BackgroundMultifaceted factors play a crucial role in the prevention and treatment of metabolic dysfunction-associated steatotic liver disease (MASLD). This study aimed to utilize multifaceted indicators to construct MASLD risk prediction machine learning models and explore the core factors within these models.MethodsMASLD risk prediction models were constructed based on seven machine learning algorithms using all variables, insulin-related variables, demographic characteristics variables, and other indicators, respectively. Subsequently, the partial dependence plot(PDP) method and SHapley Additive exPlanations (SHAP) were utilized to explain the roles of important variables in the model to filter out the optimal indicators for constructing the MASLD risk model.ResultsRanking the feature importance of the Random Forest (RF) model and eXtreme Gradient Boosting (XGBoost) model constructed using all variables found that both homeostasis model assessment of insulin resistance (HOMA-IR) and triglyceride glucose-waist circumference (TyG-WC) were the first and second most important variables. The MASLD risk prediction model constructed using the variables with top 10 importance was superior to the previous model. The PDP and SHAP methods were further utilized to screen the best indicators (including HOMA-IR, TyG-WC, age, aspartate aminotransferase (AST), and ethnicity) for constructing the model, and the mean area under the curve value of the models was 0.960.ConclusionsHOMA-IR and TyG-WC are core factors in predicting MASLD risk. Ultimately, our study constructed the optimal MASLD risk prediction model using HOMA-IR, TyG-WC, age, AST, and ethnicity.

This study investigates whether Tobacco smoke exposure (TSE) is associated with C-reactive protein (CRP) and depression and whether CRP plays a mediating role. The data set, including 8,917 adults aged more than 18 years old, was provided by US National Health and Nutrition Examination Survey during 2005-2010 and 2015-2018. A logistic-regression-based mediation analysis was applied to clarify the direct effect of serum cotinine on depression and the indirect effect mediated by CRP. The results indicated that serum cotinine was positively correlated with the risk of depression (Q4 vs. Q1, ORModel 1 = 2.72, 95% CI: 1.95-3.82; ORModel 2 = 1.62, 95% CI: 1.10, 2.37 ORModel 3 = 1.58, 95% CI: 1.07, 2.34). Active smoking was associated with the risk of depression (ORModel 2 = 1.69, 95% CI: 1.23, 2.31; ORModel 3 = 1.66, 95% CI: 1.21, 2.28), while passive smoking was not significantly associated with the risk of depression after adjusting for covariates (ORModel 2 = 1.02, 95% CI: 0.72, 1.44; ORModel 3 = 1.02, 95% CI: 0.72, 1.44). Increased serum cotinine level was associated with an increased risk of depression, and the effect (4.95%) can be explained by a significant indirect effect of CRP (OR = 3.80 × 10-6, 95% CI: 2.81 × 10-7, 8.52 × 10-6). The findings suggest that anti-inflammation may be a potential goal for depression intervention among the tobacco-smoke-exposed population.

Objective: Inflammation and nutrition are interrelated, and both are related to depression. This study explored the association between the C-reactive protein (CRP)-albumin-lymphocyte (CALLY) index, a novel immunonutrition scoring system, and depression in patients with type 2 diabetes mellitus (T2DM). Methods: We included 3517 patients with T2DM from the National Health and Nutrition Examination Survey 2005-2010 and 2015-2018. The Patient Health Questionnaire-9 was used to evaluate depression. The CALLY index was based on a comprehensive assessment of serum CRP, serum albumin, and the lymphocyte counts from whole blood. Weighted multivariate logistic regression models were used to examine the relationship between the CALLY index and depression. The restricted cubic spline was applied to explore the nonlinear relationship. Results: Compared with the non-depressed group, CALLY index and albumin in the depressed group were significantly reduced, while CRP and lymphocytes were significantly increased (P < 0.05). After adjusting for covariates, only the CALLY index significantly decreased (the highest quartile vs the lowest quartile, odds ratio = 0.58, 95 % confidence interval: 0.38-0.89, P = 0.014). The non-linear association between the CALLY index and depression was not significant (P for nonlinear=0.69). The results of subgroup analysis were basically consistent (P for interaction > 0.05). Conclusion: The CALLY index was significantly negatively correlated with depression in American patients with diabetes and served as a potential marker for early identification.

