Search Results (14,398)

This study aimed to examine the association between physical activity and individuals’ health status, healthcare utilization, socio-demographic characteristics, and health behaviors in a large representative sample from Northern Greece. A cross-sectional study was conducted involving 1227 participants (47.4% males, mean age 49.94 ± 14.87 years) from Thrace, Greece, selected through a two-stage stratified sampling method. According to the Greek version of IPAQ, participants were classified as inactive/insufficiently active, sufficiently and highly active. Data on socio-demographic, lifestyle, and health-related variables were collected through structured interviews. Multivariate logistic regression analysis was performed to determine the independent effect of physical activity on subjects’ characteristics using SPSS ver. 19. Half of the participants (49.8%) were inactive/insufficiently active, 418 participants (34.1%) were sufficiently active, and 198 participants (16.1%) were highly active. In univariate analysis, smoking (p < 0.001), higher coffee consumption (p = 0.002), higher adherence to Mediterranean diet (p < 0.001), napping during the day (p = 0.017) and short sleep duration (p < 0.001) were associated with lower prevalence of high activity. In adjusted analyses, sufficiently active participants had a lower risk for bad self-rated health (aOR = 0.63), hypertension (aOR = 0.41), dyslipidemia (aOR = 0.42), diabetes (aOR = 0.53), obesity (aOR = 0.61), cardiovascular diseases (aOR = 0.43), anxiety (aOR = 0.65), depression (aOR = 0.56), daily sleepiness (aOR = 0.62), poor sleep quality (aOR = 0.71), as well as for primary (aOR = 0.54) and secondary (aOR = 0.40) healthcare utilization compared to inactive participants. Higher-intensity physical activity did not enhance these beneficial effects of sufficient activity on subjects’ characteristics. Physical inactivity significantly compromises health across multiple domains. Promoting even moderate-intensity physical activity may reduce chronic disease burden and healthcare utilization. Full article

Accurate early diagnosis and precise assessment of disease severity are imperative for the treatment and rehabilitation of schizophrenia patients. To achieve this, we propose a computer-aided diagnostic method for schizophrenia that utilizes fusion features derived from microstate analysis and empirical mode decomposition (EMD) based on Electroencephalography (EEG) signals. At the same time, the obtained fusion features from microstate analysis and EMD are input into the Least Absolute Shrinkage and Selection Operator (LASSO) feature selection algorithm to reduce the dimensionality of feature vectors. Finally, the reduced feature vector is fed to a Logistic Regression classifier to classify SCH and healthy EEG signals. In addition, the ability of the integrated features to distinguish the severity of schizophrenia symptoms was evaluated, and the Shapley Additive Explanations (SHAP) algorithm was used to analyze the importance of the classification features that differentiate schizophrenia symptoms. Experimental results from both public and private datasets demonstrate the efficacy of EMD features in identifying healthy controls, while microstate features excel in classifying the severity of symptoms among schizophrenia patients. The classification evaluation metrics of the fused features significantly outperform those obtained using EMD or microstate analysis features independently. The fusion feature method proposed in this study achieved accuracies of 100% and 90.7% for the classification of schizophrenia in public datasets and private datasets, respectively, and an accuracy of 93.6% for the classification of schizophrenia symptoms in private datasets. Full article

Background: Probable sarcopenia, indicated by low handgrip strength, is a prevalent condition among hospitalized older adults and may reflect broader functional and nutritional decline. Methods: We examined differences in nutritional, functional, and cognitive status between Alzheimer’s clinical syndrome (ACS) patients with probable sarcopenia and those without sarcopenia. A cross-sectional analysis was conducted on 194 hospitalized older adults with ACS. Probable sarcopenia was defined using European Working Group on Sarcopenia in Older People (EWGSOP2) handgrip strength thresholds. Results: Patients with probable sarcopenia (n = 137) had significantly lower Mini-Mental State Examination (MMSE) scores, Geriatric Nutritional Risk Index (GNRI), albumin, hemoglobin, and gait speed compared to those without. After age and sex adjustment, MMSE (p = 0.023), GNRI (p = 0.002), hemoglobin (p = 0.022), albumin (p = 0.003), and gait speed (p < 0.001) remained significantly different. In the sex- and age-adjusted multivariable model (adjusted R² = 0.442), higher nutritional risk (β = 0.26, p = < 0.001), lower MMSE scores (β = 0.17, p = 0.029), polypharmacy (β = –4.20, p = 0.002), and slower gait speed (β = 4.12, p = 0.010) were associated with reduced handgrip strength. In the multivariable binary logistic regression model (adjusted for age and sex), moderate or high nutritional risk and slow gait speed emerged as independent predictors of probable sarcopenia, with OR 5.14 (95% CI 1.34–19.75; p = 0.017) and OR 3.13 (95% CI 1.30–7.52; p = 0.011), respectively. Conclusions: Probable sarcopenia in hospitalized older adults with ACS is highly prevalent and is associated with higher nutritional risk, poorer cognitive and physical function, and polypharmacy; its early recognition may help to guide more targeted nutritional and functional interventions. Full article

