A feature optimization study based on a diabetes risk questionnaire.

Introduction: The prevalence of diabetes, a common chronic disease, has shown a gradual increase, posing substantial burdens on both society and individuals. In order to enhance the effectiveness of diabetes risk prediction questionnaires, optimize the selection of characteristic variables, and raise awareness of diabetes risk among residents, this study utilizes survey data obtained from the risk factor monitoring system of the Centers for Disease Control and Prevention in the United States. Methods: Following univariate analysis and meticulous screening, a more refined dataset was constructed. This dataset underwent preprocessing steps, including data distribution standardization, the application of the Synthetic Minority Oversampling Technique (SMOTE) in combination with the Round function for equilibration, and data standardization. Subsequently, machine learning (ML) techniques were employed, utilizing enumerated feature variables to evaluate the strength of the correlation among diabetes risk factors. Results: The research findings effectively delineated the ranking of characteristic variables that significantly influence the risk of diabetes. Obesity emerges as the most impactful factor, overshadowing other risk factors. Additionally, psychological factors, advanced age, high cholesterol, high blood pressure, alcohol abuse, coronary heart disease or myocardial infarction, mobility difficulties, and low family income exhibit correlations with diabetes risk to varying degrees. Discussion: The experimental data in this study illustrate that, while maintaining comparable accuracy, optimization of questionnaire variables and the number of questions can significantly enhance efficiency for subsequent follow-up and precise diabetes prevention. Moreover, the research methods employed in this study offer valuable insights into studying the risk correlation of other diseases, while the research results contribute to heightened societal awareness of populations at elevated risk of diabetes.

Diabetes risk prediction model based on community follow-up data using machine learning.

Diabetes is a chronic metabolic disease characterized by hyperglycemia, the follow-up management of diabetes patients is mostly in the community, but the relationship between key lifestyle indicators in community follow-up and the risk of diabetes is unclear. In order to explore the association between key life characteristic indicators of community follow-up and the risk of diabetes, 252,176 follow-up records of people with diabetes patients from 2016 to 2023 were obtained from Haizhu District, Guangzhou. According to the follow-up data, the key life characteristic indicators that affect diabetes are determined, and the optimal feature subset is obtained through feature selection technology to accurately assess the risk of diabetes. A diabetes risk assessment model based on a random forest classifier was designed, which used optimal feature parameter selection and algorithm model comparison, with an accuracy of 91.24% and an AUC corresponding to the ROC curve of 97%. In order to improve the applicability of the model in clinical and real life, a diabetes risk score card was designed and tested using the original data, the accuracy was 95.15%, and the model reliability was high. The diabetes risk prediction model based on community follow-up big data mining can be used for large-scale risk screening and early warning by community doctors based on patient follow-up data, further promoting diabetes prevention and control strategies, and can also be used for wearable devices or intelligent biosensors for individual patient self examination, in order to improve lifestyle and reduce risk factor levels.

Distilling experience into a physically interpretable recommender system for computational model selection.

Model selection is a chronic issue in computational science. The conventional approach relies heavily on human experience. However, gaining experience takes years and is severely inefficient. To address this issue, we distill human experience into a recommender system. A trained recommender system tells whether a computational model does well or poorly in handling a physical process. It also tells if a physical process is important for a quantity of interest. By accumulating this knowledge, the system is able to make recommendations about computational models. We showcase the power of the system by considering Reynolds-averaged-Navier-Stokes (RANS) model selection in the field of computational fluid dynamics (CFD). Since turbulence is stochastic, there is no universal RANS model, and RANS model selection has always been an issue. A working model recommending system saves fluid engineers years and allows junior CFD practitioners to make sensible model choices like senior ones.

Risk factors for venous thromboembolism following surgical treatment of fractures: A systematic review and meta-analysis.