IntroductionTo identify key technologies within non-bioartificial liver (NBAL, an extracorporeal support system that temporarily replaces some of the liver’s functions) nursing to offer guidance for clinical practice. In the context of NBAL nursing, key technologies are crucial for successful implementation of artificial liver treatment, ensuring patient safety, and enhancing nursing quality. A review of both domestic and foreign literature revealed that studies on NBAL nursing technology are lacking and that the key technologies for NBAL nursing have not been clearly identified.MethodsUsing empirical research methods to collect and analyze data. First, the on-site survey method and literature research method were used to create a preliminary screening list of key technologies for NBAL care. Next, the focus group discussion method was used to establish the screening principles and evaluation indicators for these key technologies. Then, a two-round Delphi study via e-mail correspondence was used to screen and determine the key technologies for NBAL care. Finally, the analytic hierarchy process (AHP) and the technique for order preference by similarity to ideal solution (TOPSIS) comprehensive evaluation method were applied to evaluate these key technologies for NBAL care.ResultsSeventeen key technologies for NBAL care were identified. These include three basic nursing technologies, seven operating techniques, three items for treatment process monitoring technology, two items for health education, and two items for complication prevention and treatment technology.ConclusionThis study identified key NBAL nursing technologies, offering a systematic guide to enhance clinical practice. These technologies improve treatment safety, efficacy, and nursing standards, laying a foundation for NBAL care advancement.

Objectives: Burnout influences students’ academic performance and mental health. This study analyzed the relationship between professional identity, self-directed learning ability, and burnout, and examined the mediating effect of self-directed learning ability between professional identity and burnout among nursing students. Methods: 884 nursing students were recruited at two medical universities in China. Demographic information, Academic Burnout Scale, Professional Identity Questionnaire for Nursing Students, and Self-directed Learning Instrument were distributed to collect data. Results: Both professional identity (β = −0.17) and self-directed learning ability (β = −0.43) showed negative associations with students’ burnout. Meanwhile, there was a partial mediating effect of self-directed learning ability between professional identity and burnout (−0.24, 95% CI = −0.30, −0.20). Conclusions: Nursing educators should emphasize on developing effective strategies to improve nursing students’ professional identity and self-directed learning ability to prevent or reduce their burnout.

OBJECTIVE:Bayesian network (BN) models were developed to explore the specific relationships between influencing factors and type 2 diabetes mellitus (T2DM), coronary heart disease (CAD), and their comorbidities. The aim was to predict disease occurrence and diagnose etiology using these models, thereby informing the development of effective prevention and control strategies for T2DM, CAD, and their comorbidities.METHOD:Employing a case-control design, the study compared individuals with T2DM, CAD, and their comorbidities (case group) with healthy counterparts (control group). Univariate and multivariate Logistic regression analyses were conducted to identify disease-influencing factors. The BN structure was learned using the Tabu search algorithm, with parameter estimation achieved through maximum likelihood estimation. The predictive performance of the BN model was assessed using the confusion matrix, and Netica software was utilized for visual prediction and diagnosis.RESULT:The study involved 3,824 participants, including 1,175 controls, 1,163 T2DM cases, 982 CAD cases, and 504 comorbidity cases. The BN model unveiled factors directly and indirectly impacting T2DM, such as age, region, education level, and family history (FH). Variables like exercise, LDL-C, TC, fruit, and sweet food intake exhibited direct effects, while smoking, alcohol consumption, occupation, heart rate, HDL-C, meat, and staple food intake had indirect effects. Similarly, for CAD, factors with direct and indirect effects included age, smoking, SBP, exercise, meat, and fruit intake, while sleeping time and heart rate showed direct effects. Regarding T2DM and CAD comorbidities, age, FBG, SBP, fruit, and sweet intake demonstrated both direct and indirect effects, whereas exercise and HDL-C exhibited direct effects, and region, education level, DBP, and TC showed indirect effects.CONCLUSION:The BN model constructed using the Tabu search algorithm showcased robust predictive performance, reliability, and applicability in forecasting disease probabilities for T2DM, CAD, and their comorbidities. These findings offer valuable insights for enhancing prevention and control strategies and exploring the application of BN in predicting and diagnosing chronic diseases.

