Search Results (5,498)

The associations of pet ownership and well-being have been widely discussed, but previous studies have shown inconsistent results, often due to their limited sample size and diversity. We analyzed data from a nationally representative survey conducted by the Cabinet Office of Japan in 2019 (n = 10,293; age range = 15–89 years; 50.4% female). Ownership of co-walkable pets (e.g., dogs), indoor-only pets (e.g., cats), and ornamental pets (e.g., tropical fish) was examined as a predictor. Well-being was measured using eleven domains based on the OECD Better Life Index. Demographic covariates were included. Of the participants, 13.3% owned a co-walkable pet, 13.0% an indoor-only pet, and 6.8% an ornamental pet. The pet owners were more likely to live in a house they owned and have a larger floor area, higher income, and greater debt. The non-pet owners were more likely to live alone. In the unadjusted models, the ownership of co-walkable pets was associated with higher well-being in terms of housing and community. However, in the adjusted models, the ownership of co-walkable pets was associated with lower well-being in terms of income, jobs, environment, and, marginally, safety. No significant associations were found for indoor-only and ornamental pet ownership. In Japan, pet ownership requires both financial resources and adequate living space. It is also important to note that pet owners who go outside for walking their animals may also find that their environmental and economic circumstances are less satisfying. Full article

Background/Objectives: The objective of this ten-year report is to describe temporal trends in the cardiovascular health (CVH) score and its individual components across ages and sexes. We also examined the impact of the post-COVID-19 period on ideal CVH and identified demographic predictors of favorable cardiovascular risk profiles. Methods: Data for this cross-sectional study were collected between 2015 and 2024 as part of the Lookup 8+ project, an ongoing initiative integrating field-based CVH assessments across Italy. CVH was operationalized using a modified CVH score (0–7 points) inspired by Life’s Simple 7, combining behavioral and clinical metrics. Trends over time and across demographic groups were examined using descriptive statistics and multivariable models adjusted for age, sex, and year of assessment. Results: The study included 18,491 participants (mean age 56.1 ± 14.8 years; 55.2% women). After an initial decline in CVH score between 2015 and 2017 (mean score from 4.39 to 3.95), a gradual improvement followed, reaching 4.41 in 2024. Younger adults (18–39 years; 71.9% in 2024) and women (56.8%) consistently showed the highest prevalence of ideal CVH (score ≥ 5). The post-COVID-19 period was independently associated with higher odds of ideal CVH (OR 1.32; 95% CI 1.24–1.40). While blood pressure and cholesterol metrics improved, dietary quality and glycemic control worsened over time. Conclusions: From 2015 to 2024, overall CVH improved among Lookup participants, particularly among younger individuals after the COVID-19 pandemic. However, substantial age- and sex-related gaps remain, requiring targeted and equity-oriented prevention efforts. Full article

Long COVID is frequently accompanied by neurocognitive complaints, yet the combined effects of demographic and clinical factors remain unclear. This study examined individuals six months after their most recent SARS-CoV-2 infection using a Demographic/Infection-History form, a Physical and Neurocognitive Symptom Checklist (binary), and the Post-COVID Cognitive Impairment Scale (memory, attention; 5-point Likert). Participants were recruited through convenience sampling from multiple community and online sources. Inclusion criteria required confirmed prior COVID-19 infection, self-perceived or clinically documented Long COVID symptoms, and no history of neurological or severe psychiatric disorders. The final sample consisted of 212 participants (mean age = 39.7 years, SD = 10.5), of whom 67.9% were female, and most held a master’s (35.4%) or bachelor’s (28.3%) degree. Difficulties with retaining new information (57.8%) and concentrating (52.1%) were the most frequent neurocognitive complaints, while severe fatigue after mild activity (23.2%) and chronic fatigue (22.7%) were the most common physical symptoms. Confusion and decision-making difficulty were more frequent among younger participants; women reported greater difficulty retaining new information, and difficulty concentrating varied by education level. A multivariable regression model explained 7% of the variance in total cognitive complaints, identifying education level (β = −0.18, p < 0.01) and number of physical symptoms (β = 0.19, p < 0.01) as significant predictors. Higher educational attainment was associated with fewer cognitive complaints, whereas a greater burden of physical symptoms predicted higher complaint scores. Persistent cognitive difficulties in Long COVID appear closely related to physical symptom burden and protective factors such as education, rather than to infection frequency or sensory dysfunction duration. Findings highlight the need for routine cognitive screening, fatigue-focused management, and longitudinal multimodal research to elucidate underlying mechanisms and recovery pathways. Full article

