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.

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.

Glypican-3 knockdown inhibits the cell growth, stemness, and glycolysis development of hepatocellular carcinoma cells under hypoxic microenvironment through lactylation.

CONTEXT: Hepatocellular carcinoma (HCC) is a common malignant tumour in China. Glypican-3 (GPC3) is reported to be closely related to the occurrence and development of various tumours. OBJECTIVE: This study aimed to explore the role of GPC3 in HCC. MATERIALS AND METHODS: The cell behaviours were investigated using Cell Counting Kit-8 (CCK-8), Traswell, and sphere formation assays. The protein and mRNA expression levels were detected using western blot and Real-Time Quantitative Polymerase Chain Reaction (RT-qPCR) assays. RESULTS: The results showed that GPC3 knockdown decreased the cell viability and stemness, glucose uptake, lactate production, and extracellular acidification rate (ECAR), while increased the oxygen consumption rate (OCR) in hypoxia-treated HCC cells. Additionally, GPC3 knockdown decreased the global lactylation and c-myc lactylation, which further decreased the protein stability and expressions of c-myc. DISCUSSION AND CONCLUSION: GPC3-mediated lactylation modification may be a new direction in HCC treatment in the future.

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.

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.

Consistency Analysis of 3 Detection Systems for Measuring Serum C-Reactive Protein.

BACKGROUND C-reactive protein (CRP) is an important clinical indicator. There are many methods and instruments for CRP measurement, and therefore the consistency of CRP values measured between instruments needs to be evaluated. This study aimed to compare the consistency of 3 serum CRP detection systems using turbidimetry. MATERIAL AND METHODS The consistency of CRP measured by 3 instruments, the Mindray BC-5390, Mindray BC-6800, and Johnson Vitros5600, was evaluated, and the consistency of blood routine measurement between the BC-5390 and BC-6800 was also evaluated. Pearson correlation analysis was used to evaluate the correlation of different instrument's test results (R, correlation coefficient). The consistency of instruments was assessed by Passing-Bablok analysis and weighted Deming analysis. RESULTS CRP data and route blood test data from 847 patients were used for analysis. The results showed that there were differences in the CRP values measured by the Mindray BC5390, Mindray BC6800, and Johnson Vitros5600 (χ²=78.573, P<0.001). The CRP measurement results of the BC5390 analyzer were consistent with those of the BC6800 analyzer (R=0.994, P<0.001) and Vitros5600 analyzer (R=0.983, P<0.001). However, there was a constant deviation in the CRP values measured by the BC-6800 and Vitros5600 analyzer (R=0.994, P<0.001). In the measurement of routine blood laboratory tests, the BC5390 analyzer and BC6800 analyzer were found to be interchangeable. CONCLUSIONS This study analyzed the consistency of CRP detection by 3 instruments, the Mindray BC-5390, Mindray BC-6800, and Johnson Vitros5600, and may provide a reference for the selection of CRP detection instruments.

Effects of the Chemical and Structural Properties of Silane Monolayers on the Organization of Water Molecules and Ions at Interfaces, from Molecular Dynamics Simulations.

Understanding the organization of the hydration layer at functionalized silica surfaces is relevant for a large range of biosensing applications or surface phenomena such as biomolecule adsorption. Silane monolayers are widely used to functionalize silica surfaces. Using molecular dynamics simulations, we have investigated the role of silane molecule head-group charge, alkyl chain length, and surface coverage in the structural organization and dynamic properties of Na+ ions, Cl- ions, and water molecules at the interface. The silane molecules studied are 3-aminopropyldimethylethoxysilane, n-propyldimethylmethoxysilane, octadecyldimethylmethoxysilane, and (dimethylamino)dimethylsilylundecanoate. Our results suggest that the distribution of interfacial ions is sensitive to the 2D dispersion of the silane-charged head groups. Also, while charged silane monolayers show a strong orientation of interfacial water molecules, which leads to a rupture in the hydrogen bond network and disturbs their tetrahedral organization, the arrangement of water molecules at the interface with uncharged silane monolayers seems to be related to the surface roughness and to alkyl chain length. In line with these results, the diffusion of ions and water molecules is higher at the CH3 long monolayer interface than at the CH3 short monolayer interface and at the charged monolayer interfaces. Also, whatever the silane molecules studied, bulk properties are recovered around 0.7 nm above the interface. The interfacial water organization is known to impact biomolecule adsorption. Therefore, these results could further help in optimizing the functionalization layers to capture analytes.

A photoactivatable antibody-Chlorin e6 conjugate enabling singlet oxygen production for tumor-targeting photodynamic therapy.

