of E2 values from low to high was:heart and kidney disharmony syndrome,kidney deficiency and liver stagnation syndrome,spleen and kidney yang de-ficiency syndrome,and kidney deficiency and blood stasis syndrome,and there were significant differences among multiple groups(P<0.05).CONCLUSION The main TCM syndrome types of patients with POI and sleep disorders are heart and kidney disharmony syn-drome,spleen and kidney yang deficiency syndrome,kidney deficiency and liver stagnation syndrome,and kidney deficiency and blood stasis syndrome.Among them,the most common TCM syndrome type with the worst sleep quality is heart and kidney disharmony syn-drome,which may be closely related to estrogen E2 levels.

Non-alcoholic fatty liver disease (NAFLD) is one of the most common metabolic diseases currently in the context of obesity worldwide, which contains a spectrum of chronic liver diseases, including hepatic steatosis, non-alcoholic steatohepatitis and hepatic carcinoma. In addition to the classical "Two-hit" theory, NAFLD has been recognized as a typical gut microbiota-related disease because of the intricate role of gut microbiota in maintaining human health and disease formation. Moreover, gut microbiota is even regarded as a "metabolic organ" that play complementary roles to that of liver in many aspects. The mechanisms underlying gut microbiota-mediated development of NAFLD include modulation of host energy metabolism, insulin sensitivity, and bile acid and choline metabolism. As a result, gut microbiota have been emerging as a novel therapeutic target for NAFLD by manipulating it in various ways, including probiotics, prebiotics, synbiotics, antibiotics, fecal microbiota transplantation, and herbal components. In this review, we summarized the most recent advances in gut microbiota-mediated mechanisms, as well as gut microbiota-targeted therapies on NAFLD.
Animals ; Bile Acids and Salts ; metabolism ; Choline ; metabolism ; Dietary Supplements ; Energy Metabolism ; Fecal Microbiota Transplantation ; Gastrointestinal Microbiome ; Humans ; Insulin Resistance ; Intestines ; microbiology ; Non-alcoholic Fatty Liver Disease ; microbiology ; therapy