Search Results (112)

LC continues to be the leading cause of cancer mortality globally, among both males and females, representing a major public health challenge. The impact of mitochondria on human health and disease is a rapidly growing focus in scientific research, due to their critical roles in cellular survival and death. Mitochondria play an important role in controlling imperative cellular parameters, and alterations in mtDNAcn might be crucial for LC development. MtDNAcn has been studied as a possible marker for LC risk, but its role in prevention is still unclear. This review and meta-analysis aims to summarize the current evidence and provide an overall estimate of the relationship between the mtDNA copy number in human samples like blood and sputum. PubMed, Web of Science, and Scopus databases were used for studies published up to February 2024, following PRISMA and MOOSE guidelines. Studies were combined using a random-effects model, and we assessed the heterogeneity between studies with the chi-square-based Cochran’s Q statistic and the I² statistic. Publication bias was checked using Begg’s and Egger’s tests. Five studies, including a total of 3.748 participants, met the eligibility criteria. The MtDNA copy number was measured in blood or sputum samples and compared across different quantiles. The pooled analysis did not find a significant association between the mtDNA copy number and LC risk (OR = 0.94; 95% CI: 0.49–1.78). Moreover, when looking at different study designs, no significant results were found, due to the small number of studies available. No significant publication bias was detected. Further studies are needed to better understand the connection between the mtDNA copy number and LC risk and to better understand the role of potential confounders. Full article

This paper investigates systemic tail dependence within the crypto-asset ecosystem by examining interconnectedness across eight major tokens spanning Layer 1 cryptocurrencies, DeFi tokens, stablecoins, and infrastructure/governance assets. We employ a novel partial correlation-based network framework and quantile-specific connectedness measures to examine how co-movement patterns evolve under normal and extreme market conditions from September 2021 to March 2025. Unlike conventional correlation or variance decomposition approaches, our methodology isolates direct, tail-specific transmission channels while filtering out standard shocks. The results indicate strong asymmetries in dependence structures. Systemic risk intensifies during adverse tail events, particularly around episodes such as the Terra/Luna crash, the USDC depeg, and Bitcoin’s 2024 halving cycle. Our analysis shows that ETH, LINK, and UNI are key assets in spreading losses when the market falls. In contrast, the stablecoin DAI tends to absorb some of the stress, helping reduce risk during downturns. These results indicate critical contagion pathways and suggest that regulation targeting protocol-level transparency, liquidity provisioning, and interoperability standards may reduce amplification mechanisms without eliminating interdependence. Our findings contribute to the emerging literature on crypto-systemic risk and offer actionable insights for regulators, DeFi protocol architects, and institutional investors. In particular, we advocate for the incorporation of tail-sensitive network diagnostics into real-time monitoring frameworks to better manage asymmetric spillover risks in decentralized financial systems. Full article

The Sylhet Division of Bangladesh, highly susceptible to monsoon flooding, requires effective flood risk management to reduce socio-economic losses. Flood frequency analysis is an essential aspect of flood risk management and plays a crucial role in designing hydraulic structures. This study applies regional flood frequency analysis (RFFA) using L-moments to identify homogeneous hydrological regions and estimate extreme flood quantiles. Records from 26 streamflow gauging stations were used, including streamflow data along with corresponding physiographic and climatic characteristic data, obtained from GIS analysis and ERA5 respectively. Most stations showed no significant monotonic trends, temporal correlations, or spatial dependence, supporting the assumptions of stationarity and independence necessary for reliable frequency analysis, which allowed the use of cluster analysis, discordancy measures, heterogeneity tests for regionalization, and goodness-of-fit tests to evaluate candidate distributions. The Generalized Logistic (GLO) distribution performed best, offering robust quantile estimates with narrow confidence intervals. Multiple Non-Linear Regression models, based on catchment area, elevation, and other parameters, reasonably predicted ungauged basin peak discharges (R² = 0.61–0.87; RMSE = 438–2726 m³/s; MAPE = 41–74%) at different return periods, although uncertainty was higher for extreme events. Four homogeneous regions were identified, showing significant differences in hydrological behavior, with two regions yielding stable estimates and two exhibiting greater extreme variability. Full article