ObjectiveThe purpose of this investigation was to evaluate the potential link between physical activity (PA) and the heightened susceptibility to diabetes mellitus (DM), by examining whether remnant cholesterol (RC) might act as a mediator in this correlation.MethodsThe research utilized data from the National Health and Nutrition Examination Survey, spanning from 2005 to 2018. Various statistical analyses were conducted for continuous and categorical variables, including the t-test, ANOVA, and χ2 test. Logistic regression was employed to analyze the association between PA and DM across three distinct models. Mediation analysis was also conducted to assess the potential mediation effects of RC.ResultsThe study encompassed a total of 9,149 participants, and it was observed that individuals with DM exhibited lower levels of PA. Furthermore, PA levels were found to be associated with all participant characteristics except poverty income ratio, fasting blood glucose, and HOMA-IR (p < 0.05). After adjusting for covariates (Model 3), individuals with high PA levels demonstrated a decreased likelihood of developing DM compared to those in the low PA group (OR: 0.73, 95%CI: 0.54–0.99). A significant dose–response relationship was identified (p < 0.05). No interaction between PA and RC in relation to DM risk was detected, and RC was found to serve as a mediator in the connection between PA and DM. After considering covariates, the mediating effect of RC between PA and DM weakens.DiscussionOur findings suggest that higher levels of PA are linked to a reduced risk of DM in U.S. adults, with RC likely playing a mediating role.

BACKGROUND Portal hypertension (PHT), primarily induced by cirrhosis, manifests severe symptoms impacting patient survival. Although transjugular intrahepatic portosystemic shunt (TIPS) is a critical intervention for managing PHT, it carries risks like hepatic encephalopathy, thus affecting patient survival prognosis. To our knowledge, existing prognostic models for post-TIPS survival in patients with PHT fail to account for the interplay among and collective impact of various prognostic factors on outcomes. Consequently, the development of an innovative modeling approach is essential to address this limitation. AIM To develop and validate a Bayesian network (BN)-based survival prediction model for patients with cirrhosis-induced PHT having undergone TIPS. METHODS The clinical data of 393 patients with cirrhosis-induced PHT who underwent TIPS surgery at the Second Affiliated Hospital of Chongqing Medical University between January 2015 and May 2022 were retrospectively analyzed. Variables were selected using Cox and least absolute shrinkage and selection operator regression methods, and a BN-based model was established and evaluated to predict survival in patients having undergone TIPS surgery for PHT. RESULTS Variable selection revealed the following as key factors impacting survival: age, ascites, hypertension, indications for TIPS, postoperative portal vein pressure (post-PVP), aspartate aminotransferase, alkaline phosphatase, total bilirubin, prealbumin, the Child-Pugh grade, and the model for end-stage liver disease (MELD) score. Based on the above-mentioned variables, a BN-based 2-year survival prognostic prediction model was constructed, which identified the following factors to be directly linked to the survival time: age, ascites, indications for TIPS, concurrent hypertension, post-PVP, the Child-Pugh grade, and the MELD score. The Bayesian information criterion was 3589.04, and 10-fold cross-validation indicated an average log-likelihood loss of 5.55 with a standard deviation of 0.16. The model’s accuracy, precision, recall, and F1 score were 0.90, 0.92, 0.97, and 0.95 respectively, with the area under the receiver operating characteristic curve being 0.72. CONCLUSION This study successfully developed a BN-based survival prediction model with good predictive capabilities. It offers valuable insights for treatment strategies and prognostic evaluations in patients having undergone TIPS surgery for PHT.

