Nonlinear Relationship Research Articles

Triglyceride-glucose index in non-obese individuals: its association with and predictive value for non-alcoholic fatty liver disease

To investigate the association of triglyceride-glucose index (TyG) with non-alcoholic fatty liver disease (NAFLD) and its diagnostic value for NAFLD in non-obese individuals. We retrospectively collected the data of non-obese individuals (BMI < 25 kg/m2) undergoing routine health examination at Second Affiliated Hospital of Xi'an Jiaotong University between May, 2020 and December, 2023, who all received abdominal ultrasound examination for NAFLD screening. The nonlinear relationship between TyG and non-obese NAFLD was explored using restricted cubic splines (RCS), and LASSO regression was used for variable screening; the correlation between TyG and NAFLD risk was analyzed using multivariate logistic regression. The diagnostic value of TyG for non-obese NAFLD was assessed using receiver-operating characteristic (ROC) curves and sensitivity analysis. A total of 3723 non-obese subjects were enrolled in this study, including 432 (11.6%) patients with NAFLD. Compared with the healthy individuals, the patients with NAFLD had significant elevations of systolic and diastolic blood pressures, total cholesterol, triglycerides, LDL-C, blood uric acid, fasting blood glucose, and TyG index and a decreased HDL-C level (P < 0.05). Multivariate logistic regression revealed that for each one-unit increase of TyG, the risk of non-obese NAFLD increased by 2.2 folds (OR=3.22, 95% CI: 2.53-4.12, P < 0.001). Compared with a TyG index in the lowest quartile Q1, a TyG index in the Q2, Q3 and Q4 quartiles was associated with an increased risk of NAFLD by 1.52 folds (OR=2.52, 95% CI: 1.20-5.95), 3.56 folds (OR=4.56, 95% CI: 2.28-10.46), and 8.66-folds (OR=9.66, 95% CI: 4.83-22.18), respectively. The RCS curve demonstrated a significant linear correlation between TyG index and non-obese NALFD risk (P for nonlinear= 0.019). For diagnosing non-obese NALFD, TyG index had an area under ROC curve of 0.819 with a sensitivity of 78.0% and a specificity of 71.2%. An increase of TyG index is correlated with increased risks of NAFLD in non-obese individuals and can serve as an indicator for screening early NAFLD in healthy individuals.

Finite element analysis of a rubber bearing base isolator under vertical and horizontal loads using a nonlinear hyper-viscoelastic material model

This research is devoted to developing a finite element model using Abaqus code for the computer simulation of an in-house developed and manufactured rubber bearing subjected to static vertical and cyclic horizontal loads. A high-damping rubber compound was designed. The material behavior of the rubber was assumed to be described by the hyper-viscoelastic model. Both linear (Prony series) and nonlinear (strain hardening power law) viscoelastic relationships were used in conjunction with the Ogden-Roxburgh equation to take the addition of the stress softening phenomenon or Mullins effect into consideration. The parameters of the material model were determined using MCalibration code in which an optimization technique was used, and data obtained in experiments carried out on test specimens were fitted into the selected model. The results of the simulations were compared with their corresponding experimental data carried out on the rubber bearing. The force-displacement behavior, stress and strain fields, and computed energy were presented and discussed. It is shown that the nonlinear viscoelastic model accompanied by the Mullins effect gives the best results. Moreover, the model could accurately predict the energy variations during the earthquake loading.

Effects of MgO on the fibre spinnability, mechanical properties and acid resistance of simulated Martian soil

The ultimate goal of Mars exploration is to construct a Mars base. In particular, it is necessary to prepare fibres by using Martian soil as a raw material. High-strength "Martian glass fibres" can be used to reinforce composite materials to meet the requirements of high-strength functional materials in base construction. Owing to the wide variation in MgO content in Martian soil, in this study, the effects of MgO on the structure, strength and acid resistance of "Martian glass and glass fibres" were investigated. The preparation conditions and mechanical properties of simulated Martian soil fibre (SMSF) were studied via DSC, XRD, Raman, NMR, FT-IR and high-temperature rotational viscometry. The corrosion behaviour of MgO-SMSF in H2SO4 solution was subsequently studied via SEM/EDS. The results showed that MgO reduces the spinnability window and prevents the fibres from stretching continuously, and a threshold appears to exist at 10.89 % MgO. The viscosity of the melt decreased significantly, and the crystallization trend increased with MgO above the threshold. The fibre tensile strength showed a nonlinear relationship with a 22.85 % increase in the fibre tensile strength at 9.24 % MgO. SEM/EDS revealed that the surface of the SMSF formed a gel layer, and the mass retention of the MgO-SMSF in the H2SO4 solution reached 90.17 %. The corrosion of SMSF under acidic conditions was controlled by ion diffusion, with Mg+ and Ca+ diffusing to the fibre surface, resulting in nonuniform corrosion. Raman-based statistical structureproperty modelling further explains the impact of MgO-induced structural changes on the tensile strength and elastic modulus, with good agreement between the model predictions and measured values.