This study investigates the relationship between ESG ratings and a firm’s financial performance, focusing on Return on Assets (ROA) and Return on Equity (ROE). Using a combination of stepwise linear regression and feedforward neural networks (FFNN), we assess both the linear and nonlinear effects of ESG on financial performance. The regression models identify ESG as a significant, positively correlated factor in explaining financial performance, alongside firm demographics, sector affiliation, and financial indicators. Neural networks reveal nonlinear dynamics, particularly for ROA, suggesting threshold effects in the ESG–performance relationship. Sensitivity analysis confirms that ESG’s influence strengthens at higher values. Our findings highlight that ESG is not only statistically relevant but also interacts with firm characteristics in complex ways. These results contribute to the ongoing discourse on sustainable finance by showing that ESG can be a meaningful driver of financial outcomes, especially when modeled through nonlinear approaches. Full article

Mountainous and sparsely populated regions in Europe—here called “cold lands”—are particularly exposed to global megatrends such as climate change, demographic shifts, and economic restructuring. Addressing these interconnected challenges requires approaches that link foresight with local governance systems. This study presents a pilot methodological framework that integrates Ostrom’s Social-Ecological Systems (SES) model with the European Commission’s Megatrend Assessment method to support participatory foresight. The framework was tested in two illustrative cases, located in the Italian Alps and Norway, to demonstrate its feasibility and potential value. Through a structured discussion among researchers, key megatrends were prioritised, and qualitative scenarios were developed to explore how community preparedness can influence socio-ecological outcomes. The results highlight climate change, resource scarcity, and demographic imbalances as dominant drivers, while contrasting scenarios illustrate how proactive governance can mitigate vulnerability and foster adaptive capacity. The approach contributes a replicable and scalable foresight tool to bridge global trends and local sustainability strategies, supporting anticipatory and community-based governance in vulnerable territories. Full article

Background/Objective: Elucidation of prognostic factors is key to personalizing management approach for patients with glioblastoma (GBM). In patients who are treated with conventionally fractionated radiotherapy (cvRT), sex and other demographic variables (e.g., income level) were recently found to predict for treatment outcomes. However, it is unknown whether these factors predict for outcomes in elderly or poor performance status patients who receive hypofractionated RT (hyRT). In this study, we assess the association of clinical and non-clinical factors to outcomes in GBM patients treated with hyRT concurrent with temozolomide (TMZ). Methods: The records of 61 adult patients with newly diagnosed GBM consecutively treated at our institution with post-operative hyRT (4005 cGy in 15 daily fractions) and TMZ were retrospectively analyzed. Established clinical variables as well as key demographic variables were compared using chi-squared tests. Kaplan–Meier analyses were used to compare overall survival (OS) and progression-free survival (PFS) between clinical and demographic subgroups. Multivariate modeling was performed using Cox proportional hazards regression. Results: Female and male patients composed 44.3% and 55.7% of the study population, respectively, and did not differ significantly in their clinical or tumor characteristics. Most patients were 65 years or older (85.2%), and over half resided in middle/high-income regions (55.7%) and were privately insured (55.7%). On an univariate analysis, female sex was associated with shorter OS (median 10.0 months vs. 13.3 months in males, p = 0.0224) and PFS (median 3.00 months vs. 4.60 months in males, p = 0.0134). Female sex remained significantly associated with inferior outcomes on multivariate analysis. Income level, type of insurance and marital status were not significantly associated with treatment outcomes. Conclusion: Our study is the first to report sex differences in GBM outcomes following hyRT-TMZ. Contrary to responses following cvRT-TMZ, females appear to have inferior outcomes after hyRT-TMZ versus males. Further investigation is warranted to define the optimal treatment approach for sex subgroups in GBM. Full article

