Prevalence of S. aureus and/or MRSA in hospitalized patients with diabetic foot and establishment of LAMP methods for rapid detection of the SCCmec gene.

BACKGROUND: Patients with diabetic feet are prone to be infected due to the impaired immune system. However, the prognostic outcome of different microbial infections remains controversial. Identification and rapid screening of the pathogenic microorganisms that pose the greatest threat to the prognosis of patients with diabetic foot infections (DFIs) is critical. METHODS: Clinical data were statistically analyzed, which were obtained from 522 patients with DFIs, including pathogenic bacterial culture results and treatment outcomes at the last return visit. In addition, a loop-mediated isothermal amplification (LAMP) detection method was developed to identify the prevalent subtype of methicillin-resistant Staphylococcus aureus (MRSA) in DFIs patients. This study was approved by the Ethics Committee of Nanfang Hospital (NFEC-202012-K6) and registered on ClinicalTrials.gov (NCT04916457) on June 1, 2021. RESULTS: We found that the proportion of patients with infections of Staphylococcus aureus (S. aureus) and MRSA was 27.7% (145/522) and 33.7% (49/145), respectively. Additionally, the incidence of osteomyelitis was 46.9% (23/49) and amputation/disability was 40.8% (20/49) in patients with MRSA infection, which were significantly higher compared to patients with other types of bacterial infections such as methicillin-susceptible Staphylococcus aureus (MSSA). Notably, we demonstrated that the main prevalent subtype of MRSA in DFIs patients in our hospital was Staphylococcal chromosomal cassettes mec (SCCmec) type II. In addition, it only takes 1.5 h to complete the entire experimental procedure in this LAMP assay, providing high sensitivity (100%) and specificity (77.8%) in hospitalized patients with DFIs. CONCLUSIONS: We demonstrated there is a very high rate of MRSA isolation in patients with DFIs and revealed that patients infected with MRSA are at a higher risk of developing osteomyelitis, and amputation or disability. Importantly, we have developed a method for quickly screening newly admitted patients for MRSA.

Bayesian network analysis of factors influencing type 2 diabetes, coronary heart disease, and their comorbidities.

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.

Research Progress on Alzheimer's Disease and Resveratrol.

Alzheimer's disease (AD), a common irreversible neurodegenerative disease characterized by amyloid-ß plaques, neurofibrillary tangles, and changes in tau phosphorylation, is accompanied by memory loss and symptoms of cognitive dysfunction. Increases in disease incidence due to the ageing of the population have placed a great burden on society. To date, the mechanism of AD and the identities of adequate drugs for AD prevention and treatment have eluded the medical community. It has been confirmed that phytochemicals have certain neuroprotective effects against AD. For example, some progress has been made in research on the use of resveratrol, a natural polyphenolic phytochemical, for the prevention and treatment of AD in recent years. Elucidation of the pathogenesis of AD will create a solid foundation for drug treatment. In addition, research on resveratrol, including its mechanism of action, the roles of signalling pathways and its therapeutic targets, will provide new ideas for AD treatment, which is of great significance. In this review, we discuss the possible relationships between AD and the following factors: synapses, α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA)-type glutamate receptors (AMPARs), silent information regulator 1 (SIRT1), and estrogens. We also discuss the findings of previous studies regarding these relationships in the context of AD treatment and further summarize research progress related to resveratrol treatment.

The Effect of Antioxidant Vitamins on Patients With Diabetes and Albuminuria: A Meta-Analysis of Randomized Controlled Trials.

OBJECTIVE: This study aimed to investigate the effect of antioxidant vitamins, including vitamins E and C, on patients with diabetes and albuminuria by conducting a meta-analysis of randomized controlled trials. DESIGN: The PubMed, Embase, CENTRAL (the Cochrane Central Register of Controlled Trials at the Cochrane Library), Web of Science, OVID, and www.clinicaltrials.gov (latest search: December 10, 2018) databases were searched. This study was limited to randomized controlled trials. Patients with diabetes and albuminuria were included regardless of diabetic type, and patients must have received treatment with vitamins C or E. RESULTS: Ten studies, representing 445 participants, were identified for analysis. Antioxidant vitamins had significant effects on serum creatinine levels (mean difference = -0.11 mg/dL, 95% confidence interval -0.19 to -0.03, P = .007) and systolic pressure (mean difference = -6.02 mm Hg, 95% confidence interval -9.65 to -2.40, P = .001) with low heterogeneity. Antioxidant vitamins had no effect on albuminuria or proteinuria, diastolic blood pressure, glucose, or lipid metabolism. CONCLUSION: This meta-analysis indicated that antioxidant vitamins can benefit kidney function and systolic blood pressure in patients with diabetes and albuminuria. Further studies with larger sample sizes and longer follow-up are needed to completely understand the effect of antioxidant vitamins in these patients.