The association between the triglyceride-glucose index and sarcopenia: data from the NHANES 2011–2018

BackgroundThe Triglyceride-glucose (TyG) index is a marker of insulin resistance, but its role in sarcopenia is controversial. The purpose of this study was to investigate the association of the TyG index with sarcopenia.Methods4030 participants aged 20 years and above were selected from National Health and Nutrition Examination Survey for cross sectional study. Weighted logistic regression model was used to estimate the association between TyG index and sarcopenia. Threshold effect analysis and restricted cubic spline were employed to describe nonlinear link, with interaction tests and subgroup analyses performed.ResultsIt was found in the fully adjusted model that the TyG index was positively associated with sarcopenia (per 1-unit increase in the TyG index: OR = 1.31, 95%CI: 1.07, 1.60). This association was further highlighted in groups characterized by the absence of MetS or diabetes, as well as the absence of vigorous or moderate work activity. Furthermore, analysis of the curve fitting and threshold effects indicated a nonlinear relationship, which exhibited a turning point at 9.14.ConclusionThe study results indicated that the TyG index was positively associated with sarcopenia. Enhancing the management of insulin resistance could help reduce the risk of developing sarcopenia.

Modelling climate impacts on paediatric sepsis incidence and severity in Bangladesh.

Sepsis is a leading cause of paediatric mortality worldwide, disproportionately affecting children in low- and middle-income countries. The impacts of climate change on the burden and outcomes of sepsis in low- and middle-income countries, particularly in paediatric populations, remain poorly understood. We aimed to assess the associations between climate variables (temperature and precipitation) and paediatric sepsis incidence and mortality in Bangladesh, one of the countries most affected by climate change. We conducted retrospective analyses of patient-level data from the International Centre for Diarrhoeal Disease Research, Bangladesh, and environmental data from the National Oceanic and Atmospheric Administration. Using random forests, we assessed associations between sepsis incidence and sepsis mortality with temperature and precipitation between 2009-22. A nonlinear relationship between temperature and sepsis incidence and mortality was identified. The lowest incidence occurred at an optimum temperature of 26.6°C with a gradual increase below and a sharp rise above this temperature. Higher precipitation levels showed a general trend of increased sepsis incidence. A similar distribution for sepsis mortality was identified with an optimum temperature of 28°C. Findings suggest that environmental temperature and precipitation play a role in paediatric sepsis incidence and sepsis mortality in Bangladesh. As children are particularly vulnerable to climate impacts, it is important to consider climate change in health care planning and resource allocation, especially in resource-limited settings, to allow for surge capacity planning during warmer and wetter seasons. Further prospective research from more globally representative data sets will provide more robust evidence on the nature of the relationships between climate variables and paediatric sepsis worldwide.

High frequency monitoring of credit creation: A new tool for central banks in emerging market economies

This study utilizes weekly datasets on loan growth in Colombia to develop a daily indicator of credit expansion using a two-step machine learning approach. Initially, employing Random Forests (RF), missing data in the raw credit indicator is filled using high frequency indicators like spreads, interest rates, and stock market returns. Subsequently, Quantile Random Forest identifies periods of excessive credit creation, particularly focusing on growth quantiles above 95 %, indicative of potential financial instability. Unlike previous studies, this research combines machine learning with mixed frequency analysis to create a versatile early warning instrument for identifying instances of excessive credit growth in emerging market economies. This methodology, with its ability to handle nonlinear relationships and accommodate diverse scenarios, offers significant value to central bankers and macroprudential authorities in safeguarding financial stability.

Duration of surgical antibiotic prophylaxis and surgical site infection in orthopaedic surgery: A prospective cohort study.