Non-communicable diseases, particularly cardiovascular diseases (CVD) and metabolic syndrome (MS), remain a major challenge for primary health care (PHC). This study aimed to assess cardiometabolic risk and health behaviours in adult PHC patients using routine preventive screening. This prospective observational study included 506 adults attending routine consultations in an urban PHC centre in Poland. Preventive assessment included anthropometric measurements (body weight, height, BMI, and waist circumference), blood pressure, lipid profile, and fasting glucose levels. Health behaviours were recorded using the standardised NFZ CHUK questionnaire. The 10-year CVD risk was estimated using the SCORE2 algorithm. Multivariable logistic regression was used to identify independent factors associated with high cardiovascular risk (SCORE2 ≥ 5%) and of a composite endpoint defined as the presence of any non-optimal biochemical parameter. Nearly half of the participants had excess body weight (overweight or obesity), and more than half met criteria for central obesity. Borderline or elevated total cholesterol was found in 47% of patients, abnormal LDL in 27%, low HDL-C (<40 mg/dL) in 80% (84% when applying sex-specific cut-offs), and impaired fasting glucose or diabetes in about 12%. High SCORE2 risk (≥5%) was observed in approximately 9% of the cohort. In multivariable models, SCORE2 components (age, sex, and smoking) were, as expected, associated with high SCORE2 risk, and obesity (BMI ≥ 30 kg/m²)—a factor not included in SCORE2—was additionally associated with higher risk. Additionally, age, male sex, and obesity also predicted the presence of at least one non-optimal biochemical marker. The prevalence of high SCORE2 risk increased from 1.2% in patients with 0–1 modifiable risk factor to 25.7% in those with 4–5 factors. Lower educational attainment was associated with a higher proportion of high-risk individuals in univariate analysis. Routine preventive activities in PHC enable the identification of important lipid and glucose abnormalities and the clustering of modifiable risk factors, even in a relatively young, highly educated population. Systematic cardiovascular screening and a focus on patients with accumulated risk factors should remain a priority in PHC to enable early identification of high-risk patients and timely implementation of lifestyle and therapeutic interventions. Full article

Accurate and interpretable detection of pulmonary emphysema on low-dose computed tomography (LDCT) remains a critical challenge for large-scale screening and population health studies. This work proposes a quality-controlled and interpretable deep learning pipeline for emphysema assessment using ResNet-152 embeddings. The pipeline integrates automated lung segmentation, quality-control filtering, and extraction of 2048-dimensional embeddings from mid-lung patches, followed by analysis using logistic regression, LASSO, and recursive feature elimination (RFE). The embeddings are further fused with quantitative CT (QCT) markers, including %LAA, Perc15, and total lung volume (TLV), to enhance robustness and interpretability. Bootstrapped validation demonstrates strong diagnostic performance (ROC-AUC = 0.996, PR-AUC = 0.962, balanced accuracy = 0.931) with low computational cost. The proposed approach shows that ResNet embeddings pretrained on CT data can be effectively reused without retraining for emphysema characterization, providing a reproducible and explainable framework suitable as a research and screening-support framework for population-level LDCT analysis. Full article