Background and Objectives: Lumbar spinal stenosis (LSS) is a leading cause of disability in older adults, but the role of cigarette smoking in its development remains unclear. This study aimed to clarify the association between smoking and the incidence of LSS, with a focus on dose–response relationships and subgroup variations by age and sex. Methods: We conducted a nationwide, population-based cohort study using the Korean National Health Insurance Service database. A total of 2,123,268 adults aged ≥ 40 years who underwent health screening in 2009 were followed until LSS diagnosis, death, or 2020. Smoking status, duration, daily consumption, and pack-years were assessed. Cox proportional hazards models with progressive adjustment for demographic, lifestyle, and clinical factors were applied. Results: Over a mean follow-up of 8.2 years (17.5 million person-years), 721,909 new cases of LSS were identified. Fully adjusted models showed higher risk in former (HR 1.047; 95% CI, 1.039–1.056) and current smokers (HR 1.052; 95% CI, 1.044–1.060) compared with never smokers. A clear dose–response pattern was observed, with the greatest risk in heavy smokers (≥40 pack-years; HR 1.207; 95% CI, 1.191–1.222). Subgroup analyses indicated stronger associations among adults aged ≥ 65 years and in women. Conclusions: Cigarette smoking was independently associated with an increased risk of LSS, with risk increasing according to lifetime exposure. The findings underscore the importance of smoking cessation strategies to reduce the burden of spinal degeneration, especially in older adults and women. Full article

The growing negative consequences of climate change support the need to deepen and investigate factors that may sustain the engagement of pro-environmental behaviors. In this scenario, eco-emotions represent a key factor that can potentially shape sustainable behaviors. In keeping with this, the present study aimed at observing the potential relationships between eco-emotions and readiness to change (RTC), namely a psychological construct closely related to pro-environmental behaviors. Specifically the RTC dimensions were the following: perceived importance of the problem, motivation, self-efficacy, effectiveness of the proposed solution, social support, action, and perceived readiness. In detail, Generalized Additive Models (GAMs) were performed in order to detect both linear and non-linear associations between eco-emotions and the dimensions of RTC by assuming a complex perspective. The final sample was composed of 252 participants (mean age = 32.99, SD = 14.640). The results pointed out several significant associations (both linear and non-linear) between eco-emotions and the RTC dimensions. In detail, the perceived importance of the problem was linearly associated with anger and anxiety, while sorrow and enthusiasm showed non-linear effects. Furthermore, motivation was linearly linked to anger and guilt and non-linearly to contempt, enthusiasm, and sorrow. In terms of self-efficacy, anger, enthusiasm, and sorrow showed linear relationships, whereas isolation showed a non-linear association. Perceived effectiveness of the proposed solution was linearly related to enthusiasm and sorrow and non-linearly to anger, powerlessness, isolation, and anxiety. Similarly, social support was linearly connected with enthusiasm, isolation, and sorrow, and non-linearly with powerlessness and anxiety. Moreover, action was primarily driven by anger in a linear relationship, while enthusiasm, powerlessness, guilt, and anxiety showed non-linear associations. Finally, perceived readiness was linearly related to anxiety and non-linearly to anger, contempt, enthusiasm, powerlessness, guilt, and sorrow. These findings should be interpreted in light of the study’s limitations, including its cross-sectional nature, reliance on self-reported measures, use of snowball sampling, and sample demographic characteristics, all of which may affect the generalizability of the results. Nevertheless, the results pointed out the presence of several significant linear (e.g., anxiety and the perceived importance of the problem) and non-linear (e.g., contempt and motivation) associations between various eco-emotions and RTC factors. The findings underscore the need for a complex approach to this field of research, suggesting that further studies, policies, and environmental awareness programs should consider the multifaceted nature of these phenomena in order to develop effective and valuable interventions. Full article