The optimal duration for surgical antibiotic prophylaxis (SAP) for preventing surgical site infection (SSI) in orthopaedic surgeries remains poorly supported by high-level evidence. This study aimed to assess the association between SAP duration and the occurrence of SSI within one year postoperatively. This prospective cohort study was based on the database from Surgical Site Infection Surveillance and Improvement Project (SISIP) of a tertiary orthopaedic university hospital from October 2014 to December 2020. The main outcome was SSI, defined according to the CDC/NHSN criteria, determined by review of index hospitalization medical records, microbiology laboratory reports, and readmission records for SSI treatment within one-year after discharge. Adjusted Generalized additive models (GAMs) were performed to assess the relationships between SAP duration and SSI, determined the cut-off point of SAP duration, and estimate the relative contribution of each included variable, across the overall cohort and the three subgroups (open fracture, closed fracture, and non-traumatic group). Multivariable logistic regression models were used to estimate the association between prolonging SAP duration based on the cut-off point and SSI. There were 37,046 patients (55.1% male) included, with the overall SSI incidence of 2.35% (871/37,046). In adjusted GAMs, no statistically significant relationships were observed in overall cohort and open or closed group (P>0.05), but a nonlinear relationship was exhibited non-traumatic group (P=0.03); the cut-off point were 2.4 days for overall cohort and 3.6 days (open), 2.6 days (closed), 1.1 days (non-trauma) for three subgroups. In adjusted logistic regression, prolonging SAP duration did not demonstrate a statistically significant protective effect in overall cohort (aOR=0.868; 95% CI, 0.746-1.011) and three groups (open: aOR=0.867; 95% CI, 0.668-1.124; closed: aOR=0.925; 95% CI, 0.754-1.135; non-trauma: aOR=1.184; 95% CI, 0.832-1.683). The relative contribution ranks of SAP duration were 21st overall among 34 factors, 14th for open fractures, 28th for closed fractures, and 3rd for non-traumatic group among 33 factors. Prolonged postoperative SAP duration has no protective effect against SSI in orthopaedic surgery. Our findings support current guidelines against the practice of continuing SAP postoperatively.

Association Between Accelerometer-Measured Irregular Sleep Duration and Type 2 Diabetes Risk: A Prospective Cohort Study in the UK Biobank.

To evaluate the association between irregular sleep duration and incident diabetes in a U.K. population over 7 years of follow-up. Among 84,421 UK Biobank participants (mean age: 62 years) who were free of diabetes at the time of providing accelerometer data in 2013-2015 and prospectively followed until May 2022, sleep duration variability was quantified by the within-person SD of 7-night accelerometer-measured sleep duration. We used Cox proportional hazard models to estimate hazard ratios (HRs) for incident diabetes (identified from medical records, death register, and/or self-reported diagnosis) according to categories of sleep duration SD. There were 2,058 incident diabetes cases over 622,080 person-years of follow-up. Compared with sleep duration SD ≤ 30 min, the HR (95% CI) was 1.15 (0.99, 1.33) for 31-45 min, 1.28 (1.10, 1.48) for 46-60 min, 1.54 (1.32, 1.80) for 61-90 min, and 1.59 (1.33, 1.90) for ≥91 min, after adjusting for age, sex, and race. We found a nonlinear relationship (p nonlinearity 0.0002), with individuals with a sleep duration SD of >60 vs. ≤60 min having 34% higher diabetes risk (95% CI 1.22, 1.47). Further adjustment for lifestyle, comorbidities, environmental factors, and adiposity attenuated the association (HR comparing sleep duration SD of >60 vs. ≤60 min: 1.11; 95% CI 1.01, 1.22). The association was stronger among individuals with lower diabetes polygenic risk score (PRS; P interaction ≤ 0.0264) and longer sleep duration (P interaction ≤ 0.0009). Irregular sleep duration was associated with higher diabetes risk, particularly in individuals with a lower diabetes PRS and longer sleep duration.