Photodynamic therapy is a new technology for disease diagnosis and treatment in modern medical clinics. The main advantages of photodynamic therapy are low toxicity and side effects, a wide range of applications, no drug resistance, and no obvious trauma in the treatment process. However, to achieve effective photodynamic therapy, new photosensitizer carriers need to be constructed, which can selectively deliver photosensitizers into tumor tissues. In this work, a photoactivatable antibody-Chlorin e6 conjugate with a dual-function to target tumor tissue and realize cancer photodynamic therapy is constructed. Bothin vitroandin vivoexperiments indicate that the antibody-Chlorin e6 conjugate has the ability to target tumors rapidly and efficiently, and has the ability to generate reactive oxygen species and kill tumor cells. Overall, this photoactivable antibody-Chlorin e6 conjugate may provide a promising strategy to address the current challenges of cancer photodynamic therapy.

Notch signaling is involved in the antiapoptotic effects of liraglutide on rat H9c2 cardiomyocytes exposed to hypoxia followed by reoxygenation.

OBJECTIVE: Liraglutide (Lir) protects cardiomyocytes against high glucose-induced myocardial damage. This study investigated whether Notch signaling participated in the antiapoptotic effects of Lir on rat H9c2 cardiomyocytes subjected to hypoxia followed by reoxygenation (H/R). METHODS: We used H9c2 rat cardiomyocytes as a model of H/R and measured viability, apoptosis, and expression of the apoptotic genes Bax and Bcl-2 and Notch signaling genes Notch1 and Jagged1. Notch1 was depleted by siRNA to test the effect of Notch1 deficiency on the antiapoptotic effects of Lir on H/R-treated H9c2 cardiomyocytes. RESULTS: After H/R treatment, viability was significantly decreased, and the apoptosis rate was greater in the H/R group than in the control (CT). Lir at 50, 100, and 200 nM significantly increased viability and decreased apoptosis in H/R-treated H9c2 cells. Treatment with 50 nM Lir for 2 hours before H/R significantly increased the expression levels of Notch1, Jagged1, and Bcl-2 compared with the CT levels. Bax was downregulated, which indicated that Lir activated Notch signaling and inhibited apoptosis. Notch1 depletion partially abolished the antiapoptotic effect of Lir on H/R-treated H9c2 cells by altering apoptotic gene expression. CONCLUSION: Lir activated Notch signaling, which was responsible for the antiapoptotic effect of Lir on H9c2 cardiomyocytes.

LINC01234 promotes multiple myeloma progression by regulating miR-124-3p/GRB2 axis.

LncRNA is gradually considered as a vital regulator in various physiological and pathological processes. Recently, the role of LINC01234 in several cancers has been reported. However, the function and underlying regulatory mechanism of LINC01234 in multiple myeloma (MM) remain unclear. Our results indicated that LINC01234 was over-expressed in tumor tissues of MM patients, and LINC01234 down-regulation remarkably inhibited cell proliferation, cycle progression and promoted cell apoptosis in MM cells. Mechanistic studies revealed that LINC01234 could sponge miR-124-3p and decreased its expression, thereby up-regulated the protein level of miR-124-3p's targets growth factor receptor-bound protein 2 (GRB2). Additionally, in vivo experiments revealed that LINC01234 shRNA could serve as a tumor suppressor through down-regulating GRB2 in MM. In this study, a novel established regulatory manner of LINC01234/miR-124-3p/GRB2 axis was systematically studied, which may hold promise as a promising target for MM treatment.

B-type natriuretic peptide attenuates endoplasmic reticulum stress in H9c2 cardiomyocytes underwent hypoxia/reoxygenation injury under high glucose/high fat conditions.

Exogenously administered B-type natriuretic peptide (BNP) has been shown to provide cardioprotection against various heart diseases. However, the underlying mechanisms remain elusive. This study explores whether BNP exerts its cardioprotection against hypoxia/reoxygenation (H/R) injury under high glucose/high fat (HG/HF) conditions in cardiac H9c2 cells and uncovers the underlying mechanisms. Our data revealed that BNP significantly increased the cell viability and decreased the release of lactate dehydrogenase (LDH) and creatine kinase (CK), with a maximal effect at the BNP concentration of 10-7 mol/L. In addition, by analyzing the activation of cleaved caspase-3 and by Annexin V-FITC/PI staining, we showed that BNP attenuated H/R-induced cell apoptosis in HG/HF conditions. Western blot analysis showed enhanced phosphorylation of protein kinase RNA (PKR)-like endoplastmic reticulum (ER) kinase (PERK) and eukaryotic initiation factor 2α (eIF2α)(one of the three main signaling pathways in endoplastmic reticulum (ER) stress), and increased expression of GRP78 and CHOP proteins (ER stress-related proteins) in H9c2 cells which underwent H/R in HG/HF conditions. Treatment with BNP or 8-Br-cGMP (an analog of cGMP) reversed this activation. However, this effect was significantly weakened by KT-5823, a selective cGMP-dependent protein kinase G (PKG) inhibitor. In addition, similar to BNP, treatment with a specific inhibitor of ER stress tauroursodeoxycholic acid (TUDCA) protected the cells against H/R injury exposed to HG/HF conditions. In conclusion, these findings demonstrated that BNP effectively protected cells against H/R injury under HG/HF conditions by inhibiting the ER stress via activation of the cGMP-PKG signaling pathway.

