Search Results (15,060)

This study investigates the correlation between the Gini index and gross domestic product (GDP) in two of the world’s largest capitalist economies: the United States and the United Kingdom. Utilizing econometric methods, including stationarity tests and linear regression, this research work aims to elucidate the relationship between economic inequality and economic growth. The results for the United States reveal a significant positive correlation between GDP and the Gini index, suggesting that economic growth is associated with rising income inequality. In contrast, the United Kingdom shows a much weaker relationship, indicating that other factors, such as redistributive policies and social welfare programs, may mitigate the impact of economic growth on income inequality. These findings highlight the importance of national policies and institutional frameworks in shaping economic outcomes and can be used in policy making. This study contributes to the existing literature by providing a comparative analysis of the correlation between GDP and the Gini index in two major capitalist economies, offering fresh empirical insights. Full article

Previous evidence has demonstrated a relationship between insecure attachment and the development of emotional dependence towards an individual’s partner. However, the possibility that this relationship may be indirect and mediated by individual factors such as difficulties in emotional regulation has not previously been explored. Consequently, the objectives of the present study were to analyze the (i) differences in emotional dependence on an individual’s partner and difficulties in emotional regulation capacity according to secure, preoccupied or dismissing attachment style and (ii) mediating role of difficulties in emotional regulation in the relationship between both insecure attachment styles and emotional dependence on an individual’s partner. The sample comprised 741 participants ranging in age from 18 to 30 years (M = 21.32, SD = 2.93). The mediations were tested with linear regressions with the macro PROCESS v4.0. The results showed that emotional dependence on a partner and difficulties in emotional regulation were greater among individuals who had developed a dismissing attachment compared those with secure or preoccupied attachment. Likewise, the mediation model confirmed the mediating role of difficulties in the capacity for emotional regulation in the relationship between dismissing attachment and emotional dependence, with rejection of negative or discomfort-generating emotions predominating. The findings provide preliminary evidence that rejection of negative emotional experiences may play an important role in the relationship between insecure dismissing attachment style and emotional dependence on an individual’s partner. Consequently, it is recommended that emotional dependence intervention programs include of the management of intolerance to negative emotions. Full article

Background: kidney transplant recipients are exposed to multiple pathogenic pathways that may alter short and long-term allograft survival. Metabolomic profiling is useful for detecting potential biomarkers of kidney disease with a predictive capacity. This field is still under development in kidney transplantation and metabolome analysis is faced with analytical challenges. We performed a cross-sectional study including stable kidney transplant patients and aimed to search for relevant associations between baseline plasmatic and urinary metabolites and relevant outcomes over a follow-up period of 3 years. Methods: we performed a cross-sectional study including 72 stable kidney transplant patients with stored plasmatic and urinary samples at the baseline evaluation which were there analyzed by nuclear magnetic resonance in order to quantify and describe metabolites. We performed a 3-year follow-up and searched for relevant associations between renal failure outcomes and baseline metabolites. Between-group comparisons were made after classification by observed estimated glomerular filtration rate slope during the follow-up: positive slope and negative slope. Results: The mean estimated GFR (glomerular filtration rate) was higher at baseline in the patients who exhibited a negative slope during the follow-up (63.4 mL/min/1.73 m² vs. 55.8 mL/min/1.73 m², p = 0,019). After log transformation and division by urinary creatinine, urinary dimethylamine (3.63 vs. 3.16, p = 0.027), hippuric acid (7.33 vs. 6.29, p = 0.041), and acetone (1.88 vs. 1, p = 0.023) exhibited higher concentrations in patients with a negative GFR slope when compared to patients with a positive GFR slope. By computing a linear regression, a significant low-strength regression equation between the log 2 transformed plasmatic level of glycine and the estimated glomerular filtration rate was found (F (1,70) = 5.15, p = 0.026), with an R² of 0.069. Several metabolites were correlated positively with hand grip strength (plasmatic tyrosine with r = 0.336 and p = 0.005 and plasmatic leucine with r = 0.371 and p = 0.002). Other urinary metabolites were found to be correlated negatively with hand grip strength (dimethylamine with r = −0.250 and p = 0.04, citric acid with r = −0.296 and p = 0.014, formic acid with r = −0.349 and p = 0.004, and glycine with r = −0.306 and p = 0.01). Conclusions: some metabolites had different concentrations compared to kidney transplant patients with negative and positive slopes, and significant correlations were found between hand grip strength and urinary and plasmatic metabolites. Full article