Board gender diversity, investment efficiency and sustainable growth: evidence from Gulf Cooperation Council

Purpose This study aims to examine the impact of board gender diversity on sustainable growth by considering the mediating role of investment efficiency (INVEFF) in this relationship and the threshold effect between board gender diversity and INVEFF. This investigation focuses on the Gulf Cooperation Council (GCC) region, which is characterized by rapid socio-economic transformations and a recent emphasis on gender diversity. Design/methodology/approach Panel data regressions are applied to estimate the impact of board gender diversity on INVEFF using companies listed in the GCC in 2013–2022 as a sample. The estimations consider subsamples of underinvestment and overinvestment, as well as the pre- and post-COVID-19 pandemic periods. Findings The empirical results show a nonlinear impact of board gender diversity on INVEFF, a relationship that is more pronounced in the underinvestment subsample. The results indicate that INVEFF mediates the relationship between board gender diversity and corporate sustainable growth, which helps companies optimize their board composition to enhance their sustainable growth strategies. Research limitations/implications These findings could inform GCC regulators in mandating further increases in women’s presence on boards of directors to improve INVEFF. This study examined only GCC-listed companies. Future research should investigate other factors influencing INVEFF and conduct comparative studies across Middle Eastern and North African countries to consider different regulatory and economic contexts and to examine compliance with international standards. Social implications This study reveals the significant nonlinear impact of board gender diversity on INVEFF and the mediation of INVEFF in the relationship between board gender diversity and sustainable growth. These findings will help companies optimize their board of directors’ composition by increasing the presence of women on boards to improve their INVEFF and sustainable growth. This study aims to develop knowledge that will not only benefit companies regarding the potential impact of board gender diversity but also help international communities create better gender equality within companies. Originality/value To the best of the author’s knowledge, this study is the first to explore the relationship between board gender diversity and INVEFF in the emerging economies of the GCC region. It is also the first to examine the nonlinear relationship between board gender diversity and INVEFF and the mediating role of INVEFF in the relationship between board diversity and sustainable growth. This study contributes to the understanding of the financial impact of board gender diversity in improving corporate INVEFF and sustainable growth.

Association between serum calcium levels and in-hospital mortality in sepsis: A retrospective cohort study

BackgroundThis study examines serum calcium levels and in-hospital mortality in patients with sepsis, a subject with contradictory findings in the existing literature. MethodsThis retrospective cohort study utilized data from the MIMIC-IV database, focusing on adult patients diagnosed with sepsis between 2008 and 2019. The serum calcium levels were taken as the highest value within the first 24 h of Intensive Care Unit (ICU) admission. We performed Cox proportional hazards regression analyses in multivariable-adjusted models to investigate the association between serum calcium levels and in-hospital mortality. Restricted cubic spline functions were used to assess the nonlinear relationship, and threshold effect analysis was conducted to identify potential inflection points. ResultsA total of 18,546 patients with sepsis were included in the study, and an in-hospital mortality rate of 16.9 % (3,126 out of 18,546) was obtained. Furthermore, a U-shaped relationship was observed between serum calcium concentrations and in-hospital mortality, with the lowest point at approximately 8.23 mg/dL. Hazard ratios were calculated as 0.75 (95 % CI: 0.67–0.85, P < 0.001) on the left side and 1.10 (95 % CI: 1.04–1.16, P < 0.001) on the right side of the inflection point. Sensitivity analyses corroborated these results. ConclusionThe research identified a U-shaped correlation between serum calcium concentrations and in-hospital mortality rates among patients with sepsis, underscoring the importance of serum calcium monitoring in this patient population upon hospital admission.

Threshold effects between caffeine intake and urine flow rate: a population-based cross-sectional study.

The potential benefits of caffeine intake are currently receiving much attention and exploration. Urine flow rate (UFR) is an objective index to comprehensively reflect bladder function. The aim of this study was to investigate the association between caffeine intake and UFR using the National Health and Nutrition Examination Survey (NHANES) database. 14,142 participants were enrolled in this study. Weighted multivariate adjusted regression models were used to explore the relationship between caffeine intake and UFR. The dose-response relationships were explored using a restricted cubic spline (RCS) and a threshold effect analysis was conducted based on the inflection points identified by the two-segment linear regression model. In addition, subgroup analysis and sensitivity analysis were applied. The findings suggested that the intake of caffeine was correlated with improved UFR [Model 3: 0.091 (0.057, 0.126), P value < 0.001]. In addition, the RCS supported a nonlinear relationship between them. The analysis of threshold effect further revealed a specific level of caffeine intake (34.51mg/day) that exhibited a significant enhancement in UFR. Finally, through re-analyzing the data set obtained after multiple imputation (MI), we obtained similar results. This study found a nonlinear beneficial relationship between caffeine intake and UFR, and revealed the recommended intake of caffeine. The values varied by gender, race, education, and smoking status.