Abstract Background Physical disability is an important cause of affecting the quality of life in the elderly. The association between standing height and physical disability is less studied. Purpose The purpose of this study is to investigate the possible link between standing height and physical disability among U.S. adults aged 60 years and older. Methods The cross-sectional data were obtained from the US National Health and Nutrition Examination Survey (NHANES) 2015–2018. Physical disability was assessed by six questions: “Have serious difficulty hearing (SDH)?”, “Have serious difficulty seeing (SDS)?”, “Have serious difficulty concentrating (SDC)?”, “Have serious difficulty walking (SDW)?”, “Have difficulty dressing or bathing (DDB)?” and “Have difficulty doing errands alone (DDEA)?”. Responses to these questions were “yes” or “no”. Answer yes to one of the above six questions was identified as physical disability. Standing height (cm) was measured with an altimeter. Multivariate logistic regression was performed to examine the possible link between standing height and physical disability after adjustment for all covariates. Results A total of 2624 participants aged ≥ 60 years were included in our study, including 1279 (48.7%) females and 1345 (51.3%) males. The mean age of participants was 69.41 ± 6.82 years. After adjusting for all potential confounders, the inverse relationship between standing height and all physical disability (APD) was statistically significant (OR = 0.976, 95%CI:0.957–0.995). In addition, among six types of physical disability (SDH, SDS, SDC, SDW, DDB, DDEA), standing height was also a protective factor for SDW (OR = 0.961, 95%CI:0.939–0.983) and DDEA (OR = 0.944, 95%CI:0.915–0.975) in the full-adjusted model. Conclusion The cross-sectional population based study demonstrates that standing height is a protective factor for physical disability among U.S. adults aged 60 years and older.

Objectives To compare the prediction effects of six models based on machine learning theories, which can provide a methodological reference for predicting the risk of type 2 diabetes mellitus (T2DM).Setting and participants This study was based on the monitoring data of chronic disease risk factors in Dongguan residents from 2016 to 2018. The multistage cluster random sampling method was adopted at each monitoring site, and 4157 people were finally selected. In the initial population, we excluded individuals with more than 20% missing data and eventually included 4106 subjects.Design K nearest neighbour algorithm and synthetic minority oversampling technique were used to process the data. Single factor analysis was used for preliminary selection of variables. The 10-fold cross-validation was used to optimise the parameters of some models. The accuracy, precision, recall and area under receiver operating characteristic curve (AUC) were used to evaluate the prediction effect of models, and Delong test was used to analyse the differences of AUC values of each model.Results After balancing data, the sample size increased to 8013, of which 4023 are patients with T2DM and 3990 in control group. The comparison results of the six models showed that back propagation neural network model has the best prediction effect with 93.7% accuracy, 94.6% accuracy, 92.8% recall and the AUC value of 0.977, followed by logistic model, support vector machine model, CART decision tree model and C4.5 decision tree model. Deep neural network has the worst prediction performance, with 84.5% accuracy, 86.1% precision, 82.9% recall and the AUC value of 0.845.Conclusions In this study, six types of risk prediction models for T2DM were constructed, and the predictive effects of these models were compared based on various indicators. The results showed that back propagation neural network based on the selected data set had the best prediction effect.

Patients with decompensated cirrhosis, a symptomatic phase of cirrhosis, commonly experience multiple symptoms concurrently, referred to as symptom clusters. Effective self-management of symptoms is known to improve outcomes in various chronic diseases. However, a theory for self-management of symptom clusters in decompensated cirrhosis is lacking. In this study, we applied grounded theory research methodology to construct a new theory of self-management of symptom clusters in these patients. This qualitative study prospectively enrolled 20 patients with decompensated cirrhosis within 1 week after hospital admission. Data related to patients' experiences, needs, perspectives, and abilities related to their symptoms were collected via a semi-structured, in-depth interview and analyzed with Nvivo version 20 software. Grounded theory methodology with 3 coding steps (open, axial, and selective coding) was applied to generate a theory of self-management of symptom clusters. From the step-by-step coding process, 2 core categories or major themes were identified: patients' experiences with symptoms and coping with symptoms. The first major theme included symptom clustering, multidimensionality, recurrence, and specificity, while the second consisted of endogenous motivation, endogenous resistance, and external support needs. A new theory of self-management of symptom clusters was then constructed and delineated to enhance self-management among patients with decompensated cirrhosis. Using patient experience data, we developed a new theory of self-management of symptom clusters in patients with decompensated cirrhosis. Use of this theory has the potential to promote patient self-management and guide healthcare providers in planning optimal treatments and implementing timely interventions, ultimately improving in patient outcomes.