This work provides an economic exploration of the multifaceted world of medical malpractice risk. Third party liability insurance plays a central role in protecting healthcare providers and public care institutions from the financial consequences of medical malpractice claims, although in recent years, the industry landscape has been characterised by periods of distress for this type of protection, with rising litigations and reimbursement costs, resulting in a peculiarly complex market. For the Italian context, the study focuses on the financial repercussions for healthcare institutions of the growing trend towards risk retention practises, legally empowered by the introduction of Law No. 24/2017. The analysis employs Generalised Linear Models for the regressive approach to incorporate the structural and organisational characteristics of hospitals and uses quantitative simulations to explore different scenarios at a regional aggregate level. Due to the limited existing literature and data on the topic, this research aims to provide new methods for effectively understanding and managing this type of risk, thereby supporting decision-making processes in the healthcare sector. Full article

Background: Vitamin D deficiency is thought to increase the likelihood of insulin resistance (IR) and diabetes onset. The objective of this study was to examine the association between the triglyceride glucose (TyG) index and vitamin D levels in individuals with prediabetes and type 2 diabetes mellitus (T2DM) in the tribal community of India. Methods: This study included 270 participants, consisting of 90 individuals with prediabetes, 90 individuals with T2DM, and 90 control patients. Anthropometric and biochemical characteristics were evaluated in all participants. 25-hydroxyvitamin D [25(OH)D] levels were measured using a chemiluminescent immunoassay. The TyG index was computed as Ln [fasting triglycerides (mg/dL) × fasting glucose (mg/dL)]/2. Spearman correlation analysis and linear regression analysis were performed to assess the relationship between the TyG index and 25(OH)D levels in people diagnosed with prediabetes and T2DM. The optimum cut-off value of the TyG index for detecting vitamin D deficiency was determined by receiver operating characteristic (ROC) curve analysis. Results: We observed a significant reduction in vitamin D levels in individuals with prediabetes and T2DM compared to those in the control group. However, the TyG index was significantly greater in individuals with prediabetes and T2DM than in controls. Statistical analysis revealed a significant negative correlation between the TyG index and 25(OH)D in both prediabetes and T2DM. Conclusions: The TyG index demonstrated a negative association with vitamin D levels and was identified as an independent predictor of vitamin D deficiency in individuals with prediabetes and T2DM. Full article

Background/Objectives: Genetic factors contribute to the physiopathology of obesity and its comorbidities. This study aimed to investigate the association of the SNPs ABCA1 (rs9282541), ADIPOQ (rs2241766), FTO (rs9939609), GRB14 (rs10195252), and LEPR (rs1805134) with various clinical, anthropometric, and biochemical variables. Methods: The study included 396 Mexican mestizo individuals with obesity and 142 individuals with normal weight. Biochemical markers were evaluated from peripheral blood samples, and SNP genotyping was performed using PCR with TaqMan probes. A genetic risk score (GRS) was computed using an additive model. Results: No significant associations were found between the SNPs ABCA1, ADIPOQ, FTO, and LEPR with obesity. However, the T allele of the GRB14 SNP was significantly associated with obesity (χ² = 5.93, p = 0.01; OR = 1.52; 95% CI: 1.08–2.12). A multivariate linear regression model (adjusted R-squared: 0.1253; p < 0.001) predicting LDL-c levels among all participants (n = 538) identified significant (p < 0.05) beta coefficients for several anthropometric and biochemical variables, as well as for the GRS. Additionally, the interaction between the GRS and the waist-to-hip ratio (WHR) showed a negative beta coefficient (BC = −26.5307; p = 0.014). Participants with a WHR < 0.839 showed no effect of GRS on LDL-c concentration, while those with a WHR > 0.839 exhibited a greater effect of GRS (~9) at lower LDL-c concentrations (~50 mg/dL) and a lesser effect of GRS (~7) at higher LDL-c concentrations (~250 mg/dL). Conclusions: A significant interaction between genetics and WHR influences LDL-c in Mexicans, which may contribute to the prevention and clinical management of dyslipidemia and cardiovascular disease. Full article

