Objective To investigate the effects of the decoction of Huoxue Jiangtang Yin (HJY) combined with metformin on glucose and lipid metabolism and fat factors in type 2 diabetes patients with insulin resistance ( IR). Methods A total of 84 type 2 diabetes patients with IR were randomly assigned to experiment group and control group, 42 cases in each group. Both groups were given the basic treatment of controlling diet, exercise therapy, banning from smoking and alcohol, and psychological therapy. Additionally, the control group was given metformin orally, and the experiment group was given oral use of metformin and the decoction of HJY ( mainly composed of Radix Astragalie, Rhizoma Rehmanniae, Radix Salviae Miltiorrhizae, Radix Pseudostellariae, Fructus Schisandrae, Radix Ophiopogonis, Radix Dioscoreae, Rhizoma Polygonati, Cortex Moutan, Radix et Rhizoma Rhei, Flos Carthami, and Semen Persicae). The treatment for both groups covered 20 weeks for a course of treatment. Before and after the treatment, we observed the indexes of fasting plasma glucose (FPG), 2-h postprandial plasma glucose (2hPG), glycosylated hemoglobin (HbA1c), fasting insulin ( FINS) , homeostasis model assessment of insulin resistance ( HOMA-IR) , body mass index ( BMI) , blood lipid of total cholesterol ( TC) , triglyceride ( TG) and low density lipoprotein cholesterol ( LDL-C) , interleukin 6 ( IL -6) , tumor necrosis factor alpha ( TNF-α) , and secreted frizzled related protein 5 ( SFRP5). Results After treatment, FPG, 2hPG, HbA1c, BMI, FINS, TC, TG, LDL-C, HOMA-IR, TNF-α and IL -6 levels were all decreased (P<0.05 or P<0.01), and SFRP5 level was increased (P<0.05) in both groups compared with those before treatment. The improvement of the indexes in the experiment group was superior to that in the control group ( P<0.05 or P<0.01) Conclusion HJY combined with metformin has an effect on controlling body weight, regulating the glucose and lipid metabolism, and improving IR through regulating fat factors of IL-6, TNF-α and SFRP5 in type 2 diabetes patients with IR.

To summarize the clinical experience of Professor WANG Meng-yong in the treatment of uric acid nephropathy. Professor WANG believes that the disease is mainly caused by spleen and kidney deficiency, disorder of function of Sanjiao, and pathological products, such as phlegm dampness and blood stasis and other metabolic disorders. Therefore, the treatment should distinguish symptoms and essence. Starting from pathogenesis and pathological features of spleen and kidney deficiency and phlegm dampness and blood stasis, the treatment should flexibly apply the methods of nourishing spleen and kidney, reducing phlegm and dispelling humidity, and activating blood and using diuretic of hydragogue to alleviate water retention, which can greatly reduce side effects caused by the long-term use of Western medicine and the onset of gout, and then to help disease recovery.

Aim To study the effect of liraglutide on myocardial metabolites and related metabolic pathways in diabetic cardiomyopathy(DCM)rats.Methods Among 60 SPF male SD rats aged 3 weeks,10 rats were randomly selected as normal control group(n=10),and the remaining 50 rats were established by peritoneal injection of streptozoto-cin combined with high-sugar and high-fat diet for DCM rat model.A total of 36 rats were successfully modeled for DCM and randomly divided into DCM model group(DCM group,n=12),low-dose liraglutide treatment group(LL group,n=12)and high-dose liraglutide treatment group(HL group,n=12).Rats in LL group(100 μg/kg)and HL group(200μg/kg)were given intraperitoneal injection of liraglutide once a day.And after 12 weeks of intervention,the rats were killed under anesthesia after echocardiography to detect cardiac function,and the heart tissues were taken for metabolomics detection.The differential metabolites and related pathways that may be related to liraglutide improving myocardial metab-olism in DCM rats were screened and enriched.Results Compared with normal control group,left ventricular ejection fraction(LVEF)and left ventricular fractional shortening(LVFS)in DCM group were significantly decreased,and the ra-tio of early to late diastolic mitralflow velocities(E/A)was significantly increased(P＜0.05).Compared with DCM group,LVEF and LVFS in LL group and HL group were significantly increased,and E/A ratio was significantly decreased(P＜0.05),suggesting that the impairment of left ventricular systolic and diastolic function in LL group and HL group was significantly alleviated.395 metabolites were detected by metabolomics,among which 239,116 and 187 different metab-olites and 13,6 and 20 metabolic pathways were enriched in DCM group and normal control group,LL group and DCM group,HL group and DCM group.In the above three groups,29 key differential metabolites were identified related to 3 metabolic pathways including choline metabolic pathway,caffeine metabolic pathway and valine,leucine and isoleucine bi-osynthesis pathway,among which choline metabolic pathway had the most significant differences.Conclusion These results indicated that liraglutide can ameliorate cardiac dysfunction in DCM rats through improving myocardial metabolism in which choline metabolism pathway may play a key role.

OBJECTIVE: To explore clinical rules based on the big data of the emergency department of the Second Affiliated Hospital of Guangzhou Medical University, and to establish an integrated platform for clinical research in emergency, which was finally applied to clinical practice. METHODS: Based on the hospital information system (HIS), laboratory information system (LIS), emergency specialty system, picture archiving and communication systems (PACS) and electronic medical record system of the Second Affiliated Hospital of Guangzhou Medical University, the structural and unstructured information of patients in the emergency department from March 2019 to April 2022 was extracted. By means of extraction and fusion, normalization and desensitization quality control, the database was established. In addition, data were extracted from the database for adult patients with pre screening triage level III and below who underwent emergency visits from March 2019 to April 2022, such as demographic characteristics, vital signs during pre screening triage, diagnosis and treatment characteristics, diagnosis and grading, time indicators, and outcome indicators, independent risk factors for poor prognosis in patients were analyzed. RESULTS: (1) The data of 338 681 patients in the emergency department of the Second Affiliated Hospital of Guangzhou Medical University from March 2019 to April 2022 were extracted, including 15 modules, such as demographic information, triage information, visit information, green pass and rescue information, diagnosis information, medical record information, laboratory examination overview, laboratory information, examination information, microbiological information, medication information, treatment information, hospitalization information, chest pain management and stroke management. The database ensured data visualization and operability. (2) Total 140 868 patients with pre-examination and triage level III and below were recruited from the emergency department database. The gender, age, type of admission to the hospital, pulse, blood pressure, Glasgow coma scale (GCS) and other indicators of the patients were included. Taking emergency admission to operating room, emergency admission to intervention room, emergency admission to intensive care unit (ICU) or emergency death as poor prognosis, the poor prognosis prediction model for patients with pre-examination and triage level III and below was constructed. The receiver operator characteristic curve and forest map results showed that the model had good predictive efficiency and could be used in clinical practice to reduce the risk of insufficient emergency pre-examination and triage. CONCLUSIONS The establishment of high-quality clinical database based on big data in emergency department is conducive to mining the clinical value of big data, assisting clinical decision-making, and improving the quality of clinical diagnosis and treatment.
Adult ; Humans ; Big Data ; Emergency Service, Hospital ; Triage/methods* ; Intensive Care Units ; Hospitalization ; Retrospective Studies