This study aimed to determine the risk factors for postoperative venous thromboembolism (VTE) in patients treated surgically for fractures using a meta-analytic approach. Electronic searches were performed in PubMed, Embase, and the Cochrane library from inception until February 2022. The odds ratio (OR) and 95% confidence interval (CI) were applied to calculate the pooled effect estimate using the random-effects model. Sensitivity, subgroup, and publication bias tests were also performed. Forty-four studies involving 3 239 291 patients and reporting 11 768 VTE cases were selected for the meta-analysis. We found that elderly (OR: 1.72; 95% CI: 1.38-2.15; P < .001), American Society of Anesthesiologists (ASA) ≥ 3 (OR: 1.82; 95% CI: 1.46-2.29; P < .001), blood transfusion (OR: 1.82; 95% CI: 1.14-2.92; P = .013), cardiovascular disease (CVD) (OR: 1.40; 95% CI: 1.22-1.61; P < .001), elevated D-dimer (OR: 4.55; 95% CI: 2.08-9.98; P < .001), diabetes mellitus (DM) (OR: 1.36; 95% CI: 1.19-1.54; P < .001), hypertension (OR: 1.31; 95% CI: 1.09-1.56; P = .003), immobility (OR: 3.45; 95% CI: 2.23-5.32; P < .001), lung disease (LD) (OR: 2.40; 95% CI: 1.29-4.47; P = .006), obesity (OR: 1.52; 95% CI: 1.27-1.82; P < .001), peripheral artery disease (PAD) (OR: 2.13; 95% CI: 1.21-3.73; P = .008), prior thromboembolic event (PTE) (OR: 5.17; 95% CI: 3.14-8.50; P < .001), and steroid use (OR: 2.37; 95% CI: 1.73-3.24; P < .001) were associated with an increased risk of VTE. Additionally, regional anaesthesia (OR: 0.66; 95% CI: 0.45-0.96; P = .029) was associated with a reduced risk of VTE following surgical treatment of fractures. However, alcohol intake, cancer, current smoking, deep surgical site infection, fusion surgery, heart failure, hypercholesterolemia, liver and kidney disease, sex, open fracture, operative time, preoperative anticoagulant use, rheumatoid arthritis, and stroke were not associated with the risk of VTE. Post-surgical risk factors for VTE include elderly, ASA ≥ 3, blood transfusion, CVD, elevated D-dimer, DM, hypertension, immobility, LD, obesity, PAD, PTE, and steroid use.

Equivalence of three thermal boundary conditions in compressible turbulent channel flows.

In this paper, direct numerical simulations have been performed to explore the equivalence of different thermal boundary conditions in compressible turbulent channel flows at fixed Re=6000,Ma=1.5. Three canonical types of thermal boundary conditions will be investigated at almost equivalent setups, including the first boundary condition with fixed wall temperature T_{w} (constant Dirichlet boundary condition), the second boundary condition with fixed wall heat-flux q_{w} (constant Neumann boundary condition), and the third boundary condition (Robin boundary condition). The turbulent statistics of the temperature and velocity fields, including mean profiles, root-mean-square values, second-order statistics, and normalized probability density functions, temperature stripes near the wall and the budget of internal energy have been analyzed in detail to clarify the differences caused by the different thermal boundary conditions. The results show that the three thermal boundary conditions have almost negligible effect on the velocity field, whereas some discernible deviations can be observed for the temperature field in the near-wall region with y^{+}≲30. Furthermore, the statistics from the second and third thermal boundary conditions are very close, enabling the usage of the second boundary condition to mimic the more complex third boundary conditions.

Febuxostat combined with hydration for the prevention of contrast-induced nephropathy in hyperuricemia patients undergoing percutaneous coronary intervention: A CONSORT-compliant randomized controlled trial.