ObjectiveTo explore the protective effect and mechanism of salidroside on hypoxia-reoxygenationtreated human coronary endothelial cells（HCAEC）.MethodsThe HCAEC ischemia/reperfusion injury model was established by hypoxia-reoxygenation,and different doses of salidroside(10,20,40 μmol·L -1 )were given for intervention. Cell viability was detected by CCK-8,cell apoptosis and reactive oxygen species（ROS）levels were detected by flow cytometry. Superoxide dismutase（SOD）,glutathione（GSH）activity,malondialdehyde（MDA）content were detected by spectrophotometry. The acetylation levels of divalent metal ion transporter 1（DMT1）,ferroportin（FPN）,silent information regulator 1（SIRT1）,and forkhead box transcription factor 1（FoxO1）were detected by Western Blot. After siRNA was used to silence SIRT1, a high-dose of salidroside was given to intervene,and the contents of SOD,GSH and MDA were detected.ResultsCompared with the control group,cell viability, SOD and GSH activities and FoxO1 acetylation level decreased in the hypoxia-reoxygenation model group,and the apoptosis rate,MDA content,ROS level,SIRT1,DMT1,FPN expression increased（P＜0.05,P＜0.01）. Compared with the hypoxia-reoxygenation group,the above indicators in each group were reversed after the intervention of low-,medium-and high-doses of salidroside,and the difference between the medium-and high-dose groups was statistically significant（P＜0.05）,and there was a certain dose dependence（P＜0.05）.SIRT1-specific siRNA significantly down-regulated the expression of SIRT1（P＜0.01）. After SIRT1 was silenced,the up-regulating effect of salidroside on SOD and GSH activities and the down-regulating effect of MDA content were all reversed.ConclusionSalidroside could improve hypoxia-reoxygenation-induced damage in HCAEC by reducing apoptosis, improving iron metabolism, and inhibiting oxidative stress through SIRT1/FoxO1 signaling pathway.

Aim The association of polymorphisms in the three genes of SOCS3, JAK2 and STAT3 with genetic susceptibility to type 2 diabetes mellitus (T2DM) was explored, and its interaction with environmental factors such as hypertension and triglycerides was analyzed. Methods The Hardy–Weinberg balance test was used to analyze the random balance of genes in the population. The analysis of the association of SNPs with T2DM was performed using Pearson’s chi-square test. Haplotype frequency distribution, SNPs-SNPs interaction and environmental factors were analyzed by chi-square test and logistic regression. Results The genotype distribution of SNPs rs2280148 of the SOCS3 gene was statistically significant. The allele frequency distribution of SNPs (rs4969168/rs2280148) was statistically different. After covariate correction, the SOCS3 gene locus (rs4969168) showed an association with T2DM in additive model, while the rs2280148 locus showed an association with T2DM in all three models. The locus (rs10974914/rs10815157) allele and genotype frequency distribution of JAK2 were statistically significant. After covariate correction, two SNPs in the gene showed association with T2DM in both additive and recessive models. The distribution of genotype frequencies of SNPs rs1053005 locus in gene STAT3 was statistically significant between the two groups. In recessive genetic models, rs1053005 locus polymorphisms was associated with T2DM. Haplotype S3 (G G)/S 4 (G T) of the SOCS3 gene as well as haplotype J2 (A G)/J 3 (G C) of the JAK2 gene were closely associated with T2DM. There was an interaction between SNPs rs4969168 and SNPs rs2280148 in the SOCS3 gene. There was an interaction between the SOCS3, JAK2 and STAT3 genes and hypertension/triglycerides. Conclusion The SOCS3 and JAK2 genes may be associated with T2DM in the Chinese population, in which SNPs carrying the A allele (rs4969168)/G allele (rs2280148)/C allele (rs10815157) have a reduced risk of T2DM. Haplotype S3 (G G)/S 4 (G T) of the SOCS3 gene and haplotype J2 (A G)/J 3 (G C) of the JAK2 gene may be influencing factor for T2DM. The interaction between SNPs rs4969168 and SNPs rs2280148 increases the risk of T2DM. Hypertension and triglycerides may interact with SNPs of T2DM susceptibility genes.

The structure of a back propagation neural network was optimized by a particle swarm optimization (PSO) algorithm, and a back propagation neural network model based on a PSO algorithm was constructed. By comparison with a general back propagation neural network and logistic regression, the fitting performance and prediction performance of the PSO algorithm is discussed. Furthermore, based on the back propagation neural network optimized by the PSO algorithm, the risk factors related to hypertension were further explored through the mean influence value algorithm to construct a risk prediction model. In the evaluation of the fitting effect, the root mean square error and coefficient of determination of the back propagation neural network based on the PSO algorithm were 0.09 and 0.29, respectively. In the comparison of prediction performance, the accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve of the back propagation neural network based on PSO algorithm were 85.38%, 43.90%, 96.66%, and 0.86, respectively. The results showed that the backpropagation neural network optimized by PSO had the best fitting effect and prediction performance. Meanwhile, the mean impact value algorithm could screen out the risk factors related to hypertension and build a disease prediction model, which can provide clues for exploring the pathogenesis of hypertension and preventing hypertension.