Background/Objectives: Insulin resistance and ambulatory blood pressure monitoring (ABPM) abnormalities represent distinct but interrelated pathways contributing to cardiovascular risk. The triglyceride–glucose (TyG) index reflects metabolic burden, whereas arterial load—captured through arterial stiffness, blood pressure variability, and morning surge—reflects hemodynamic instability. Whether the coexistence of these domains identifies a particularly high-risk ambulatory phenotype remains unclear. To evaluate the independent and combined effects of metabolic burden (TyG) and arterial load on circadian blood pressure pattern and short-term systolic blood pressure variability. Methods: This retrospective cross-sectional study included 294 adults who underwent 24 h ABPM. Arterial load was defined using three ABPM-derived indices (high AASI, high SBP-ARV, high morning surge). High metabolic burden was defined as TyG in the upper quartile. The “double-high” phenotype was classified as high TyG plus high arterial load. Primary and secondary outcomes were non-dipping pattern and high SBP variability. Multivariable logistic regression and Firth penalized models were used to assess independent associations. Predictive performance was evaluated using ROC analysis. Results: The double-high phenotype (n = 15) demonstrated significantly higher nighttime SBP, reduced nocturnal dipping, and markedly elevated BP variability. It was the strongest independent predictor of non-dipping (adjusted OR = 42.0; Firth OR = 11.73; both p < 0.001) and high SBP variability (adjusted OR = 41.7; Firth OR = 26.29; both p < 0.001). Arterial load substantially improved model discrimination (AUC = 0.819 for non-dipping; 0.979 for SBP variability), whereas adding TyG to arterial load produced minimal incremental benefit. Conclusions: The coexistence of elevated TyG and increased arterial load defines a distinct hemodynamic endotype characterized by severe circadian blood pressure disruption and exaggerated short-term variability. While arterial load emerged as the principal determinant of adverse ambulatory blood pressure phenotypes, TyG alone demonstrated limited discriminative capacity. These findings suggest that TyG primarily acts as a metabolic modifier, amplifying adverse ambulatory blood pressure phenotypes predominantly in the presence of underlying arterial instability rather than serving as an independent discriminator. Integrating metabolic and hemodynamic domains may therefore improve risk stratification and help identify a small but clinically meaningful subgroup of patients with extreme ambulatory blood pressure dysregulation. Full article

The integration of biomethane into the natural gas infrastructure is a critical element of energy-sector decarbonization, yet optimal infrastructure development scenarios remain insufficiently compared using unified decision frameworks. This study evaluates three biomethane market integration scenarios—direct connection to the gas system, biomethane injection points (compressed biomethane transported by trucks to the gas system), and off-grid delivery using the multi-criteria decision-making method TOPSIS. Environmental, economic, and technical dimensions are jointly assessed. Results indicate that direct connection to the system provides the most balanced overall performance, achieving the highest integrated score (Ci = 0.70), driven by superior environmental and technical characteristics. Biomethane injection points demonstrate strong economic advantages (Ci = 0.49), particularly where capital investments need to be reduced or there is limited access to the gas system, but show weaker environmental and technical performance. Off-grid solutions perform poorly in integrated assessment (Ci = 0.00), reflecting limited scalability and high logistical complexity, although niche applications may remain viable under specific conditions. Sensitivity analysis confirms the robustness of these rankings across a wide range of weighting assumptions, strengthening the reliability of the findings for policy and infrastructure planning. This study provides one of the first integrated multi-criteria assessments explicitly incorporating virtual pipeline logistics, offering a transferable decision-support framework for sustainable biomethane development in diverse regional contexts. Full article

Objectives: This study aimed to compare the predictive performance of the Korean Triage and Acuity Scale (KTAS) and the National Early Warning Score (NEWS) for serious adverse events (SAEs), including mortality and intensive care unit (ICU) admission, during emergency department (ED) stay. We also evaluated whether combining the two systems improves prediction accuracy. Methods: This retrospective study included adult patients (≥19 years) who presented to a university-affiliated ED between October and December 2024. KTAS and NEWS were assessed simultaneously at triage. NEWS2 was calculated retrospectively based on routinely documented vital signs and medical history without performing routine arterial blood gas analysis. The primary outcome was the occurrence of SAE during the ED stay. Predictive performance was evaluated using receiver operating characteristic (ROC) curves and the area under the curve (AUC), and logistic regression models were used to identify independent associations. Results: A total of 4216 patients were analyzed, of whom 255 (6.0%) experienced SAEs. All three scores—KTAS, NEWS and NEWS2—were independently associated with the occurrence of SAEs. The AUCs for KTAS, NEWS and NEWS2 were 0.75 (95% CI, 0.74–0.76), 0.73 (95% CI, 0.71–0.74) and 0.73 (95% CI, 0.71–0.74), respectively. Combining KTAS with NEWS or NEWS2 significantly improved predictive accuracy (AUC 0.81, 95% CI 0.79–0.82; p < 0.001). Conclusions: Both KTAS and NEWS/NEWS2 reliably predicted in-ED adverse outcomes, and their combination further enhanced prognostic performance. Integrating physiology-based early warning scores with structured triage systems may help identify high-risk ED patients earlier and optimize resource allocation. Full article