Association between triglyceride to high-density lipoprotein cholesterol ratio and nonalcoholic fatty liver disease and liver fibrosis in American adults: an observational study from the National Health and Nutrition Examination Survey 2017-2020.

This study investigated the link between triglyceride to high-density lipoprotein cholesterol (TG/HDL-C) ratio and nonalcoholic fatty liver disease (NAFLD) and liver fibrosis in American adults. Information for 6495 participants from the National Health and Nutrition Examination Survey (NHANES) 2017-2020.03 was used for this cross-sectional study. The link between TG/HDL-C ratios and NAFLD and liver fibrosis was assessed by multiple linear regression before evaluating nonlinear correlations based on smoothed curve fitting models. Stratification analysis was then applied to confirm whether the dependent and independent variables displayed a stable association across populations. TG/HDL-C ratios were positively correlated with NAFLD, with higher ratios being linked to increased prevalence of NAFLD. After adjusting for potential confounders, the odds ratios (OR) for NAFLD patients in the fourth TG/HDL-C quartile were 3.61 (95% confidence interval [CI], 2.94-4.38) (P for trend < 0.001) in comparison with those in the first quartile after adjusting for clinical variables. However, no statistical significance was noted for the ratio for liver fibrosis after adjusting for potential confounders (P for trend = 0.07). A nonlinear correlation between TG/HDL-C ratios and NAFLD was observed based on smoothed curve fitting models. However, a nonlinear relationship between the ratios and liver fibrosis was not established. In subgroup analyses, there was an interaction between smoking status and TG/HDL-C ratio in relation to the prevalence of liver fibrosis (P for interaction < 0.001). Among American adults, the TG/HDL-C ratio was noted to be nonlinearly positively associated with the prevalence of NAFLD; however, this relationship was not present in liver fibrosis.

The Effect of Age and Stellar Model Choice on Globular Cluster Color-to-metallicity Conversions

The photometric colors of globular clusters (GCs) act as effective proxies for metallicity, since all normally used optical/IR color indices exhibit a nonlinear but monotonic relation between their integrated color and their metallicity. One color index, (g − z) or (F475W–F850LP), has been spectroscopically calibrated in several studies, providing leverage to define color-to-metallicity conversions for other indices. In this paper, building on the work of Hartman et al., we study the GC color-metallicity relation in more detail by testing the dependence of the relations on different suites of stellar models and different assumed GC ages. Though noticeable differences between models exist, we find that the net effect on the derived GCS metallicity distributions is small.

Neural interval‐censored survival regression with feature selection

AbstractSurvival analysis is a fundamental area of focus in biomedical research, particularly in the context of personalized medicine. This prominence is due to the increasing prevalence of large and high‐dimensional datasets, such as omics and medical image data. However, the literature on nonlinear regression algorithms and variable selection techniques for interval‐censoring is either limited or nonexistent, particularly in the context of neural networks. Our objective is to introduce a novel predictive framework tailored for interval‐censored regression tasks, rooted in Accelerated Failure Time (AFT) models. Our strategy comprises two key components: (i) a variable selection phase leveraging recent advances on sparse neural network architectures; (ii) a regression model targeting prediction of the interval‐censored response. To assess the performance of our novel algorithm, we conducted a comprehensive evaluation through both numerical experiments and real‐world applications that encompass scenarios related to diabetes and physical activity. Our results outperform traditional AFT algorithms, particularly in scenarios featuring nonlinear relationships.

Low-density lipoprotein cholesterol-to-lymphocyte count ratio (LLR) is a promising novel predictor of postoperative new-onset deep vein thrombosis following open wedge high tibial osteotomy: a propensity score-matched analysis