Disaster management requires the development of effective technologies for managing both pre-and post-disaster processes. Therefore, utilizing effective tools and techniques to mitigate the disaster risks or lower the adversarial impacts is essential. Over the last decade, digital twin (DT) applications have found a wider implementation area for varying purposes, but most importantly, they are utilized for simulating disaster impacts. This study aims to develop an open-source digital twin (DT) framework for earthquake disaster management in the Cayirova district of Kocaeli, Türkiye, one of the country’s most seismically active regions. The primary objective is to enhance local resilience by integrating multi-source data into a unified digital environment that supports risk assessment, response planning, and recovery coordination. The digital model developed using QGIS (3.40.9 Bratislava), Autodesk InfraWorks 2025 software for DT modeling integrates various data types, including geospatial, environmental, transportation, utility, and demographic data. As a result, the developed model is expected to be used as a digital database for disaster management, storing both geospatial and building data in a unified structure. The developed model also aims to contribute to sustainable practices in cities, where disaster risks are particularly critical. In this respect, the developed model is expected to create sustainable logistics chains and sustainable targets aiming to reduce the number of people affected by disasters, reducing the direct economic losses caused by disasters. In this framework, the developed model is expected to further assess seismic risk and mitigate risks with DTs. These capabilities enable the project to establish an open-source district-level DT system implemented for the first time in Cayirova, provide an alternative disaster model focused on region-specific earthquakes, and integrate 2D/3D assets into an operational, ready-to-respond digital database. In terms of practical importance, the model provides a digital database (digital backup) that can be used in emergencies, helping decision-makers make faster, data-driven decisions. The significance of this study lies in bridging the gap between urban digitalization and disaster resilience by providing a scalable and transparent tool for local governments. Ultimately, the developed DT contributes to sustainable urban management, enhancing preparedness, adaptive capacity, and post-disaster recovery efficiency. Full article

Background: Chronic skin ulcers are characterized by an impaired and delayed wound healing process, posing a major economic and healthcare burden. These multifactorial conditions are influenced by both biological, clinical and psychosocial factors. The aim of our cross-sectional study was to investigate the influence of psychosocial and lifestyle factors—specifically adherence to the Mediterranean diet, emotional health, sleep quality and demographic characteristics—on physical symptoms and clinical severity in patients with skin ulcers, using a multidisciplinary approach to identify key predictors of disease burden. Methods: A cross-sectional analysis was conducted on patients with skin ulcers, using data from a monocentric pathology registry. Collected variables included gender, age, dietary habits (specifically, adherence to the Mediterranean diet), sleep disturbances, educational level, anxiety and depressive symptoms, Physician Global Assessment (PGA), Patient Global Assessment (PtGA), and Skindex-17 (a dermatology-specific quality of life measure). Hierarchic multivariate linear regression models were applied to identify predictors of physical symptoms and clinical severity, while simultaneously controlling for potential confounders. Results: Older age, poorer adherence to the Mediterranean diet, and elevated anxiety levels emerged as the strongest predictors of worse physical symptoms, as measured by the Skindex-17. Male sex and more severe depressive symptoms were significantly associated with higher PGA scores. Our data also suggest older age and poorer Mediterranean diet adherence to influence clinical severity. Lastly, sleep disturbances were also found to correlate with patient-reported severity. Conclusions: Our study underscores the impact of psychosocial and behavioral/lifestyle factors on the clinical burden of skin ulcers through a comprehensive multidisciplinary approach. In particular, our data indicate that dietary patterns and emotional health appear to shape both symptom perception and clinical evaluation, emphasizing the need for holistic management strategies. Full article