BACKGROUND:Chronic inflammation of adipose tissue may be one of the key factors contributing to the development of insulin resistance in T2DM adipose tissue. Transient receptor potential vanilloid type 4 (TRPV4) can be involved in a variety of cellular inflammatory responses. In this study, we evaluated the role of TRPV4 channelin in the T2DM adipose tissue inflammatory pathway. METHODS:Based on the gene expression profiling data of the public database, bioinformatics methods were used to screen the target gene population of the TRPV4 channel protein involved in the regulation of T2DM fat cells. A mature adipocyte model was constructed to verify the expression level of target genes and to evaluate the regulatory effect of TRPV4 channel inhibition on target genes of inflammation-related pathways. RESULTS:In shTRPV4 adipocytes, 144 genes with downregulation expression were screened, a PPI network was constructed and a core module containing 15 genes was screened out, and the core genes were mainly enriched in the Toll-like receptor signaling pathway through enrichment analysis. Constructing a mature adipocyte model found that the TRPV4 inhibitor HC067047 inhibited the effect of upregulation of the expression level of the relevant gene in the signaling pathway. CONCLUSIONS:Our findings suggest that the expression of highly expressed pro-inflammatory cytokines and chemokines in T2DM adipose tissue decreases after inhibiting the expression of TRPV4 in adipocytes, suggesting that TRPV4 may become a potential drug target for the treatment of T2DM.

目的 分析东莞市新型冠状病毒肺炎(COVID-19)疫情的流行特征,为疫情防控提供参考.方法 采用横断面研究,收集东莞市COVID-19疫情病例个案信息资料进行统计描述和统计分析.结果 截至2021年2月28日,东莞市累计报告136例新冠肺炎病例,其中确诊病例101例,无症状感染者35例.确诊病例以待业人群为主,年龄集中在30～岁(29.70％);男性52例,女性49例,男女性别比为1.06∶1;其中,重症病例17例、普通型67例、轻症病例16例、死亡1例;从发病到就诊平均需1.5 d,到确诊平均需5 d.无症状感染者中主要年龄集中在30～岁(25.71％);男性24例;女性11例,男女性别比为2.18∶1,多见于学生群体.报告确诊病例数最多的是南城街道(11例);而无症状感染者最多的是大岭山镇(5例).流行病学史明确者136例,确诊患者中有湖北旅居史68例,无症状感染者中有湖北旅居史10例.确诊患者与无症状感染者的发现途径差异有统计学意义(P＜0.05),疾病进程也不尽相同.累计境外输入感染者病例数与累计新增感染者病例数呈高度正相关.结论 东莞市新冠确诊病例与无症状感染者的性别、年龄具有一定的相似性,其他流行病学特征均有所差异.疫情由输入性向本地续发过渡,并以境外输入性病例为主.

The aim of the present study is to explored the relationship between ADIPO signalling pathway and T2DM, to provide clues for further study of the pathogenesis of T2DM and to determine the possible drug targets. This study employed a case-control study design. Twenty-three single nucleotide polymorphisms (SNPs) of 13 genes in the selected ADIPO signalling pathway were genotyped by SNPscanTM kit. All statistical analysis was performed by SPSS 25.0, PLINK 1.07, R 2.14.2, Haploview 4.2, SNPstats, and other statistical software packages. In the association analysis based on a single SNPs, rs1044471 had statistical significance in the overdominant model without adjusting covariates. Rs1042531 had statistical significance in the overdominant model. Rs12718444 had statistical significance in the recessive model. There was a linkage disequilibrium between the loci within 9 genes, and the two loci in RXRA gene did not form blocks. Four kernel functions were used for SNPs set analysis based on ADIPO signalling pathway showed that there was no statistical significance whether covariates were added or not, P>0.05.According to our research results, it is found that some single nucleotide polymorphisms (ADIPOR2 rs1044471, PCK1 rs1042531, GLUT1 rs12718444) in the adiponectin signalling pathway may be associated with T2DM.