BackgroundThe association of low-density lipoprotein cholesterol (LDL-C) and lymphocyte counts with the development of deep vein thrombosis (DVT) has been demonstrated in many fields but remains lacking in open wedge high tibial osteotomy (OWHTO). This study aimed to assess the predictive value of LDL-C to lymphocyte count ratio (LLR) in screening for postoperative new-onset DVT.MethodsClinical data were retrospectively collected from patients who underwent OWHTO between June 2018 and May 2023. The limited restricted cubic spline (RCS) was conducted to evaluate the nonlinear relationship between LLR and the risk of postoperative new-onset DVT. The receiver operating characteristic (ROC) curves were plotted and the predictive value of biomarkers was assessed. After adjusting for intergroup confounders by propensity score matching, the univariate logistic regression was applied to assess the association between LLR and DVT.Results1293 eligible patients were included. RCS analysis showed a linear positive correlation between LLR and the risk of DVT (P for overall = 0.008). We identified LLR had an area under the curve of 0.607, accuracy of 74.3%, sensitivity of 38.5%, and specificity of 80.7%, and LLR > 1.75 was independently associated with a 1.45-fold risk of DVT (95% CI: 1.01–2.08, P = 0.045). Furthermore, significant heterogeneities were observed in the subgroups of age, BMI, diabetes mellitus, hypertension, Kellgren-Lawrence grade, the American Society of Anesthesiologists (ASA) score, and intraoperative osteotomy correction size.ConclusionLLR is a valuable biomarker for predicting postoperative new-onset DVT in patients with OWHTO, and routine screening is expected to yield positive benefits.

Artificial Neural Network Models for Solution Concentration Measurement during Cooling Crystallization of Ceritinib

The development of a quantitative in-line UV spectroscopic method for monitoring of solute concentration during the crystallization process of the active pharmaceutical ingredient (API), ceritinib is described. The method is based on artificial neural networks (ANN). A seeded cooling crystallization process of ceritinib from tetrahydrofuran was studied as a model system. The model was constructed from collected ATR-UV spectra and temperature records within the metastable zone. The collected spectra were preprocessed with the first derivative using the Savitzky-Golay filter. ANN models with different architectures were created and the optimal architecture was chosen based on the root mean square error of prediction (RMSEP) criterion. In addition, ANN models were compared with the models obtained by the linear partial least squares regression (PLSR). Due to the nonlinear relationship in the data set, ANN models predict the solution concentration with higher accuracy compared to linear models. The developed models were successfully used in real-time solution concentration measurement during ceritinib crystallization along with a supersaturation control module developed in-house.

Comparative Analysis of Machine Learning Models for Predicting the Mechanical Behavior of Bio-Based Cellular Composite Sandwich Structures.

The growing demand for sustainable materials has significantly increased interest in biocomposites, which are made from renewable raw materials and have excellent mechanical properties. The use of machine learning (ML) can improve our understanding of their mechanical behavior while saving costs and time. In this study, the mechanical behavior of innovative biocomposite sandwich structures under quasi-static out-of-plane compression was investigated using ML algorithms to analyze the effects of geometric variations on load-bearing capacities. A comprehensive dataset of experimental mechanical tests focusing on compression loading was employed, evaluating three ML models-generalized regression neural networks (GRNN), extreme learning machine (ELM), and support vector regression (SVR). Performance indicators such as R-squared (R2), mean absolute error (MAE), and root mean square error (RMSE) were used to compare the models. It was shown that the GRNN model with an RMSE of 0.0301, an MAE of 0.0177, and R2 of 0.9999 in the training dataset, and an RMSE of 0.0874, MAE of 0.0489, and R2 of 0.9993 in the testing set had a higher predictive accuracy. In contrast, the ELM model showed moderate performance, while the SVR model had the lowest accuracy with RMSE, MAE, and R2 values of 0.5769, 0.3782, and 0.9700 for training, and RMSE, MAE, and R2 values of 0.5980, 0.3976 and 0.9695 for testing, suggesting that it has limited effectiveness in predicting the mechanical behavior of the biocomposite structures. The nonlinear load-displacement behavior, including critical peaks and fluctuations, was effectively captured by the GRNN model for both the training and test datasets. The progressive improvement in model performance from SVR to ELM to GRNN was illustrated, highlighting the increasing complexity and capability of machine learning models in capturing detailed nonlinear relationships. The superior performance and generalization ability of the GRNN model were confirmed by the Taylor diagram and Williams plot, with the majority of testing samples falling within the applicability domain, indicating strong generalization to new, unseen data. The results demonstrate the potential of using advanced ML models to accurately predict the mechanical behavior of biocomposites, enabling more efficient and cost-effective development and optimization processes in the field of sustainable materials.