Introduction: The global prevalence of chronic kidney disease (CKD) is increasing and it is associated with higher mortality rates. Transforming growth factor-beta (TGF-β) can serve as a novel biomarker for early prediction of chronic kidney disease (CKD) progression. Methods: This was a prospective longitudinal study among black patients with CKD who attended the Charlotte Maxeke Johannesburg Academic Hospital between September 2019 and March 2022. Patients provided urine and blood samples for laboratory investigations at study entry (0) and at 24 months follow up. Baseline serum and urine TGF-β1, TGF-β2 and TGF-β3 levels were measured using ELISAs. Multivariable logistic regression analysis was utilized to determine if TGF-β isoforms could predict CKD progression. Results: A total of 312 patients were enrolled at baseline, of whom 275 (88.1%) had early-stage CKD (Stage 1–3). A majority, 95.2% (297/312), of the patients completed the study after 2 years follow up. The prevalence of CKD progression was 47.8% when measured by a sustained decline in eGFR of >4 mL/min/1.73 m²/year or more and 51.9% when measured by a change in uPCR > 30%. The patients with CKD progression had significantly lower eGFR and increased urine protein–creatinine ratios compared to non-progressors. Furthermore, comparing progressors with non-progressors, the median serum TGF-β1 was 21210 (15915–25745) ng/L vs. 24200 (17570–29560) ng/L and the median urine TGF-β3 was 17.5 (5.4–76.2) ng/L vs. 2.8 (1.8–15.3) ng/L, respectively. Baseline serum and urine TGF-β isoforms were unable to discriminate between CKD progressors and non-progressors after multivariable logistic regression analysis. Conclusions: Despite the multiple roles of TGF-β isoforms in kidney disease, baseline levels were not predictive of chronic kidney disease progression. Full article

Background: Viral respiratory tract infections (RTIs) frequently lead to emergency department (ED) presentations and hospital admissions, particularly among older adults and individuals with underlying health conditions. Identifying patients at increased risk for hospitalization is essential for optimizing triage and resource allocation. This study aimed to determine independent demographic, clinical, and virological predictors of hospital admission among adults presenting with confirmed viral RTIs. Methods: A retrospective cohort study was conducted at a tertiary hospital between September 2022 and May 2024. Adult patients with molecularly confirmed viral RTIs were included. Demographic, clinical, and microbiological data were extracted from electronic medical records. Predictors of admission were assessed using univariate and multivariate logistic regression. Results: Among 311 patients, 147 (47.3%) required hospitalization. Hospitalized patients were significantly older and more likely to present with fever, cough, tachypnea, dyspnea, chest pain, comorbidities, and lower or mixed respiratory tract infections (all p < 0.001). In multivariate analysis, older age, fever, cough, and lower or mixed RTIs were strong independent predictors of admission. Several viral pathogens, including human rhinovirus, non–SARS-CoV-2 coronaviruses, influenza A, and parainfluenza virus, were associated with reduced odds of hospitalization. Conclusions: Age, comorbidity burden, and lower respiratory tract involvement are key determinants of hospitalization in viral RTIs. Integrating clinical and virological data may improve risk stratification and guide ED triage during seasonal and emerging respiratory virus activity. Full article