BACKGROUND: To assess the efficacy of febuxostat combined with hydration on contrast-induced nephropathy (CIN) in coronary heart disease patients with hyperuricemia undergoing percutaneous coronary intervention (PCI). METHODS: Patients with hyperuricemia who underwent PCI were randomly assigned to 2 groups. The control group was given hydration only, and the febuxostat group received febuxostat 40 mg daily before administration of contrast agent and hydration. The primary endpoint of the study was the incidence of CIN, defined as an increase in baseline serum creatinine concentration by 25% at 2 days after contrast media administration, and variations in the serum levels of creatinine, neutrophil gelatinase-associated lipocalin, uric acid, and estimated glomerular filtration rate were compared. RESULTS: A total of 202 patients with hyperuricemia were randomly assigned to either the febuxostat group (n = 100) or the control group (n = 102). The baseline characteristics of the 2 groups were similar. The incidence of CIN was 6.0% (6/100) in the febuxostat group and 14.71% (15/102) in the control group.The levels of neutrophil gelatinase-associated lipocalin at 6-hour and serum creatinine and uric acid at 48-hour in the febuxostat combined hydration group were lower than those in the control group after surgery, and the level of estimated glomerular filtration rate was higher than that in the control group (all P < .05). Multivariate logistic regression analysis revealed that febuxostat was an independent predictor of CIN. CONCLUSION: Our study demonstrated that prophylactic treatment with febuxostat combined with hydration can reduce the incidence of CIN in patients with coronary heart disease and hyperuricemia after PCI.

To disclose or to falsify: The effects of cognitive trust and affective trust on customer cooperation in contact tracing.

Contact tracing involves collecting people's information to track the spread of COVID-19 and to warn people who have been in the proximity of infected individuals. This measure is important to public health and safety during the pandemic. However, customers' concerns about the violation of their privacy might inhibit their cooperation in the contact tracing process, which poses a risk to public safety. This research investigates how to facilitate customers' cooperative behavior in contact tracing based on cognitive trust and affective trust. The findings show that cognitive trust increases people's willingness to disclose information and reduces their willingness to falsify it, whereas affective trust increases the willingness for both disclosure and falsification. This research contributes to the literature on customer data privacy by illuminating how cognitive and affective trust distinctly influence cooperative behavior, which has important implications for hospitality businesses.

Contribution of viscous stress work to wall heat flux in compressible turbulent channel flows.

In this paper we derive an exact expression for the mean heat flux at the wall for the compressible turbulent channel flow. In the expression, the heat flux at the wall can be decomposed into four parts, including the contribution from the turbulent heat transfer, the contribution from the molecular heat transfer, the contribution from the pressure work, and the contribution from the work from viscous stress (VW). The decomposition is validated in compressible turbulent channel flows with isothermal walls at three different Reynolds and Mach numbers, and the results match very well with the direct estimations at the wall. The data also show that the VW term dominates the contributions (around 90%) in the decomposition while around 90% of VW is from the near-wall region (y/h<0.2), illustrating the importance of the viscous stress work in the near-wall region.

Mortality trends of bladder cancer in China from 1991 to 2015: an age-period-cohort analysis.

PURPOSE: The effects of age, period, and cohort on mortality rates of bladder cancer in China remained vague. This study aimed to analyze the secular trends of bladder cancer mortality in China and estimate the independent effects of age, period, and cohort. METHODS: Data for bladder cancer mortality from 1991 to 2015 was obtained from the WHO Mortality Database and China Health Statistical Yearbook. The age-period-cohort model was used to estimate the effect of age, period, and cohort. The intrinsic estimator method was used to solve the nonidentification problem of collinearity among age, period, and cohort. RESULTS: The age-standardized mortality rates of total residents (2.33-1.87/100,000), male (3.45-2.89/100,000), and female (1.24-0.82/100,000) showed decreasing trends, which was more obvious in males than in females. Age effects increased consistently with age in all age groups (coefficients: -2.02 to 1.91 in the total population, -2.06 to 2.02 in males and -2.04 to 1.81 in females). Cohort effects decreased overall (coefficients: 0.96 to -1.62 in the total population, 1.11 to -1.66 in males and 0.78 to -1.46 in females). Period effects were not found in China. CONCLUSION: Although a decreasing mortality was observed, the bladder cancer burden in China will likely increase in the next few years due to population aging, environmental pollution, and food safety. The findings suggested that preventive measures should be taken corresponding to the changes in age-and cohort-related factors in the population.

Triglycerides to high-density lipoprotein cholesterol ratio as a surrogate for nonalcoholic fatty liver disease: a cross-sectional study.