Overexpression of Circular RNA ciRS-7 Abrogates the Tumor Suppressive Effect of miR-7 on Gastric Cancer via PTEN/PI3K/AKT Signaling Pathway.

Gastric cancer (GC) has one of the highest mortality rates of malignancies globally. Currently, ciRS-7, a novel circular RNA, has emerged as a potential sponge for miR-7. However, few studies on ciRS-7 in GC have been performed. In this study, we investigated the clinical significance and function of ciRS-7 in GC. First, the expression levels of ciRS-7 in 102 primary GC tissues and the matched para-carcinoma tissues were evaluated and the clinical relevance was confirmed in an independent validation cohort (n = 154). Second, the effects of ciRS-7 on miR-7, PTEN, and PI3K were evaluated. Finally, the function of ciRS-7 in GC was analyzed with cell lines and nude mice. The expression of ciRS-7 was significantly upregulated in GC tissues compared with the matched para-carcinoma tissues (P = 0.0023), and the upregulation of ciRS-7 was linked to poor survival in the testing (P = 0.0143) and validation cohort (P = 0.0061). Multivariate survival analysis revealed that ciRS-7 was probably an independent risk factor of overall survival (P < 0.05). Furthermore, overexpression of ciRS-7 blocked the miR-7-induced tumor suppression in MGC-803 and HGC-27 cells and led to a more aggressive oncogenic phenotype, via antagonizing miR-7-mediated PTEN/PI3K/AKT pathway. ciRS-7 may act as a prospective prognostic biological marker and a promising therapeutic target for GC. J. Cell. Biochem. 119: 440-446, 2018. © 2017 Wiley Periodicals, Inc.

A systematic study on dysregulated microRNAs in cervical cancer development.

MicroRNAs (miRNAs) are short regulatory RNAs that modulate the transcriptome and proteome at the post-transcriptional level. To obtain a better understanding on the role of miRNAs in the progression of cervical cancer, meta-analysis and gene set enrichment analysis were used to analyze published cervical cancer miRNA studies. From 85 published reports, which include 3,922 cases and 2,099 noncancerous control tissue samples, 63 differentially expressed miRNAs (DEmiRNAs) were identified in different stages of cervical cancer development (CIN 1-3 and CC). It was found that some of the dysregulated miRNAs were associated with specific stages of cervical cancer development. To illustrate the impact of miRNAs on the pathogenesis of cervical cancer, a miRNA-mRNA interaction network on selected pathways was built by integrating viral oncoproteins, dysregulated miRNAs and their predicted/validated targets. The results indicated that the deregulated miRNAs at the different stages of cervical cancer were functionally involved in several key cancer related pathways, such as cell cycle, p53 and Wnt signaling pathways. These dysregulated miRNAs could play an important role in cervical cancer development. Some of the stage-specific miRNAs can also be used as biomarkers for cancer classification and monitoring the progression of cancer development.

Estradiol mediates vasculoprotection via ERRα-dependent regulation of lipid and ROS metabolism in the endothelium.

The estrogen-mediated vasculoprotective effect has been widely reported in many animal studies, although the clinical trials are controversial and the detailed mechanisms remain unclear. In this study, we focused on the molecular mechanism and consequence of 17ß-estradiol (E2)-induced ERRα (estrogen-related receptor alpha) expression in endothelium and its potential beneficial effects on vascular function. The human aorta endothelial cells were used to identify the detailed molecular mechanism and consequences for E2-induced ERRα expression through estrogen receptors (ER), where ERα responses E2-induced ERRα activation, and ERß responses basal ERRα expression. E2-induced ERRα expression increases fatty acid uptake/oxidation with increased mitochondrial replication, ATP generation and attenuated reactive oxygen species (ROS) formation. We have obtained further in vivo proof from high-fat diet mice that the lentivirus-carried endothelium-specific delivery of ERRα expression on the vascular wall normalizes E2 deficiency-induced increased plasma lipids with ameliorated vascular damage. ERRα knockdown worsens the problem, and the E2 could only partly restore this effect. This is the first time we report the detailed mechanism with direct evidence that E2-induced ERRα expression modulates the fatty acid metabolism and reduces the circulating lipids through endothelium. We conclude that E2-induced ERRα expression in endothelium plays an important role for the E2-induced vasculoprotective effect.