Understanding public acceptance is pivotal for integrating automated cars (AC) and shared mobility services (SMS) into mobility systems. This paper presents a holistic framework and demonstrates its application based on a dataset (N = 419; EU-focused sub-sample N = 289) originating from an online survey, capturing metrics like socio-demographics, mobility habits, and perceptions. Acceptance was measured as willingness to use (WTU), and links to willingness to pay (WTP) were examined. A two-stage approach was conducted: non-parametric screening (Chi-square, Spearman’s rank correlation) and proportional-odds ordinal logistic models. Results show that 25.6% would likely use AC and 21.1% would use SMS. WTP for SMS is positively associated with WTU (p < 0.001), whereas WTP and WTU are not statistically related for AC. Perceived usefulness and ease of use are positively related to WTU for both AC and SMS (all p < 0.01). The acceptance of SMS correlates positively with the acceptance of AC (p < 0.001), and the preference for combining SMS with public transport (PT) is associated with higher acceptance. The ordinal logistic models confirm these patterns after adjustment, with perceptions/experience and (for SMS) pricing and PT-related variables remaining significant, while several socio-demographic effects attenuate. The cross-country results indicate modest acceptance in Austria and the UK, aligning with recent European evidence. Full article

Deception detection is considered a concern for all individuals in their everyday lives, as it greatly affects human interactions. While multiple automatic lie detection systems exist, their accuracy still needs to be improved. Additionally, the lack of adequate and realistic datasets hinders the development of reliable systems. This paper presents a new multimodal dataset with physiological data (heart rate, galvanic skin response, and body temperature), in addition to demographic data (age, weight, and height). The presented dataset was collected from 49 unique subjects. Moreover, this paper presents a polygraph-based lie detection system utilizing multimodal sensor fusion. Different machine learning algorithms are used and evaluated. Random Forest has achieved an accuracy of 97%, outperforming Logistic Regression (58%), Support Vector Machine (58% with perfect recall of 1.00), and k-Nearest Neighbor (83%). The model shows excellent precision and recall (0.97 each), making it effective for applications such as criminal investigations. With a computation time of 0.06 s, Random Forest has proven to be efficient for real-time use. Additionally, a robust k-fold cross-validation procedure was conducted, combined with Grid Search and Particle Swarm Optimization (PSO) for hyperparameter tuning, which substantially reduced the gap between training and validation accuracies from several percentage points to under 1%, underscoring the model’s enhanced generalization and reliability in real-world scenarios. Full article

Background: Hepatocellular carcinoma (HCC) frequently develops in patients with chronic hepatitis B and C. Early detection is critical, but current methods, including ultrasound and AFP, have suboptimal accuracy. Objectives: This study aimed to evaluate the predictive performance of protein induced by vitamin K absence or antagonist-II (PIVKA-II) and alpha-fetoprotein (AFP) testing, alone and in combination, for HCC development. Methods: A retrospective cohort study at a single university center included 242 CHB and 181 CHC patients. Data on demographics, clinical status, laboratory parameters, and imaging were collected, with fibrosis and steatosis assessed by FibroScan^®. Serum AFP and PIVKA-II were measured, but measurements of PIVKA-II in patients receiving vitamin K antagonists were excluded from the analysis. HCC diagnosis and staging followed clinical guidelines. Cox regression and ROC analyses identified independent predictors and evaluated biomarker accuracy for HCC detection. Results: HCC incidence was comparable between cohorts (5.0% in CHB vs. 5.5% in CHC). Both AFP and PIVKA-II independently predicted HCC development in multivariate models adjusted for age and sex. The combined biomarker score (AFP × PIVKA-II) showed superior predictive accuracy with hazard ratios of 1.38 (CHB) and 1.36 (CHC). ROC analyses demonstrated high discriminative ability for PIVKA-II (AUC ~0.81) and AFP (AUC ~0.83) in both cohorts. Additional independent predictors were chronic alcohol abuse, cirrhosis, and higher liver stiffness measurements. Specific viral factors such as HBeAg positivity and HCV subgenotype 1b were also associated with increased HCC risk. Conclusions: AFP and PIVKA-II are independent, valuable biomarkers for HCC risk in chronic hepatitis B and C. Combined use improves early detection, aiding timely treatment. These results support adding PIVKA-II to AFP in surveillance, but larger studies are needed to confirm the findings and refine cut-off values. Full article