BACKGROUND: Triglycerides (TG) to high-density lipoprotein cholesterol (HDL-C) ratio (TG/HDL-C) has been recommended as a surrogate marker for insulin resistance. In the present study, we aimed to investigate the relationship between TG/HDL-C and NAFLD in an apparently healthy population. METHODS: A total of 18,061 subjects who participated in a health checkup program were included. NAFLD was diagnosed by ultrasonography. RESULTS: The prevalence rate of NAFLD was 24.8% in the whole population, and progressively increased across the quartiles of TG/HDL-C (4.9, 14.1, 26.8 and 53.5%, respectively, P <  0.001). After adjustment for confounding factors, TG/HDL-C was independently associated with the risk of NAFLD. Compared with the first quartile of TG/HDL-C (Q1), the odds ratios (95% confidence intervals) for NAFLD in the increasing quartiles (Q2-Q4) were 2.1(1.8-2.6), 3.6 (3.0-4.3) and 9.2(7.6-11.1), respectively. In addition, the area under receiver operator characteristic curve (95% confidence interval) of TG/HDL-C for NAFLD was 0.85 (0.84-0.86) in women and 0.79 (0.78-0.80) in men, significantly higher than that of TG, TC, LDL-C, HDL-C, ALT and AST (P <  0.05). The optimal cutoff point of TG/HDL-C for detection of NAFLD was 0.9 in women (sensitivity = 78.8%, specificity = 77.3%) and 1.4 in men (sensitivity = 70.7%, specificity = 73.5%). CONCLUSIONS: TG/HDL-C is independently associated with NAFLD in apparently healthy individuals and may be used as a surrogate for NAFLD.

Multivariate analysis of genome-wide data to identify potential pleiotropic genes for five major psychiatric disorders using MetaCCA.

BACKGROUND: Genome-wide association studies have been extensively applied in identifying SNP associated with major psychiatric disorders. However, the SNPs identified by the prevailing univariate approach only explain a small percentage of the genetic variance of traits, and the extensive data have shown the major psychiatric disorders have common biological mechanisms and the overlapping pathophysiological pathways. METHODS: We applied the genetic pleiotropy-informed metaCCA method on summary statistics data from the Psychiatric Genomics Consortium Cross-Disorder Group to examine the overlapping genetic relations between the five major psychiatric disorders. Furthermore, to refine all genes, we performed gene-based association analyses for the five disorders respectively using VEGAS2. Gene enrichment analysis was applied to explore the potential functional significance of the identified genes. RESULTS: After metaCCA analysis, 1147 SNPs reached the Bonferroni corrected threshold (p < 1.06 × 10-6) in the univariate SNP-multivariate phenotype analysis, and 246 genes with a significance threshold (p < 3.85 × 10-6) were identified as potentially pleiotropic genes in the multivariate SNP-multivariate phenotype analysis. By screening the results of gene-based p-values, we identified 37 putative pleiotropic genes which achieved significance threshold in metaCCA analyses and were also associated with at least one disorder in the VEGAS2 analyses. LIMITATIONS: Alternative approaches and experimental studies may be applied to check whether novel genes could still be identified/substantiated with these methods. CONCLUSIONS: The metaCCA method identified novel variants associated with psychiatric disorders by effectively incorporating information from different GWAS datasets. Our analyses may provide insights for some common therapeutic approaches of these five major psychiatric disorders based on the pleiotropic genes and common mechanisms identified.

Multivariate analysis of genome-wide data to identify potential pleiotropic genes for type 2 diabetes, obesity and coronary artery disease using MetaCCA.

BACKGROUND: Although genome-wide association studies (GWAS) have been extensively applied in identifying SNP associated with metabolic diseases, the SNPs identified by this prevailing univariate approach only explain a small percentage of the genetic variance of traits. The extensive previous studies have repeatedly shown type2 diabetes (T2D), obesity and coronary artery disease (CAD) have common genetic mechanisms and the overlapping pathophysiological pathways. METHODS: The genetic pleiotropy-informed metaCCA method was applied on summary statistics data from three independent meta-GWAS summary statistics to identify shared variants and pleiotropic effect between T2D, obesity and CAD. Furthermore, to refine all genes, we performed gene-based association analyses for these three diseases respectively using VEGAS2. Gene enrichment analysis was applied to explore the potential functional significance of the identified genes. RESULTS: After metaCCA analysis, 833 SNPs reached the Bonferroni corrected threshold (p < 7.99 × 10-7) in the univariate SNP-multivariate phenotype analysis, and 327 genes with a significance threshold (p < 3.73 × 10-6) were identified as potentially pleiotropic genes in the multivariate SNP-multivariate phenotype analysis. By screening the results of gene-based p-values, we identified 22 putative pleiotropic genes which achieved significance threshold in metaCCA analyses and were also associated with at least one disease in the VEGAS2 analyses. CONCLUSIONS: The metaCCA method identified novel variants associated with T2D, obesity and CAD by effectively incorporating information from different GWAS datasets. Our analyses may provide insights for some common therapeutic approaches of metabolic diseases based on the pleiotropic genes and common mechanisms identified.