Sustainable agricultural development in China in the face of growing environmental concerns relies critically on how well regulatory policies strengthen agricultural resilience. This study aims to systematically investigate the impact of air pollution on agricultural economic resilience and its mechanisms of action and to explicitly assess the moderating role of environmental regulation. This study develops a thorough index system that evaluates agricultural economic resilience in three areas: risk resistance and recovery, adaptive adjustment capacity, and restructuring innovation. Panel data from 30 Chinese provinces from 2000 to 2023 is used to achieve this. The implications of air pollution and its diverse consequences on agricultural economic resilience are systematically assessed using a two-way fixed-effects and moderating-effects model. The following are the primary conclusions: First, air pollution has a significant negative impact on the economic resilience of agriculture. This conclusion holds after considering the endogeneity problem and a series of robustness tests, such as the exclusion of samples, random sampling, and quantile regression. Second, different dimensions of agricultural economic resilience, intensity levels, and economic growth phases influence how much air pollution reduces agricultural economic resilience. Notably, at various stages of economic growth, air pollution steadily weakens the economic resilience of agriculture. In particular, the impact is more pronounced in the post-financial-crisis phase of domestic demand expansion and the phase of financial clearing and high-quality development. According to a dimensional perspective, air pollution significantly reduces the farm sector’s capacity to endure and recover from dangers while also making adaptive modifications easier, and the impact on transformational innovation is not significant. In terms of intensity, in contrast to places with higher resilience, those with lower resilience are disproportionately more adversely affected by air pollution. Third, environmental control mitigates some of the detrimental effects of air pollution on agricultural economic resilience. Based on these results, this study calls for stricter air pollution control measures, strengthens environmental regulatory support for agricultural resilience, and demonstrates region-specific governance solutions to guarantee the stability and sustainability of the agricultural economic framework. Full article

Background: Myopia is a leading cause of visual impairment worldwide, and accumulating evidence suggests that biomechanics may be closely linked to its development. Understanding this relationship may help clarify the underlying mechanisms of myopia and guide treatment strategies. The aim of the study is to investigate the causal relationship between myopia and corneal biomechanics using the UK Biobank (UKB) database. Methods: Data from 11,064 eyes in the UKB, including refraction results and Ocular Response Analyzer (ORA) measurements, were analyzed. Eyes were categorized by spherical equivalent (SE) into emmetropia, mild myopia, moderate myopia, and high myopia. One-way ANOVA assessed differences in corneal biomechanical parameters across the varying myopia groups, while Quantile Regression (QR) explored the relationship between these parameters and myopia severity across the different quantiles. A Mendelian randomization (MR) analysis was employed to explore the causal relationships. Results: Significant differences in corneal biomechanical parameters and intraocular pressure (IOP) were observed across the myopia levels (p < 0.001). High myopia was associated with lower corneal hysteresis (CH), a lower corneal resistance factor (CRF), and increased IOP. The QR analysis demonstrated that lower corneal biomechanics were associated with higher degrees of myopia, with the impact of corneal biomechanics becoming more pronounced as the myopia severity increased. The MR analysis indicated that low CH (OR = 0.9943, p = 0.004) and CRF (OR = 0.9946, p = 0.002) values were risk factors for myopia, while no causal effect was found when the myopia was treated as the exposure and corneal biomechanics as the outcome. Conclusions: This study establishes a causal relationship where reduced corneal biomechanics contribute to myopia, while myopia itself does not directly affect biomechanics. Corneal biomechanics could serve as a biomarker for assessing high myopia risk. These findings offer new insights into high myopia’s pathological mechanisms and targeted prevention. Full article