Background/Objectives: The main outcomes of the COVID-19 pandemic can be used to assess the capability and sustainability of public healthcare and Long-Term Care (LTC) systems. This study aims to identify the population’s demographic and socio-economic characteristics, as well as other national resources associated with the incidence and mortality of COVID-19, by comparing three European countries during the first pandemic period (Italy, Spain, and Germany). The results will identify possible strengths and weaknesses that could be considered as hints of the need for health and social intervention. Methods: Variables describing the countries’ core demographics, socio-economic characteristics, and national resources were collected from 2001–2021 from well-established international databases. COVID-19 incidence and death figures from 1 March 2020 to 31 March 2021 were extracted from national health databases. Analysis focused on bivariate and weighted multivariable linear regressions between incidence, mortality, and socio-economic covariates. Results: Findings show that both care models and socio-demographic characteristics influenced the capability of the first year’s response to the COVID-19 emergency. Formal public care appears to represent the most effective strategy against incidence and mortality regarding COVID-19, especially for older people, because it mitigates the adverse effects of socio-economic characteristics. Conclusions: Current strategies oriented towards privatizing care should, therefore, be considered critically, since they may result in weaker protection of vulnerable groups, such as frail older people, due to the unequal position of individuals with different socio-economic conditions in purchasing services from the care market. Full article

Understanding the driving mechanisms behind surface urban heat island (SUHI) effects is essential for mitigating the degradation of urban thermal environments and enhancing urban livability. However, previous studies have primarily concentrated on central urban areas, lacking a comprehensive analysis of the entire metropolitan area over distinct time periods. Additionally, most studies have relied on regression analysis models such as ordinary least squares (OLS) or logistic regression, without adequately analyzing the spatial heterogeneity of factors influencing the surface urban heat island (SUHI) effects. Therefore, this study aims to explore the spatial heterogeneity and driving mechanisms of surface urban heat island (SUHI) effects in the Guangzhou-Foshan metropolitan area across different time periods. The Local Climate Zones (LCZs) method was employed to analyze the landscape characteristics and spatial structure of the Guangzhou-Foshan metropolis for the years 2013, 2018, and 2023. Furthermore, Geographically Weighted Regression (GWR), Multi-scale Geographically Weighted Regression (MGWR), and Geographical Detector (GD) models were utilized to investigate the interactions between influencing factors (land cover factors, urban environmental factors, socio-economic factors) and Surface Urban Heat Island Intensity (SUHII), maximizing the explanation of SUHII across all time periods. Three main findings emerged: First, the Local Climate Zones (LCZs) in the Guangzhou-Foshan metropolitan area exhibited significant spatial heterogeneity, with a non-linear relationship to SUHII. Second, the SUHI effects displayed a distinct core-periphery pattern, with Large lowrise (LCZ 8) and compact lowrise (LCZ 3) areas showing the highest SUHII levels in urban core zones. Third, land cover factors emerged as the most influential factors on SUHI effects in the Guangzhou-Foshan metropolis. These results indicate that SUHI effects exhibit notable spatial heterogeneity, and varying negative influencing factors can be leveraged to mitigate SUHI effects in different metropolitan locations. Such findings offer crucial insights for future urban policy-making. Full article

Background: Albuminuria and albumin excretion rate (AER) are important risk factors for chronic kidney disease (CKD) development. Despite the extensive evidence of the influence of sodium and potassium on cardiovascular health, the existing evidence regarding their impact on albuminuria and kidney disease is limited and inconsistent. Our study aimed to assess the correlation between urinary sodium and potassium excretion, and the sodium-to-potassium ratio (Na/K ratio) with impaired kidney function, particularly the AER and albuminuria. Materials and Methods: Data were collected from the Lithuanian NATRIJOD study. A total of 826 single 24-h urine samples from individuals aged 18 to 69 were collected and analyzed for their sodium and potassium levels, Na/K ratio, and AER. Albuminuria was defined as an AER exceeding 30 mg/24 h. Results: The participant mean age was 47.2 ± 12.1 years; 48.5% of the participants were male. The prevalence of albuminuria was 3%. Correlation analysis revealed a positive correlation between AER and urinary sodium excretion (r_s = 0.21; p < 0.001) and urinary potassium excretion (r_s = 0.28; p < 0.001). In univariate linear regression analysis, sodium and potassium excretion and the Na/K ratio were significant AER predictors with β coefficients of 0.028 (95% CI: 0.015; 0.041; p < 0.001), 0.040 (95% CI: 0.003; 0.077; p = 0.035), and 1.234 (95% CI: 0.210; 2.259; p = 0.018), respectively. In the multivariable model, only urinary sodium excretion remained significant, with a β coefficient of 0.028 (95% CI: 0.016; 0.041). Potential albuminuria predictive factors identified via univariate logistic regression included urinary sodium excretion (OR 1.00; 95% CI: 1:00; 1.01) and the Na/K ratio (OR 1.53; 95% CI: 1.11; 2.05). However, these factors became statistically insignificant in the multivariate model. Conclusions: Urinary sodium and potassium excretion and the Na/K ratio are significantly associated with kidney damage, considering the assessed 24-h albumin excretion rate and presence of albuminuria content. Full article