Association between the serum uric acid/serum creatinine ratio and cognitive function in older adults: NHANES in the United States

Cognitive impairment can potentially become a significant health concern in older adults. However, early effective diagnostic methods are still lacking. Therefore, we utilized the NHANES database in the US to investigate the relationship between serum uric acid to serum creatinine (SUA/SCR) ratio and cognitive impairment. In our study, a total of 3874 participants were included (2001–2002, 2011–2014). Weighted t tests or chi-square tests were utilized to analyze the basic characteristics of the population. Weighted logistic regression analysis, smooth-fit curves, threshold effects, and subgroup analysis were conducted to investigate the correlation between the SUA/SCR and cognitive impairment. In this study, the SUA/SCR was significantly lower in individuals with cognitive impairment. The logistic regression model, after adjusting for all covariates, revealed that the Q2–Q4 were 0.65 (95% CI 0.49, 0.86), 0.60 (95% CI 0.40, 0.90), 0.55 (95% CI 0.39, 0.77) respectively. This indicates that participants in the Q4 had a 45% reduced risk of cognitive impairment. Smooth-fit curves and threshold effect analysis revealed a nonlinear relationship between SUA/SCR and cognitive impairment, with a turning point at 4.13. Subgroup analysis showed no statistically significant differences in the relationship between SUA/SCR and cognitive impairment among different subgroups (P > 0.05). Our findings indicate a negative correlation between the SUA/SCR and the risk of cognitive impairment in the population of adults aged 60 and above in the US. This suggests that the SUA/SCR holds promise as a potential indicator for cognitive impairment.

The entrepreneurial orientation of special district governments: a study of public entrepreneurship in local governments in Tennessee

ABSTRACT Special districts are an increasingly prevalent form of local government. The creation of special districts is an act of public entrepreneurship. However, it is not clear if special districts are imprinted with an enduring entrepreneurial orientation when they are founded or if their entrepreneurial orientation fades as the organisations mature and become more bureaucratic. Building on theories of imprinting and organisational structure and utilising computer-automated text analysis, we explore the persistence and performance implications of the entrepreneurial orientation of special districts through text analysis of local government documents. In a sample of 277 special districts in Tennessee (USA), we find that special districts exhibit an entrepreneurial orientation, this orientation differs based on special district type, and a nonlinear (inverted-U) relationship exists between entrepreneurial orientation and special district performance. Our findings contribute to research at the intersection of local governments and public entrepreneurship and generate actionable insights for local government managers.

Association between organophosphorus insecticides exposure and osteoarthritis in patients with arteriosclerotic cardiovascular disease

BackgroundOrganic phosphorus insecticides (OPPs) are a class of environmental pollutants widely used worldwide with potential human health risks. We aimed to assess the association between exposure to OPPs and osteoarthritis (OA) particularly in participants with atherosclerotic cardiovascular disease (ASCVD).MethodsParticipants’ information was obtained from data in the National Health and Nutrition Examination (NHANES). Weighted logistic regression models were utilized to detect associations between OPPs metabolites and OA. Restricted cubic spline plots (RCS) were drawn to visualize the dose-response relationship between each metabolite and OA prevalence. Weighted quantile sum (WQS) regression and Bayesian kernel-machine regression (BKMR), were applied to investigate the joint effect of mixtures of OPPs on OA.ResultsA total of 6871 samples were included in our study, no significant associations between OPPs exposure and OA incidence were found in whole population. However, in a subset of 475 individuals with ASCVD, significant associations between DMP (odds ratio [OR] as a continuous variable = 1.22, 95% confidence interval [CI]: 1.07,1.28), DEP ((odds ratio [OR] of the highest tertile compared to the lowest = 2.43, 95% confidence interval [CI]: 1.21,4.86), and OA were observed. DMP and DEP showed an increasing dose-response relationship to the prevalence of OA, while DMTP, DETP, DMDTP and DEDTP showed a nonlinear relationship. Multi-contamination modeling revealed a 1.34-fold (95% confidence intervals:0.80, 2.26) higher prevalence of OA in participants with high co-exposure to OPPs compared to those with low co-exposure, with a preponderant weighting (0.87) for the dimethyl dialkyl phosphate metabolites (DMAPs). The BKMR also showed that co-exposure of mixed OPPs was associated with an increased prevalence of OA, with DMP showing a significant dose-response relationship.ConclusionHigh levels of urine dialkyl phosphate metabolites (DAP) of multiple OPPs are associated with an increased prevalence of OA in patients with ASCVD, suggesting the need to prevent exposure to OPPs in ASCVD patients to avoid triggering OA and further avoid the occurrence of cardiovascular events caused by OA.