Flooding is one of the most significant natural hazards in Iran, primarily due to the country’s arid and semi-arid climate, irregular rainfall patterns, and substantial changes in watershed conditions. These factors combine to make floods a frequent cause of disasters. In this case study, flood susceptibility patterns in the Marand Plain, located in the East Azerbaijan Province in northwest Iran, were analyzed using five machine learning (ML) algorithms: M5P model tree, Random SubSpace (RSS), Random Forest (RF), Bagging, and Locally Weighted Linear (LWL). The modeling process incorporated twelve meteorological, hydrological, and geographical factors affecting floods at 485 identified flood-prone points. The data were analyzed using a geographic information system, with the dataset divided into 70% for training and 30% for testing to build and validate the models. An information gain ratio and multicollinearity analysis were employed to assess the influence of various factors on flood occurrence, and flood-related variables were classified using quantile classification. The frequency ratio method was used to evaluate the significance of each factor. Model performance was evaluated using statistical measures, including the Receiver Operating Characteristic (ROC) curve. All models demonstrated robust performance, with an area under the ROC curve (AUROC) exceeding 0.90. Among the models, the LWL algorithm delivered the most accurate predictions, followed by RF, M5P, Bagging, and RSS. The LWL-generated flood susceptibility map classified 9.79% of the study area as highly susceptible to flooding, 20.73% as high, 38.51% as moderate, 29.23% as low, and 1.74% as very low. The findings of this research provide valuable insights for government agencies, local authorities, and policymakers in designing strategies to mitigate flood-related risks. This study offers a practical framework for reducing the impact of future floods through informed decision-making and risk management strategies. Full article

Background: While current epidemiological studies have documented associations between environmental metals and renal dysfunction, the majority have concentrated on plasma metal levels. The relationship between urinary metal exposure and chronic kidney disease (CKD) remains contentious, particularly within specific demographic groups. Methods: This cross-sectional study included 2919 rural Chinese adults recruited between 2018 and 2019. Urine metals were measured by ICP-MS. Least absolute shrinkage and selection operator (LASSO) regression was employed to identify metals significantly associated with CKD. Then, we used binary logistic regression, along with restricted cubic spline (RCS) models, to assess the individual exposure effects of specific metals on CKD. Quantile g-computation, weighted quantile sum regression, and Bayesian kernel machine regression (BKMR) models were applied to evaluate combined effects of metal exposures on CKD. Gender-stratified analyses were also conducted to explore these associations. Results: LASSO identified seven metals (V, Cu, Rb, Sr, Ba, W, Pb) with significant impacts on CKD. In single-metal models, Cu and W exhibited a positive correlation with CKD, whereas V, Rb, Sr, Ba, and Pb showed significant negative correlations (all p < 0.05). RCS analysis revealed nonlinear associations between V, Cu, Ba, Pb, and CKD (all p-nonlinear < 0.05). In the multi-metal model, quantile-based g-computation demonstrated a collective negative association with CKD risk for the seven mixed urinary metal exposures (OR (95% CI) = −0.430 (−0.656, −0.204); p < 0.001), with V, Rb, Sr, Ba, and Pb contributing to this effect. The WQS model analysis further confirmed this joint negative association (OR (95% CI): −0.885 (−1.083, −0.899); p < 0.001), with V as the main contributor. BKMR model analysis indicated an overall negative impact of the metal mixture on CKD risk. Interactions may exist between V and Cu, as well as Cu and Sr and Pb. The female subgroup in the BKMR model demonstrated consistency with the overall association. Conclusions: Our study findings demonstrate a negative association between the urinary metal mixture and CKD risk, particularly notable in females. Joint exposure to multiple urinary metals may involve synergistic or antagonistic interactions influencing renal function. Further research is needed to validate these observations and elucidate underlying mechanisms. Full article