Remediation of cadmium, lead and zinc in contaminated soil with CETSA and MA/AA.

Soil washing, which is used to remove heavy metals from soil, is dependent on suitable washing agents. However, there is still a lack of economical, environmentally friendly washing agents with high removal efficiency. In this study, three washing agents, carboxyalkylthiosuccinic acid (CETSA), copolymer of maleic and acrylic acid (MA/AA) and ethylenediamine tetra acetic acid (EDTA), were used to remove heavy metals from contaminated soil. The influence of washing solution concentration, pH and washing time on heavy metals removal was also investigated. The cadmium (Cd), lead (Pb), and zinc (Zn) removal efficiencies increased as washing solution concentrations increased from 0 to 60 g L-1, while they declined as pH increased from 3 to 8. Despite fluctuations between 90 and 120 min, heavy metal removal efficiencies increased continuously from 10 to 90 min. The three agents also effectively reduced the potential risks of Cd, Pb, and Zn in contaminated soil, but only CETSA and MA/AA produced no significant changes in chemical properties. Fourier transform infrared spectroscopy revealed that the hydroxyl, carboxyl, carbonyl, methoxyl, and sulfur groups were related to the heavy metal ions from the soil colloids. Thus, CETSA and MA/AA were suitable washing agents for remediation of heavy metals contaminated soil.

Helicobacter pylori Infection Is Not Associated with Non-alcoholic Fatty Liver Disease: A Cross-Sectional Study in China.

Background and Aim:Helicobacter pylori infection has been reported to promote the development of a variety of extra-digestive manifestations, including type 2 diabetes, cardiovascular and liver diseases. Recently, the association between H. pylori infection and non-alcoholic fatty liver disease (NAFLD) was also proposed. However, evidence from different studies was controversial. We therefore performed this study to investigate the relationship between them in a large population of apparently healthy subjects in China. Methods: A total of 21,456 subjects underwent a healthy checkup program were included. H. pylori infection was detected by 14C urea breath test (14C-UBT) and NAFLD was diagnosed by ultrasonography. Results: Subjects infected with H. pylori had a more unfavorable metabolic profile, including higher levels of body mass index (BMI), blood pressure, triglycerides (TG) and lower levels of high-density lipoprotein cholesterol (HDL-C), as compared with those without H. pylori infection (all P < 0.05). Moreover, the prevalence rate of NAFLD was significantly increased in subjects with H. pylori infection when compared with those without H. pylori in women (23.6% vs. 21.5%, P < 0.05), but not in men (46.5% vs. 45.5%, P > 0.05). After adjusting for confounding factors including age, sex, BMI, blood pressure and lipid profiles, multivariate logistic analysis revealed that H. pylori infection was not independently associated with the risk of NAFLD in the total population (OR = 0.9, 95% CI = 0.9-1.0, P = 0.097). Also, subgroup analysis (stratified by age, sex, BMI, and diabetes status) showed no independent association between H. pylori infection and NAFLD. Conclusion: Our data suggests that H. pylori infection is not independently associated with the risk of NAFLD in apparently healthy subjects.

Sex-Specific Association between Serum Uric Acid and Nonalcoholic Fatty Liver Disease in Type 2 Diabetic Patients.