Current State of Circulating MicroRNAs as Cancer Biomarkers.

BACKGROUND: Numerous studies have demonstrated the existence of stable regulatory RNAs, microRNAs (miRNAs), in the circulation and have shown that the spectrum of these extracellular miRNAs is affected by various pathologic conditions including cancers. CONTENT: Circulating miRNAs have been the focus of numerous cancer biomarker discovery efforts over the past few years; however, a considerable number of these studies have yielded inconsistent and irreproducible findings. Here, we have summarized and compared the results of studies covering 8 different cancer types to address key questions, including the possibility of using circulating miRNA to detect cancers and what factors may affect miRNA signatures. Although identifying circulating miRNA signatures to detect specific types of early stage cancers can be challenging, study results suggest that it may be possible to use miRNAs to detect cancers in general. SUMMARY: Circulating miRNA is a rich source for potential disease biomarkers; however, factors, both intrinsic and extrinsic, that may affect measurement of circulating miRNA have not been fully characterized. Better understanding of intra- and intercellular miRNA trafficking and the fundamental biology of cancer cell-derived lipid vesicles may facilitate the development of circulating miRNA-based biomarkers for cancer detection and classification.

Association between vitamin C in serum and trouble sleeping based on NHANES 2017-2018.

Vitamin C is an important micronutrient for human. Association between vitamin C and trouble sleeping was less studied. Therefore, the purpose of this study was to investigate the possible link between vitamin C in serum and trouble sleeping. The cross-sectional data was derived from the National Health and Nutrition Examination Survey (NHANES, 2017-2018). Trouble sleeping was measured by asking participants: "Have you ever told doctor had trouble sleeping". Responses to this question was "yes" or "no". vitamin C in serum was obtained by measuring the serum samples. We used multivariable binary logistic regressions to examine the possible link between vitamin C in serum and trouble sleeping, and then a subgroup analysis was performed. Moreover, the non-linear relationship between vitamin C in serum and trouble sleeping was further detected using a restricted cubic spline (RCS) model. A total of 3227 participants were included in the study. After adjusting all potential confounders, the results of multivariable logistic regression showed the significant negative association between vitamin C in serum and trouble sleeping(OR = 0.816; 95% CI:0.669 ~ 0.995). The significant inverse association was also found in female(OR = 0.713; 95% CI:0.546 ~ 0.931), age ≤ 65 years(OR = 0.773; 95% CI:0.600 ~ 0.996), and in participants with high cholesterol level(OR = 0.738; 95% CI:0.548 ~ 0.994). In addition, the RCS model demonstrated the significant non-linear relationship between vitamin C in serum and trouble sleeping (P value of nonlinear = 0.010). Our study demonstrates the significant negative association between vitamin C in serum and trouble sleeping.

Associations between dietary inflammatory index and stroke risk: based on NHANES 2005-2018.

The dietary inflammatory index (DII) is a measure of the inflammatory potential of the diet and is closely associated with insulin resistance (IR) and stroke. And IR may play an important role in the development of stroke. Therefore, this study aimed to evaluate the relationship between DII and stroke risk while delving into the potential role of IR in this association. We analyzed data from the National Health and Nutrition Examination Survey (NHANES) from 2005 to 2018, performing weighted univariate analyses, logistic regression, and mediation analyses. At baseline, 3.89% of participants developed stroke, and we observed stroke patients exhibited higher DII scores. After adjusting for covariates, compared to participants in the first quartile of DII scores, those in the third quartile and fourth quartile had increased odds of experiencing a stroke (OR: 1.78, 95% CI: 1.18-2.68) and (OR: 1.70, 95% CI: 1.16-2.50), respectively. Moreover, a significant dose-response relationship was observed (P-trend < 0.05). However, there was no observed interaction between DII and homeostatic model assessment-IR (HOMA-IR) concerning stroke risk, and HOMA-IR did not mediate the association between DII and stroke. In summary, our study elucidated the significant association between DII and stroke risk, independent of IR. This insight suggests that an anti-inflammatory diet may serve as an effective strategy for stroke prevention.