Aptamer selection and application in multivalent binding-based electrical impedance detection of inactivated H1N1 virus.

The type A influenza viruses are the most virulent and variable human pathogens with epidemic or even pandemic threat. The development of sensitive, specific and safe field testing methods is in particular need and quite challenging. We report here the selection and practical utilization of the inactivated influenza virus-specific aptamers. The DNA aptamers against inactivated intact H1N1 virus particles were identified through the systematic evolution of ligands by exponential enrichment (SELEX) procedure. The discriminative aptamers and their truncated sequences showed selectively high affinity to inactive H1N1 virus and H3N2 virus with the Kd in the low nanomolar range and collective binding properties. The truncated sequences were first applied in a sandwich enzyme-linked oligonucleotide assay (ELONA) with a H1N1 detection limit (LOD, S/N = 3) of 0.3 ng/µL and then in an electrochemical impedance (EIS) aptasensor with more than 300 times improved LOD (0.9 pg/µL) and the excellent selectivity over other viruses (> 100 times). Therefore the developed aptasensors represent the safer, simpler, and possibly better virus-variation adaptable means of virus diagnostics.

Concurrence of multiple human papillomavirus infections in a large US population-based cohort.

We examined the concurrence of multiple human papillomavirus (HPV) infections in 47,617 women who underwent cervical screening in New Mexico between December 2007 and April 2009 using the LINEAR ARRAY HPV Genotyping Test (Roche Diagnostics, Indianapolis, Indiana), which detects 37 different types of HPV. Our primary goal was to examine the distributions of multiple HPV types with a special interest in negative interactions, which could signal the possibility of type replacement associated with a common niche if some HPV types were prevented by vaccination. Multiple infections were found to be more common than expected under independence, but this could largely be accounted for by a woman-specific latent heterogeneity parameter which was found to be dependent on age and cytological grade. While multiple infections were more common in young women and in those with abnormal cytology, greater heterogeneity was seen in older women and in those with normal cytology, possibly reflecting greater variability in exposure due to current or past HPV exposure or due to heterogeneity in related HPV reactivation or in immune responses to HPV infection or persistence. A negative interaction was found between HPV 16 and several other HPV types for women with abnormal cytology but not for those with normal cytology, suggesting that type replacement in women vaccinated against HPV 16 is unlikely to be an issue for the general population.

Prediction of heterogeneous differential genes by detecting outliers to a Gaussian tight cluster.

BACKGROUND: Heterogeneously and differentially expressed genes (hDEG) are a common phenomenon due to bio-logical diversity. A hDEG is often observed in gene expression experiments (with two experimental conditions) where it is highly expressed in a few experimental samples, or in drug trial experiments for cancer studies with drug resistance heterogeneity among the disease group. These highly expressed samples are called outliers. Accurate detection of outliers among hDEGs is then desirable for dis- ease diagnosis and effective drug design. The standard approach for detecting hDEGs is to choose the appropriate subset of outliers to represent the experimental group. However, existing methods typically overlook hDEGs with very few outliers. RESULTS: We present in this paper a simple algorithm for detecting hDEGs by sequentially testing for potential outliers with respect to a tight cluster of non- outliers, among an ordered subset of the experimental samples. This avoids making any restrictive assumptions about how the outliers are distributed. We use simulated and real data to illustrate that the proposed algorithm achieves a good separation between the tight cluster of low expressions and the outliers for hDEGs. CONCLUSIONS: The proposed algorithm assesses each potential outlier in relation to the cluster of potential outliers without making explicit assumptions about the outlier distribution. Simulated examples and and breast cancer data sets are used to illustrate the suitability of the proposed algorithm for identifying hDEGs with small numbers of outliers.

A frailty model for interaction between multiple events.

Most models for categorical data rely on linear models in which higher order interactions are limited, usually to second order terms. Here we explore a dataset where second order interactions lead unavoidably to high order interactions. However in certain cases these interactions have a simple form which can be explained by population heterogeneity or 'frailty' and the Rasch model which uses a logistic structure is one such model. However its parameters are difficult to interpret. When event probabilities are low, we develop a simple model based on a multiplicative structure and a one-parameter gamma frailty model is developed to accommodate this sort of data. Moment estimators estimates are also provided for more general cases and goodness of fit statistics are given. An example involving 35 different human papillomavirus types in 33 614 women with normal cervical cytology smears (stratified by age) is explored.