Across-sectional study was performed in 541 type 2 diabetic patients to determine the relationship between serum uric acid (SUA) and NAFLD in type 2 diabetic patients. Clinical parameters including SUA were determined and NAFLD was diagnosed by ultrasonography. SUA was significantly higher in type 2 diabetic subjects with NAFLD than in those without NAFLD in men, but not in women. Furthermore, the prevalence rate of NAFLD increased progressively across the sex-specific SUA tertiles only in men (37.9%, 58.6%, and 72.6%, resp., P for trend < 0.001). After adjusting for confounding factors, the odd ratios (95% CI) for NAFLD were 1 (reference), 2.93 (95%CI 1.25-6.88), and 3.93 (95% CI 1.55-9.98), respectively, across the tertiles of SUA in men. Contrastingly, SUA levels in women were not independently associated with the risk of NAFLD. Our data suggests that SUA is specifically associated with NAFLD in male type 2 diabetic subjects, independent of insulin resistance and other metabolic factors.

Midkine, a potential link between obesity and insulin resistance.

Obesity is associated with increased production of inflammatory mediators in adipose tissue, which contributes to chronic inflammation and insulin resistance. Midkine (MK) is a heparin-binding growth factor with potent proinflammatory activities. We aimed to test whether MK is associated with obesity and has a role in insulin resistance. It was found that MK was expressed in adipocytes and regulated by inflammatory modulators (TNF-α and rosiglitazone). In addition, a significant increase in MK levels was observed in adipose tissue of obese ob/ob mice as well as in serum of overweight/obese subjects when compared with their respective controls. In vitro studies further revealed that MK impaired insulin signaling in 3T3-L1 adipocytes, as indicated by reduced phosphorylation of Akt and IRS-1 and decreased translocation of glucose transporter 4 (GLUT4) to the plasma membrane in response to insulin stimulation. Moreover, MK activated the STAT3-suppressor of cytokine signaling 3 (SOCS3) pathway in adipocytes. Thus, MK is a novel adipocyte-secreted factor associated with obesity and inhibition of insulin signaling in adipocytes. It may provide a potential link between obesity and insulin resistance.

Follistatin-like 1: a potential mediator of inflammation in obesity.

Obesity is associated with a state of chronic low-grade inflammation, which contributes to insulin resistance and type 2 diabetes. However, the molecular mechanisms that link obesity to inflammation are not fully understood. Follistatin-like 1 (FSTL1) is a novel proinflammatory cytokine that is expressed in adipose tissue and secreted by preadipocytes/adipocytes. We aimed to test whether FSTL1 could have a role in obesity-induced inflammation and insulin resistance. It was found that FSTL1 expression was markedly decreased during differentiation of 3T3-L1 preadipocytes but reinduced by TNF-α. Furthermore, a significant increase in FSTL1 levels was observed in adipose tissue of obese ob/ob mice, as well as in serum of overweight/obese subjects. Mechanistic studies revealed that FSTL1 induced inflammatory responses in both 3T3-L1 adipocytes and RAW264.7 macrophages. The expression of proinflammatory mediators including IL-6, TNF-α, and MCP-1 was upregulated by recombinant FSTL1 in a dose-dependent manner, paralleled with activation of the IKKß-NFκB and JNK signaling pathways in the two cell lines. Moreover, FSTL1 impaired insulin signaling in 3T3-L1 adipocytes, as revealed by attenuated phosphorylation of both Akt and IRS-1 in response to insulin stimulation. Together, our results suggest that FSTL1 is a potential mediator of inflammation and insulin resistance in obesity.

Momentum-exchange method in lattice Boltzmann simulations of particle-fluid interactions.

The momentum exchange method has been widely used in lattice Boltzmann simulations for particle-fluid interactions. Although proved accurate for still walls, it will result in inaccurate particle dynamics without corrections. In this work, we reveal the physical cause of this problem and find that the initial momentum of the net mass transfer through boundaries in the moving-boundary treatment is not counted in the conventional momentum exchange method. A corrected momentum exchange method is then proposed by taking into account the initial momentum of the net mass transfer at each time step. The method is easy to implement with negligible extra computation cost. Direct numerical simulations of a single elliptical particle sedimentation are carried out to evaluate the accuracy for our method as well as other lattice Boltzmann-based methods by comparisons with the results of the finite element method. A shear flow test shows that our method is Galilean invariant.