This study investigates the combined effects of environmental pollutants (lead, cadmium, total mercury) and behavioral factors (alcohol consumption, smoking) on depressive symptoms in women. Data from the National Health and Nutrition Examination Survey (NHANES) 2017–2018 cycle, specifically exposure levels of heavy metals in blood samples, were used in this study. The analysis of these data included the application of descriptive statistics, linear regression, and Bayesian Kernel Machine Regression (BKMR) to explore associations between environmental exposures, behavioral factors, and depression. The PHQ-9, a well-validated tool that assesses nine items for depressive symptoms, was used to evaluate depression severity over the prior two weeks on a 0–3 scale, with total scores ranging from 0 to 27. Exposure levels of heavy metals were measured in blood samples. BKMR was used to estimate the exposure–response relationship, while posterior inclusion probability (PIP) in BKMR was used to quantify the likelihood that a given exposure was included in the model, reflecting its relative importance in explaining the outcome (depression) within the context of other predictors in the mixture. A descriptive analysis showed mean total levels of lead, cadmium, and total mercury at 1.21 µg/dL, 1.47 µg/L, and 0.80 µg/L, respectively, with a mean PHQ-9 score of 5.94, which corresponds to mild depressive symptoms based on the PHQ-9 scoring. Linear regression indicated positive associations between depression and lead as well as cadmium, while total mercury had a negative association. Alcohol and smoking were also positively associated with depression. These findings were not significant, but limitations in linear regression prompted a BKMR analysis. BKMR posterior inclusion probability (PIP) analysis revealed alcohol and cadmium as significant contributors to depressive symptoms, with cadmium (PIP = 0.447) and alcohol (PIP = 0.565) showing notable effects. Univariate and bivariate analyses revealed lead and total mercury’s strong relationship with depression, with cadmium showing a complex pattern in the bivariate analysis. A cumulative exposure analysis of all metals and behavioral factors concurrently demonstrated that higher quantile levels of combined exposures were associated with an increased risk of depression. Finally, a single variable-effects analysis in BKMR revealed lead, cadmium, and alcohol had a stronger impact on depression. Overall, the study findings suggest that from exposure to lead, cadmium, mercury, alcohol, and smoking, cadmium and alcohol consumption emerge as key contributors to depressive symptoms. These results highlight the need to address both environmental and lifestyle choices in efforts to mitigate depression. Full article

Background: Limited evidence links urinary metal exposure to osteoporosis in broad populations, prompting this study to cover this knowledge gap using supervised and unsupervised approaches. Methods: This study included 15,923 participants from the National Health and Nutrition Examination Survey (NHANES) spanning from 1999 to 2020. Urinary concentrations of nine metals—barium (Ba), cadmium (Cd), cobalt (Co), cesium (Cs), molybdenum (Mo), lead (Pb), antimony (Sb), thallium (Tl), and tungsten (Tu)—were measured using inductively coupled plasma mass spectrometry (ICP-MS). Osteoporosis was assessed via dual-energy X-ray absorptiometry. A weighted quantile sum (WQS) regression analysis evaluated each metal’s contribution to osteoporosis risk. Partitioning around medoids (PAM) clustering identified the high- and low-exposure groups, and their association with the risk and prognosis of osteoporosis was evaluated. Results: WQS regression identified Cd as a significant osteoporosis risk factor in the general population (odds ratio (OR) = 1.19, 95% confidence interval (CI): 1.08, 1.31, weight = 0.66). Pb notably affected those individuals aged 30–49 years and classified as Mexican American, while Sb impacted Black individuals. PAM clustering showed that the high-exposure group had a significantly higher risk of osteoporosis (OR = 1.74, 95% CI: 1.43, 2.12) and cumulative mortality risk. Conclusions: Urinary metals are associated with the risk and prognosis of osteoporosis. Full article

Background: Emerging studies demonstrate that exposure to brominated flame retardants (BFRs) can have harmful effects on human health. Our study focused on the relationship between exposure to various BFRs and markers of liver function. Methods: To further explore the association between BFR exposure and liver function impairment, we used data from the National Health and Nutrition Examination Surveys (NHANES) for three cycles from 2009 to 2014, leaving 4206 participants (≥20 years of age) after screening. Nine BFRs and eight liver function tests (LFTs) were measured in the participants’ serum to represent BFRs and liver function impairment in vivo. To investigate whether there is a relationship between BFRs and health outcome, statistical research methods such as the weighted linear regression model, restricted cubic spline (RCS), weighted quantile sum (WQS), quantile-based g computing (QGC), and the Bayesian Kernel Machine Regression (BKMR) were used to evaluate the correlation between serum BFRs and LFTs. Results: The studies reveals that exposure to BFRs is associated with liver function biomarkers. In a weighted linear regression model, we found that PBB153, PBDE99, PBDE154, PBDE209, PBDE85 exposure was positively correlated with AST, ALT, GGT, ALP, TP, and SL risk. In RCS model, the nonlinear relationships between PBB153 and AST, ALT, and GGT and PBDE209 and ALT and TP are the most significant. The exposure to combined BFRs was positively correlated with AST, ALT, and GGT in WQS and QGC models. BKMR analysis showed that BFR exposure was positively correlated with AST, ALT, ALP, and GGT. Conclusions: Exposure to BFRs is associated with liver function impairment, suggesting that BFR exposure is potentially toxic to the human liver, but more in-depth studies are needed to explore this correlation. Full article