Accurate and timely estimation of oat biomass is crucial for the development of sustainable and efficient agricultural practices. This research focused on estimating and predicting forage oat biomass using UAV and agronomic variables. A Matrice 300 equipped with a multispectral camera was used for 14 flights, capturing 21 spectral indices per flight. Concurrently, agronomic data were collected at six stages synchronized with UAV flights. Data analysis involved correlations and Principal Component Analysis (PCA) to identify significant variables. Predictive models for forage biomass were developed using various machine learning techniques: linear regression, Random Forests (RFs), Support Vector Machines (SVMs), and Neural Networks (NNs). The Random Forest model showed the best performance, with a coefficient of determination R² of 0.52 on the test set, followed by Support Vector Machines with an R² of 0.50. Differences in root mean square error (RMSE) and mean absolute error (MAE) among the models highlighted variations in prediction accuracy. This study underscores the effectiveness of photogrammetry, UAV, and machine learning in estimating forage biomass, demonstrating that the proposed approach can provide relatively accurate estimations for this purpose. Full article

Background: Antimicrobial consumption (AMC) data in Latin America are scarce and usually spread out across different sources used to make AMC calculations, making it difficult to both standardize and compare regions through similar time frames. The main objective was to analyze AMC trends in Social Security tertiary care hospitals in Costa Rica in the period spanning January 2017 to December 2021, using both the defined daily dose (DDD)/100 bed days and DDD/100 discharges. Methods: This is a retrospective observational study of antimicrobial consumption. Global consumption trends were calculated and expressed as DDD/100 bed days and DDD/100 discharges. Trends in antimicrobial consumption were analyzed using a simple linear regression model to determine potential differences in antimicrobial usage throughout the study’s duration. Results: A statistically significant increase in the consumption expressed in DDD/100 discharges was observed in the following groups: carbapenems, 7.6% (trend: 64.68, p < 0.0001), trimethoprim-sulfamethoxazole: 12.6% (trend: 16.45, p < 0.0001), quinolones 9.4% (trend: 36.80, p = 0.02), vancomycin 2.0% (trend: 16.30, p = 0.03), echinocandins: 6.0% (trend: 15.17, p = 0.01) and azole antifungals: 12.10% (trend: 102.05, p < 0.0001). Additionally, a statistically significant increase of 10.30% in the consumption of azole antifungals expressed in DDD/100 bed days was observed (p = 0.0008). In contrast, a statistically significant decrease in consumption, expressed in DDD/100 discharges, was identified for cephalosporins −6.0% (p < 0.0001) and macrolides −16.5% (p < 0.0001). Macrolides also showed a downward trend in consumption, as expressed in DDD/100 bed days (−14.3%, p < 0.0001). According to World Health Organization (WHO) access, watch and reserve (AWaRe) classification trend analysis, only the reserve group showed a statistically significant upward change of 9.2% (p = 0.016). Conclusions: This five-year analysis demonstrated trends over time in overall antimicrobial consumption measured in DDD/100 bed days and DDD/100 discharge rates that correlate. In general, for all antimicrobials, after the implementation of antimicrobial stewardship programs (ASP), a downward trend is reported; in contrast, during the COVID-19 pandemic the AMC shows a general upward trend. The comparison between DDD/100 bed days and DDD/100 discharges allows for complementary comparisons to be made regarding antimicrobial exposure in a clinical setting. Full article