Role of remnant cholesterol in the relationship between physical activity and diabetes mellitus: an intermediary analysis.

Objective: The 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. Methods: The 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. Results: The 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. Discussion: Our 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.

The association between dietary inflammation index and depression.

Objective: We aimed to evaluate whether depression is associated with increased risk of dietary inflammatory index (DII) or energy-adjusted DII (E-DII) and whether the association is partly explained by insulin resistance (IR). Methods: Base on the National Health and Nutrition Examination Survey (NHANES) 2005-2018. Univariate analyses of continuous and categorical variables were performed using t-test, ANOVA, and χ 2 test, respectively. Logistic regression was used to analyze the relationship between DII or E-DII and depression in three different models. Mediation analysis was used to assess the potential mediation effects of homeostatic model assessment-IR (HOMA-IR). Results: A total of 70,190 participants were included, and the DII score was higher in the depressed group. DII score was related to all participant characteristics except age (p < 0.05). After being included in covariates (Model 3), participants in the highest quartile of DII score have increased odds of depression (OR: 1.82, 95% CI: 1.28-2.58) compared with those in the first quartile of DII score. And, a significant dose-response relationship was found (p-trend <0.05). No interaction between DII and HOMA-IR was observed in terms of the risk of depression, and HOMA-IR did not find to play a mediating role in the association between DII and depression. Similar results were obtained for the association between E-DII and depression. Conclusion: Our results suggest that a higher pro-inflammatory diet increases the risk of depression in U.S. adults, while there was no evidence of a multiplicative effect of DII or E-DII and HOMA-IR on disease risk, nor of a mediating effect of HOMA-IR.

Comparative study on risk prediction model of type 2 diabetes based on machine learning theory: a cross-sectional study.

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.

Genetically predicted lifestyle factors, socioeconomic status and risk of coronary artery disease in individuals with diabetes: a Mendelian randomization study.

Background: This study explores the causal links between genetically predicted lifestyle factors, socioeconomic status, and coronary artery disease (CAD) risk in individuals with diabetes using a bidirectional Mendelian-randomization approach. Methods: This study explored the potential causal relationships of lifestyle factors and socioeconomic status with the risk of CAD in diabetes patients by a bidirectional, two-sample Mendelian-randomization (MR) analysis. Results: Genetically predicted smoking initiation (p = 0.005, 95% CI: 1.08-1.55) and insomnia (p = 0.001, 95% CI: 1.06-1.29) were associated with a higher risk of CAD in individuals with diabetes, whereas educational attainment (p = 0.0001, 95% CI: 0.47-0.78) was associated with a lower risk of CAD. The lifetime smoking index (p = 0.016, 95% CI: 1.12-3.03) was suggestively associated with a higher risk of CAD, while household income before taxes (p = 0.048, 95% CI: 0.41-1.00) was suggestively associated with a lower risk of CAD. In addition, we observed a suggestive negative association between the genetically predicted risk of CAD and the lifetime smoking index (p = 0.016, 95% CI: 0.98-0.99) and a significant causal relationship between the risk of CAD and household income before taxes (p = 0.006, 95% CI: 0.97-0.99). Conclusion: The results of this study provide evidence that smoking initiation, lifetime smoking index and insomnia are associated with an increased risk of CAD in individuals with diabetes, educational attainment and household income before taxes are associated with a reduced risk of CAD in individuals with diabetes, and the possible role of lifetime smoking index and household income before taxes on the risk of CAD in individuals with diabetes. It provides an opportunity for the prevention and management of CAD in individuals with diabetes.

Association between systemic immune-inflammatory index and diabetes mellitus: mediation analysis involving obesity indicators in the NHANES.

