【摘要】 目的 探讨腹部非胃、十二指肠手术后胃瘫综合征（postoperative gastroparesis syndrome，PGS）发生的高危因素。 方法 回顾分析2004年9月-2010年3月2 559例腹部非胃、十二指肠术后患者的临床资料，将患者分为PGS组和非PGS组，其中PGS组23例，非PGS组2 536例。 结果 比较PGS组和非PGS组间年龄、性别、术后开始进食时间、手术持续时间、是否为肿瘤晚期、有无贫血低蛋白血症、既往有无腹部手术史、术后早期有无营养支持等因素，χ2值分别为:19.687、0.018、0.346、48.243、21.801、16.803、24.679、0.870，P值分别是:lt;0.01、gt;0.05、gt;0.05、lt;0.01、lt;0.01、lt;0.01、lt;0.01、gt;0.05。 结论 年龄gt;65岁、手术持续时间gt;4 h、肿瘤晚期、既往有腹部手术史及贫血低蛋白血症是腹部非胃、十二指肠手术后PGS发生的高危因素。【Abstract】 Objective To analyze the risk factors of postoperative gastroparesis syndrome (PGS) after non-gastroduodenal abdominal surgery. Methods We retrospectively analyzed the clinical data of 2 559 patients who underwent non-gastroduodenal abdominal surgeries in our hospital between September 2004 and March 2010. We divided them into the PGS group with 23 patients and the non-PGS group with 2 536 patients. Results By comparing the age, the gender, the starting time of eating after surgery, the duration of surgery, whether the patients had advanced cancer, whether anemia or hypoproteinemia existed, whether the patients had a history of previous abdominal surgery, and whether nutritional support was provided early after operation between the PGS group and the non-PGS group, we found that the chi-square value was 19.687, 0.018, 0.346, 48.243, 21.801, 16.803, 24.679, 0.870 and the P value waslt;0.01, gt;0.05, gt;0.05, lt;0.01, lt;0.01, lt;0.01, lt;0.01, gt;0.05 respectively. Conclusion Over 65 years of age, the duration of surgery over four hours, advanced cancer, the history of previous abdominal surgery and anemia or hypoproteinemia are the risk factors of PGS after non-gastroduodenal abdominal surgery.
Objective To explore the potential molecular mechanism of Rhodiola crenulata (RC) for type 2 diabetes mellitus (T2DM) and Alzheimer’s disease (AD) by network pharmacology and molecular docking. Methods The target genes of T2DM and AD, the effective active components and targets of RC were identified through multiple public databases during March to August, 2022. The main active components and core genes of RC anti T2DM-AD were screened. The key genes were enrichment analyzed by gene ontology function and Kyoto gene and Kyoto Encyclopedia of Genes and Genomes. AutoDock Vina was used for molecular docking and binding energy calculation. Results A total of 5189 T2DM related genes and 1911 AD related genes were obtained, and the intersection result showed that there were 1418 T2DM-AD related genes. There were 48 active components of RC and 617 corresponding target genes. There were 220 crossing genes between RC and T2DM-AD. The main active components of RC anti T2DM-AD included kaempferol, velutin, and crenulatin. The key genes for regulation include ESR1, EGFR, and AKT1, which were mainly enriched in the hypoxia-inducible factor-1 signal pathway, estrogen signal pathway, and vascular endothelial growth factor signal pathway. The docking binding energies of the main active components of RC and key gene molecules were all less than −1.2 kcal/mol (1 kcal=4.2 kJ). Conclusions RC may play a role in influencing T2DM and AD by regulating the hypoxia-inducible factor-1 signaling pathway, estrogen signaling pathway, and vascular endothelial growth factor signaling pathway.
ObjectiveTo explore the metabolic changes during the differentiation of 3T3-L1 adipocytes caused by the treatment of the transient receptor potential vanilloid 4 (TRPV4)-specific agonist GSK1016790A basing on ultra-performance liquid chromatography-mass spectrometry technology. MethodsMouse 3T3-L1 cells were treated with GSK1016790A at different concentrations (0.1, 1, and 10 μmol/L), and the effect of drugs on cell proliferation was detected by cell counting kit-8 method. A mature adipocyte model was constructed, and GSK1016790A was used to activate TRPV4 channel protein activity and verify the expression levels of TRPV4 and triglycerides. Cell metabolites were collected for metabolomic studies, differential metabolites were screened between groups, and related metabolic pathways were analyzed. Results After GSK1016790A intervened in mature adipocytes, the expression levels of TRPV4 mRNA and triglycerides in cells were significantly upregulated (P<0.05). Metabolomics detection found that GSK1016790A screened a total of 45 differential metabolites such as 2-amino-1,3,4-octadecanetriol, linoleic acid, sphingosine, sphinganine, sn-glycerol-3-phosphate and uridine, mainly involving 13 possible metabolic pathways such as sphingolipid metabolism and biosynthesis of unsaturated fatty acids. Conclusion GSK1016790A may promote adipogenesis in adipocytes by activating TRPV4 channel protein activity, and at the same time participate in regulating metabolic pathways such as the biosynthesis of unsaturated fatty acids pathway and sphingolipid metabolism pathway, affecting lipid metabolism in adipocytes.
Objective To analyze the risk factors of type 2 diabetes mellitus and establish BP neural network model for screening of type 2 diabetes mellitus based on particle swarm optimization (PSO) algorithm. Methods Inpatients with type 2 diabetes mellitus in the Department of Endocrinology of the Affiliated Hospital of Guangdong Medical University and the Second Affiliated Hospital of Guangdong Medical University between July 2021 and August 2022 were selected as the case group and healthy people in the Health Management Center of the Affiliated Hospital of Guangdong Medical University as the control group. Basic information and physical and laboratory examination indicators were collected for comparative analysis. PSO-BP neural network model, BP neural network model and logistic regression models were established using MATLAB R2021b software and the optimal screening model of type 2 diabetes mellitus was selected. Based on the optimal model, the mean impact value algorithm was used to screen the risk factors of type 2 diabetes mellitus. Results A total of 1 053 patients were included in the case group and 914 healthy peoples in the control group. Except for type of salt, family history of comorbidities, body mass index, total cholesterol, low density lipoprotein cholesterol and staple food intake (P>0.05), the other indexes showed significant differences between the two groups. The performance of the PSO-BP neural network model outperformed the BP neural network model and the logistic regression model. Based on PSO-BP neural network model, the mean impact value algorithm showed that the risk factors for type 2 diabetes mellitus were fasting blood glucose , heart rate, age , waist-arm ratio and marital status , and the protective factors for type 2 diabetes mellitus were high density lipoprotein cholestero, vegetable intake, residence, education level, fruit intake and meat intake. Conclusions There are many influencing factors of type 2 diabetes mellitus. Focus should be placed on high-risk groups and regular disease screening should be carried out to reduce the risk of type 2 diabetes. The screening model of PSO-BP neural network performs the best, and it can be extended to the early screening and diagnosis of other diseases in the future.