Elastic modulus, crucial for assessing material stiffness and structural deformation, has recently gained popularity in predictions using data-driven methods. However, research systematically comparing different machine learning models under the same conditions, especially for ultra-high-performance concrete (UHPC), remains limited. In this study, 10 different machine learning models were evaluated for their capacity to predict the elastic modulus of UHPC. The results showed that XGBoost demonstrated the highest accuracy in predictions with large training datasets, followed by KNNs. For smaller training datasets, Decision Tree exhibited the greatest accuracy, while XGBoost was the second-best performing model. Linear regression displayed the lowest accuracy. XGBoost demonstrated the most potential for accurately predicting the elastic modulus of UHPC, particularly when a comprehensive dataset is available for model training. The optimized XGBoost exhibited better predictive performance than fitting equations for different UHPC formulations. The findings of this study provide valuable insights for researchers and engineers working on the data-driven design and characterization of UHPC. Full article

Drought is a major limiting factor in the cultivation of Sophora alopecuroides in China’s arid and semi-arid regions. This study aimed to identify drought-resistant S. alopecuroides ecotypes and explore key drought tolerance indicators during germination by simulating drought conditions with a 10% PEG-6000 solution, using pure water as a control. Determination of seven germination indicators for S. alopecuroides, including germination rate (GR), germination energy (GE), germination index (GI), vigor index (VI), promptness index (PI), fresh weight (FW), and dry weight (DW), was conducted. Principal component analysis (PCA), membership function, cluster analysis, and linear regression were employed to comprehensively evaluate the drought resistance of thirty-five S. alopecuroides ecotypes. The results showed that drought stress caused reductions in six of the seven indicators across all ecotypes, except for DW, compared to the control. Correlation analysis revealed varying relationships among the indicators, with most showing significant or highly significant correlations. PCA reduced the seven indicators to two independent comprehensive factors, with a cumulative contribution rate of 83.99%. Based on the D-value and cluster analysis, the thirty-five ecotypes were ranked for drought resistance and classified into four categories. The top five drought-tolerant genotypes during the germination stage were identified as Yutian, Alar, Jinghe, Baoding, and Guyuan. Moreover, the stepwise regression model was established and demonstrated that GR, GE, PI, FW, and DW are key indicators for screening and identifying drought-resistant S. alopecuroides ecotypes. This study offers a comprehensive and reliable method for evaluating drought resistance in S. alopecuroides ecotypes and provides a reference for selecting ecotypes for artificial cultivation in Northwestern China. Full article

Background/Objectives: During 2022–2023, the School Lunch Project (SLP) provided free nutritious cooked lunches 1–4 days per week to Kinder to Grade 10 students attending 30 schools in areas of high disadvantage in Tasmania, Australia. This analysis examined if the SLP was associated with student attendance. Methods: Staff (teachers, support staff, and principals) from 12 schools completed an online survey and/or participated in focus groups/interviews. Government-held, objectively measured, grade-level attendance data were provided for 17 SLP and 11 matched comparison schools for 2018–2023. Linear mixed models compared attendance on school lunch and non-school lunch days in SLP schools. Difference-in-difference regression compared attendance between SLP and comparison schools. Qualitative data were analysed thematically. Results: Sixty-five staff completed surveys, where 22% reported that increased attendance was a benefit of the SLP. Similar findings were observed in the staff focus groups/interviews (N = 51). Mean attendance was similar on school lunch and non-school lunch days among the SLP schools during 2022 (difference: 0.04, 95% CI: −0.5, 0.6) and 2023 (difference 0.1, 95% CI: −0.2, 0.4) and similar between SLP and comparison schools (average treatment effect in the treated: 1.2, 95% CI: −0.7, 3.0). Conclusions: The SLP was perceived by some staff to improve attendance but was not associated with objectively measured attendance examined at the grade level. Full article

This paper introduces bivariate log-symmetric models for analyzing the relationship between two variables, assuming a family of log-symmetric distributions. These models offer greater flexibility than the bivariate lognormal distribution, allowing for better representation of diverse distribution shapes and behaviors in the data. The log-symmetric distribution family is widely used in various scientific fields and includes distributions such as log-normal, log-Student-t, and log-Laplace, among others, providing several options for modeling different data types. However, there are few approaches to jointly model continuous positive and explanatory variables in regression analysis. Therefore, we propose a class of generalized linear model (GLM) regression models based on bivariate log-symmetric distributions, aiming to fill this gap. Furthermore, in the proposed model, covariates are used to describe its dispersion and correlation parameters. This study uses a dataset of anthropometric measurements of newborns to correlate them with various biological factors, proposing bivariate regression models to account for the relationships observed in the data. Such models are crucial for preventing and controlling public health issues. Full article