Background: It is essential to identify the risk factors for poor clinical outcomes in patients with acute myocardial infarction (AMI). The coronary artery calcium score (CACS) is gathering attention as a predictor for future cardiovascular events. This study aimed to (1) measure CACSs in patients with AMI by non-ECG-gated computed tomography (CT), (2) compare clinical outcomes between patients with a high CACS and a low–intermediate CACS and (3) to elucidate the association between high CACS and clinical outcomes. Methods: We defined the high CACS group as the highest quantile of CACS (Q4) and defined the low–intermediate CACS group as the other quantiles of CACS (Q1–Q3). The primary endpoint was major adverse cardiovascular events (MACE), which were defined as the composite of all-cause death, re-admission for heart failure, non-fatal MI and target vessel revascularization. We included 548 patients with AMI who underwent non-ECG-gated CT and divided them into the high CACS group (CACS ≥ 5346.5, n = 137) and the low–intermediate CACS group (CACS ≤ 5329.3, n = 411). Results: During the median follow-up duration of 535 days, 150 MACE were observed. The Kaplan–Meier curves showed that MACE occurred more frequently in the high CACS group than in the low–intermediate CACS group (p < 0.001). Multivariable Cox hazard analysis revealed that a high CACS was significantly associated with MACE (hazard ratio 1.597, 95% confidence interval 1.081–2.358, p = 0.019) after controlling for multiple confounding factors. Conclusions: Clinical outcomes were worse in AMI patients with a high CACS than in those with a low–intermediate CACS. A high CACS was significantly associated with MACE in multivariate analysis. Full article

In this study, we investigate the volatility spillover effects across uncertainty indices (Infectious Disease Equity Market Volatility Tracker (IDEMV) and Geopolitical Risk Index (GPR)), carbon emissions, crude oil, natural gas, and green assets (green bonds and green stock) under extreme market conditions based on the quantile connectedness approach. The empirical findings reveal that the total and directional connectedness across green assets and other variables in extreme market conditions is much higher than that in the median, and there is obvious asymmetry in the connectedness measured at the extreme lower and upper quantiles. Our findings suggest that the uncertainty caused by COVID-19 has a more significant impact on green assets than the uncertainty related to the Russia–Ukraine war under normal and extreme market conditions. Furthermore, we discover that the uncertainty indices are more important in predicting green asset volatility under extreme market conditions than they are in the normal market. Finally, we observe that the dynamic total spillover effects in the extreme quantiles are significantly higher than those in the median. Full article

Sustainable management in high-tech enterprises is a key aspect of successfully operating modern companies, especially under conditions of risk and uncertainty. This study reviews the field of sustainable management and interval analysis and identifies the main trends and challenges facing high-tech enterprises in the modern world. This study emphasizes the importance of applying interval analysis in making strategic decisions and developing sustainable business models that can adapt to variable environments. This paper presents empirical data, illustrating the practical application of interval analysis tools in the management in high-tech enterprises. It analyzes the effectiveness and potential of this approach to increase the levels of sustainability and competitiveness of organizations in constantly changing business environments. In general, this article is a valuable contribution to the development of sustainable management theory and practice for high-tech enterprises, enriching the existing knowledge in this area and offering new perspectives for research and practical application. Our research has been validated and is presented in the results section. The purpose of this study is to present current developments in methodologies and tools for risk measurement within the probabilistic paradigm of uncertainty, which are supposed to be used in relation to the economic evaluation of real investment projects. The methodological directions or approaches to risk measurement formed in this context are (1) based on quantile measures, within which the quantitative aspect of risk is modeled using quantile quantiles of the distribution of a random variable describing the possible (predicted) results of economic activity; (2) the Monte Carlo method, which is a tool for evaluating the indicators of economic efficiency and risk in justifying real investments, taking into account different distribution laws and mutual relations for the financial and economic parameters of the investment project, as well as its computational and instrumental elaboration. Full article