Background: Opioid use is common among patients hospitalized for inflammatory bowel disease (IBD) exacerbation and has been associated with an increased risk of readmissions. Prior studies, however, have mostly limited their analysis to hospital opioid use. This study examines opioid exposure and dosing before, during, and after hospitalization and its impact on 30- and 90-day hospital readmissions. Methods: We reviewed all adults admitted for an IBD exacerbation from 1 January 2016 to 3 January 2020, excluding pregnant patients and those with an IBD-related surgery. Use and dose of opioids before and during hospitalization and at discharge were identified through manual chart review. IBD type, demographics, and comorbidities were defined. The associations between opioid use characteristics and readmission were assessed using a series of multivariable logistic regression models. Results: Among 1062 patients meeting inclusion criteria, 191 (18.0%) were readmitted within 30 days of their index hospitalization, and 285 (26.8%) were readmitted within 90 days. Of these 1062 patients, 96 (9.02%) had preadmission opioid use, 340 (31.95%) had inpatient use, and 133 (12.50%) received a discharge opioid prescription. Preadmission, inpatient, and discharge opioid use were not associated with 30-day readmission. Preadmission and inpatient opioid use were also not associated with 90-day readmission; however, a prescription for an opioid at discharge was associated with 90-day readmission even after adjusting for confounders, OR 1.86 (1.27, 2.75), p = 0.002. On multivariable analysis, we also found that higher maximum daily dose of discharge opioids, OR 1.01 (1.00, 1.02), p = 0.037, was found to be associated with 30-day readmissions, and higher opioid doses preadmission, OR 1.01 (1.00, 1.03), p = 0.029, and at hospital discharge, OR 1.01 (1.00, 1.02), p = 0.034, were associated with increased 90-day readmission. Conclusions: Opioid prescribing at discharge poses a significant risk for readmissions. Discharge planning should emphasize minimal use of opioids at discharge. Full article

Objective: Partial hip arthroplasty (PHA) procedures are often associated with significant blood loss, particularly in elderly patients with comorbidities. Predicting the need for intraoperative transfusion in advance is crucial for patient safety and surgical planning. Machine learning (ML) algorithms offer data-driven solutions to support clinical decision-making in such scenarios. Methods: This retrospective, single-center cohort study evaluated data from 202 patients who underwent PHA between December 2023 and July 2025. Demographic data, as well as preoperative and intraoperative variables, were collected. Six ML algorithms—Logistic Regression, Decision Tree, Support Vector Machines (SVM), Artificial Neural Network (ANN), Random Forest, and Gradient Boosting—were trained and tested to predict intraoperative blood transfusion. Model performance was assessed using accuracy, F1-score, and area under the ROC curve (AUC). SHAP (SHapley Additive exPlanations) analysis was used to evaluate model interpretability. Results: Among the 202 patients, 85 (42.1%) received intraoperative blood transfusions. Significant predictors included low preoperative hemoglobin, high ASA score, prolonged operative time, increased intraoperative blood loss, and elevated INR (all p < 0.05). The Random Forest and Decision Tree models achieved the highest accuracy (95.1%) and F1-score (0.960), while the SVM model yielded the highest AUC (0.992). SHAP analysis identified hemoglobin, age, ASA score, INR, and operative time as the most influential features in model decision-making. Conclusions: Machine learning models—particularly Random Forest, Decision Tree, and SVM—demonstrated high performance in predicting intraoperative transfusion needs during PHA. The incorporation of explainable AI techniques such as SHAP enhanced the clinical interpretability of model outputs, supporting personalized patient management. These findings provide a strong foundation for integrating such models into clinical decision support systems, though external validation through multicenter and prospective studies is warranted. Full article