Background: Inflammation and obesity have been widely recognized to play a key role in Diabetes mellitus (DM), and there exists a complex interplay between them. We aimed to clarify the relationship between inflammation and DM, as well as the mediating role of obesity in the relationship. Methods: Based on the National Health and Nutrition Examination Survey (NHANES) 2005-2018. Univariate analyses of continuous and categorical variables were performed using t-test, linear regression, and χ2 test, respectively. Logistic regression was used to analyze the relationship between Systemic Immune-Inflammatory Index (SII) or natural logarithm (Ln)-SII and DM in three different models. Mediation analysis was used to determine whether four obesity indicators, including body mass index (BMI), waist circumference (WC), visceral adiposity index (VAI) and lipid accumulation product index (LAP), mediated the relationship between SII and DM. Results: A total of 9,301 participants were included, and the levels of SII and obesity indicators (BMI, WC, LAP, and VAI) were higher in individuals with DM (p < 0.001). In all three models, SII and Ln-SII demonstrated a positive correlation with the risk of DM and a significant dose-response relationship was found (p-trend <0.05). Furthermore, BMI and WC were associated with SII and the risk of DM in all three models (p < 0.001). Mediation analysis showed that BMI and WC mediated the relationship between SII with DM, as well as Ln-SII and DM, with respective mediation proportions of 9.34% and 12.14% for SII and 10.23% and 13.67% for Ln-SII (p < 0.001). Conclusion: Our findings suggest that increased SII levels were associated with a higher risk of DM, and BMI and WC played a critical mediating role in the relationship between SII and DM.

Development and validation of a simplified chinese version of EORTC QLQ-CR38 to measure the quality of life of patients with colorectal cancer.

OBJECTIVES: This study aimed to develop and validate the simplified Chinese version of the EORTC Quality of Life Questionnaire for Colorectal Cancer, QLQ-CR38. METHODS: It was developed by the strict translation procedure of EORTC translation guidelines, including translation, back translation, a pilot test and a cultural adaptation. Three instruments (QLQ-CR38, Quality of Life Instruments for Cancer Patients-Colorectal Cancer, and Functional Assessment of Cancer Therapy-Colorectal) were used on a sample of 110 patients with colorectal cancer at the time of their admission to the hospital and 1-2 days after hospitalization, and a subsample of patients was measured a third time at discharge. The psychometric properties were evaluated by correlational analysis, multi-trait scaling analysis, factor analysis, and t tests. RESULTS: Multi-trait scaling analysis and correlation analysis among domains and items of the three instruments showed good construct validity and criterion-related validity. Cronbach's α and the test-retest reliability coefficients were greater than 0.70 for all domains (except chemotherapy side effects, 0.44 and 0.60, respectively). All domains had statistically significant changes after treatment with the standardized response mean ranging from 0.44 to 2.47. CONCLUSIONS: The simplified Chinese version of QLQ-CR38 has good validity, reliability, and responsiveness and can be used to measure the quality of life of Chinese patients with colorectal cancer.

Application of back propagation neural network model optimized by particle swarm algorithm in predicting the risk of hypertension.

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.

Ginsenoside Rc attenuates DSS-induced ulcerative colitis, intestinal inflammatory, and barrier function by activating the farnesoid X receptor.

Objectives: Farnesoid X receptor (FXR) activation is involved in ameliorating inflammatory bowel disease (IBD), such as ulcerative colitis (UC), and inflammatory regulation may be involved in its mechanism. Ginsenoside Rc (Rc) is a major component of Panax ginseng, and it plays an excellent role in the anti-inflammatory processes. Our aim is to explore the alleviative effect of Rc on dextran sulfate sodium (DSS)-induced inflammation and deficiencies in barrier function based on FXR signaling. Materials and Methods: In vitro, we treated human intestinal epithelial cell lines (LS174T) with LPS to explore the anti-inflammatory effect of Rc supplementation. In vivo, a DSS-induced IBD mice model was established, and the changes in inflammatory and barrier function in colons after Rc treatment were measured using the disease activity index (DAI), hematoxylin and eosin (H&E) staining, immunofluorescence, ELISA, and qPCR. Molecular docking analysis, luciferase reporter gene assay, and qPCR were then used to analyze the binding targets of Rc. DSS-induced FXR-knockout (FXR-/-) mice were used for further validation. Results: Rc significantly recovered the abnormal levels of inflammation indexes (TNF-α, IL-6, IL-1ß, and NF-KB) induced by LPS in LS174T. DSS-induced C57BL/6 mice exhibited a significantly decreased body weight and elevated DAI, as well as a decrease in colon weight and length. Increased inflammatory markers (TNF-α, IL-6, IL-1ß, ICAM1, NF-KB, F4/80, and CD11b displayed an increased expression) and damaged barrier function (Claudin-1, occludin, and ZO-1 displayed a decreased expression) were observed in DSS-induced C57BL/6 mice. Nevertheless, supplementation with Rc mitigated the increased inflammatory and damaged barrier function associated with DSS. Further evaluation revealed an activation of FXR signaling in Rc-treated LS174T, with FXR, BSEP, and SHP found to be upregulated. Furthermore, molecular docking indicated that there is a clear interaction between Rc and FXR, while Rc activated transcriptional expression of FXR in luciferase reporter gene assay. However, these reversal abilities of Rc were not observed in DSS-induced FXR-/- mice. Conclusion: Our findings suggest that Rc may ameliorate inflammation and barrier function in the intestine, which in turn leads to the attenuation of DSS-induced UC, in which Rc may potentially activate FXR signaling to protect the intestines from DSS-induced injury.