Objective. This study is aimed at analyzing the relationship between leptin (LEP) signaling pathway and type 2 diabetes mellitus (T2DM) and at providing support for molecular genetic research on the pathogenesis of T2DM in Chinese Han population. Methods. A case-control study was designed, including 1092 cases with T2DM and 1092 healthy controls of Chinese Han origin recruited from ten hospitals in Guangdong Province, Southern China. Twenty-three single nucleotide polymorphisms (SNPs) of 15 genes in LEP signaling pathway were genotyped by SNPscan™ kit. The Pearson chi-square test, Cochran-Armitage trend test, MAX3, and logistic regression were applied to analyze the association between single nucleotide polymorphism (SNP) and T2DM; unconditional logistic regression was used to analyze haplotype in LD block; and SNP set analysis based on logistic kernel machine regression was used to analyze pathway. All statistical analysis was performed by SPSS25.0, R2.14, Haploview4.2, SNPStats, and other statistical software packages. Results. In association analysis based on SNP, rs2167270 had statistical significance both in the adjusted and unadjusted covariate dominant model and in the unadjusted covariate overdominant model while it had no significant difference in the adjusted covariate overdominant model. Compared to GG genotype, rs2167270 of AG genotype had statistical significance in both the adjusted and unadjusted covariate codominant models. And rs16147 had statistical significance in robust test, stealth model and overdominant model, and adjusting and unadjusting covariate. This study found linkage disequilibrium existed between rs2167270 and rs4731426 of LEP, rs10889502 and rs17127107 of JAK1, rs2970847 and rs6821591 of PPARGC1A, rs249429 and rs3805486 of PRKAA1, rs1342382 and rs6588640 of PRKAA2, rs3766522 and rs6937 of PRKAB2, rs2970847 and rs6821591 of PRKAG2, and rs6436094 and rs645163 of PRKAG3. There was no positive finding with statistical significance from the unconditional logistic regression of the mentioned genes’ haplotype of LD block. Conclusions. LEP signaling pathway association with T2DM remained to be confirmed in Chinese Han population, although rs2167270 and rs16147 were significantly associated with T2DM.

Background and Objectives: Hepatic encephalopathy is a common complication in patients who have received transjugular intrahepatic portosystemic shunt (TIPS) as treatment for cirrhosis. The objective of this study was to reduce the incidence of post-TIPS hepatic encephalopathy for these patients via positive diet intervention. Methods and Study Design: As a control group, 99 cirrhosis patients who underwent TIPS treatment in our department between August 2011 and February 2013 were chosen. Among these, postoperative hepatic encephalopathy occurred in 28 cases. After analyzing the possible causes and incentives of hepatic encephalopathy by applying retrospective analysis, it was seen that hepatic encephalopathy was caused mostly by improper diet (85.7%). The experimental group was comprised of 83 cirrhosis patients who underwent TIPS treatment during the period from May 2013 to September 2014. In view of the influence of improper diet, appropriate intervention measures were taken, including developing a postoperative nursing routine, training nurses about nutrition and the protein content of kinds of various common foods, customizing low-protein meals, training nurses in communication skills to improve the education of patients and establishing family support systems. Results: For the experimental group, hepatic encephalopathy occurred in 10 patients, for an incidence of 12.1%, which is significantly lower than the control group (28.3%). This is a statistically significant difference (p<0.01) in the occurrence of this complication. Conclusions: After TIPS, early positive dietary intervention can significantly improve the compliance of cirrhosis patients to consume a low-protein diet and reduce the incidence of hepatic encephalopathy.

[目的]探讨新分型与治疗对策下外伤性幕上急性硬膜下血肿的护理对策及对预后的影响。[方法]对206例外伤性幕上急性硬膜下血肿病人的临床资料进行回顾性分析，寻找临床实用的护理对策，总结护理经验，达到改善疗效、提高生活质量的目的。[结果]恢复良好137例，中度残疾32例，严重残疾18例，植物状态生存8例，死亡11例。[结论]针对不同分型及治疗措施，动态观察病情变化、准确评估和实施有效的护理措施是提高病人生存率和降低致残率的重要保证。