The main objective of the paper is to assess the relationship between firms’ financial resilience and firms’ strategic sustainable development vulnerabilities, in the context of implications of the COVID-19 pandemic on firms’ business environment. Background: The last decade has emphasized an increase in business models’ uncertainty and risk exposure. The COVID-19 pandemic has highlighted the awareness in this direction, especially in a changing context, that looks more and more for corporate sector operations’ orientation towards sustainable development. The question we would address in this paper is how the nexus between corporate sustainability performance and corporate financial resilience is affected by management decision through process improvements, product quality assurance, or managers’ preference to improve corporate financials by earnings management practice instead, especially in the context of specific corporate financial risk management. Methods: The data are extracted from the Refinitiv database. The sample is limited to 275 European Union listed firms, selected based on data availability. The empirical analysis consists of an OLS multiple regression. For robustness purposes, a quantile regression model is estimated as well. Results: The approach considers implications of the pandemic on firms’ business environment and earnings management accounting based policies and strategies as well. The result suggests that alignment to sustainability frameworks lead to the deterioration of firms’ financial resilience. Similar results show the negative impact of firms’ financial vulnerability (credit default risk) on firms’ financial resilience. Instead, the risk of bankruptcy, firms’ liquidity, or high product quality and business process improvement determine the positive impact on firms’ financial resilience. Conclusions: The study highlights several insights both for management and policy makers. First, the results underline the relevance of management’s choice for earnings management on ensuring firms’ financial resilience, which ask for better corporate governance and high-quality and effective institutional regulatory and enforcement mechanisms. Second, the paper brings evidence on the impact of the COVID-19 pandemic on firms’ financial sustainable development. Third, the study emphasizes the importance of the efforts of corporate process improvements and high-quality products on generating value-add, by looking on the relevance of those drivers on the level of corporate economic value-add, a measure that limits the impact of discretionary management accrual-based accounting choices on our discussion. Full article

Perfluoroalkyl and polyfluoroalkyl substances (PFASs), widely utilized in various industries, may pose potential reproductive well-being risks. However, the research on the impact of PFAS exposures on pregnancy and live birth rates remains scarce. To address this gap, we conducted a cross-sectional study using the data from the United States National Health and Nutrition Examination Survey (NHANES) collected between 2013 and 2018. We focused on six PFAS compounds measured in the serum of women aged 20 to 50 years, employing the Poisson regression, Quantile G-composition (Qgcomp), and Weighted Quantile Sum (WQS) regression models. Adjusting for age, racial/ethnic origin, educational level, marital status, family income, body mass index (BMI), menarche age, birth control pill use, and other female hormone consumption, the Poisson regression identified significant negative associations between the individual PFAS exposures and pregnancy and live birth numbers (p < 0.05 for all 24 null hypotheses for which the slope of the trend line is zero). The Qgcomp analysis indicated that a one-quartile increase in the mixed PFAS exposures was associated with reductions of 0.09 (95% CI: −0.15, −0.03) in the pregnancy numbers and 0.12 (95% CI: −0.19, −0.05) in the live birth numbers. Similarly, the WQS analysis revealed that a unit increase in the WQS index corresponded to decreases of 0.14 (95% CI: −0.20, −0.07) in the pregnancy numbers and 0.14 (95% CI: −0.21, −0.06) in the live birth numbers. Among the six specific PFAS compounds we studied, perfluorononanoic acid (PFNA) had the most negative association with the pregnancy and live birth numbers. In conclusion, our findings suggest that PFAS exposures are associated with lower pregnancy and live birth numbers among women of reproductive age. Full article