ATL I, Acts as a SIRT6 Activator to Alleviate Hepatic Steatosis in Mice via Suppression of NLRP3 Inflammasome Formation.

Accumulating evidence has highlighted that sirtuin-6 (SIRT6) plays an important role in hepatic gluconeogenesis and lipogenesis. We aim to investigate the underlying mechanisms and pharmacological interventions of SIRT6 on hepatic steatosis treatment. Herein, our results showed that atractylenolide I (ATL I) activated the deacetylase activity of SIRT6 to promote peroxisome proliferator-activated receptor alpha (PPARα) transcription and translation, while suppressing nuclear factor NF-kappa-B (NFκB)-induced NACHT, LRR, and PYD domains containing protein 3 (NLRP3) inflammasome formation. Together, these decreased the infiltration of F4/80 and CD11B positive macrophages, accompanied by decreased mRNA expression and serum levels of tumor necrosis factor alpha (TNF-α), interleukin-6 (IL6), and interleukin-1 beta (IL1ß). Additionally, these changes decreased sterol regulatory element-binding protein-1c (SREBP-1c) expression, while restoring carnitine O-palmitoyltransferase 1a (Cpt1a) expression, to decrease the size of adipocytes and adipose deposition, which, in turn, reversed high-fat diet (HFD)-induced liver weight and body weight accumulation in C57 mice. SIRT6 knockout or hepatic SIRT6 knockout in C57 mice largely abolished the effect of ATL I on ameliorating hepatic steatosis. Taken together, our results suggest that ATL I acts as a promising compound that activates SIRT6/PPARα signaling and attenuates the NLRP3 inflammasome to ameliorate hepatic inflammation and steatosis.

Association between Single Nucleotide Polymorphism rs9891119 of STAT3 Gene and the Genetic Susceptibility to Type 2 Diabetes in Chinese Han Population from Guangdong.

Background: The aim of this study was to investigate the association between single nucleotide polymorphism (SNP) rs9891119 of the signal transducer and activator of the transcription 3 (STAT3) gene and genetic susceptibility to type 2 diabetes in Chinese Han population from the Guangdong province. Objective: The aim of the present study was to explore the relationship between single nucleotide polymorphism rs9891119 of STAT3 gene and type 2 diabetes mellitus (T2DM), which provides a basis for molecular genetic research on the pathogenesis of T2DM in Chinese Han population. Methods: In our case-control study, the SNP rs9891119 was picked out from the STAT3 gene and the SNP genotyping was performed by using the SNPscan™ kit in 1092 patients with type 2 diabetes as cases and 1092 normal persons as controls. The distributions of genotype and allele frequencies in two groups were analyzed by SPSS 20.0 software. Results: Our results showed that the alleles of A and C of rs9891119 of the STAT3 gene were 54.3 and 45.7% in patients with type 2 diabetes, while 55.5% and 44.5% in the normal persons, which have no statistical significance (P > 0.05). There were also no significant differences in AA, AC, and CC genotype frequencies between type 2 diabetes patients and normal persons. There were no significant differences in codominant, dominant, recessive, and overdominant genetic models of SNP rs9891119 before and after adjusting the covariant factors (P > 0.05). Conclusions: Therefore, genetic susceptibility to type 2 diabetes may be not associated with SNP rs9891119 of the STAT3 gene in Chinese Han population from the Guangdong province.