Background/Objectives: The transperineal (TP) approach has progressively replaced the transrectal (TR) approach for prostate biopsy because of its improved safety profile. However, its impact on the detection of clinically significant prostate cancer (csPCa), particularly within modern lesion-focused biopsy strategies that combine targeted and perilesional sampling, remains uncertain. We aimed to evaluate the real-world diagnostic impact of transitioning from a TR systematic-based biopsy strategy to a TP lesion-focused approach. Methods: We conducted a retrospective single-centre study including consecutive men who underwent image-guided prostate biopsy between 2018 and 2025. Only patients with a single MRI-visible lesion (PI-RADS ≥ 3) were included. Two biopsy strategies were compared: TR systematic biopsy (TR–SBx), combining targeted and systematic cores, and TP lesion-focused biopsy (TP–LFx), combining targeted and perilesional cores. The primary outcome was the detection of csPCa (Gleason Grade Group ≥ 2). Secondary outcomes included detection of Gleason Grade Group 1 cancer and negative biopsies. Inverse probability of treatment weighting (IPTW) based on a propensity score was applied to adjust for baseline differences. Doubly robust weighted logistic regression models were used, with predefined subgroup and sensitivity analyses. Results: Among 1032 included patients, 931 underwent TR–SBx and 101 TP–LFx. After restriction to the region of common support, 528 patients were retained for IPTW analyses. In the IPTW-adjusted analysis, TP–LFx was associated with higher csPCa detection compared with TR–SBx (adjusted odds ratio [OR] 2.52, 95% confidence interval [CI] 1.40–4.52; p = 0.002) and with lower detection of Gleason Grade Group 1 cancer (OR 0.50, 95% CI 0.27–0.92; p = 0.03). Subgroup analyses suggested a stronger association in patients with prior negative biopsy and in anterior or apical lesions. Conclusions: In routine clinical practice, transitioning from a transrectal systematic-based biopsy strategy to a transperineal lesion-focused approach was associated with improved detection of csPCa and reduced overdiagnosis. These findings support the consideration of transperineal, lesion-focused MRI-guided biopsy strategies in contemporary prostate cancer diagnostics. Full article

Background: Pheochromocytoma is a rare adrenal neuroendocrine tumor characterized by excessive catecholamine secretion, which can lead to significant perioperative hemodynamic instability. Despite advances in anesthetic and surgical management, intraoperative hypotension is a common complication. This study aimed to identify preoperative and intraoperative predictors of hemodynamic instability during adrenalectomy for pheochromocytoma in order to improve intraoperative management and patient safety. Methods: This retrospective study included adult patients who underwent adrenalectomy for pheochromocytoma at the University Clinical Center of Serbia between January 2022 and June 2025. Preoperative clinical and biochemical data, tumor characteristics evaluated by imaging methods (CT or MRI), surgical approach, and intraoperative hemodynamic parameters were analyzed. Intraoperative hypotension was defined as mean arterial pressure (MAP) < 60 mmHg despite adequate volume resuscitation. Univariate and multivariate logistic regression analyses were performed to identify predictors of hypotension. Results: A total of 51 adult patients were included in the analysis. Intraoperative hypotension occurred in 26 patients (51%) and was significantly associated with larger tumor size and increased intraoperative fluid requirements. Multivariate analysis identified tumor diameter ≥ 49 mm (OR 0.176, 95% CI 0.034–0.895, p = 0.036) and intraoperative crystalloid infusion ≥ 1200 mL/h (OR 0.132, 95% CI 0.030–0.574, p = 0.007) as independent predictors of intraoperative hypotension. Preoperative catecholamine levels, surgical approach, and type of alpha-blocker were not significant predictors. Conclusions: Tumor size was identified as a significant predictor of intraoperative hemodynamic instability during adrenalectomy for pheochromocytoma. Careful preoperative assessment and individualized intraoperative fluid management may help reduce the risk of hypotension and optimize perioperative outcomes. Full article

This study aimed to evaluate the prediction potential of phenology metrics from two vegetation indices using Sentinel-2 images, the Normalized Difference Vegetation Index (NDVI) and Three Red-Edge Vegetation Index (NDVI3RE), for maize yield prediction. Ground truth maize yield samples were collected near Koška, Croatia, on 13 October 2023, using a Quantimeter yield mapping sensor on Claas Lexion 6900 combine harvester. The phenology analysis was performed based on a time-series of all available Sentinel-2 images during 2023, using the Beck logistic model for determining the start of season (SOS), peak of season (POS), end of season (EOS), greenup, maturity, senescence, and dormancy. A total of fourteen covariates, including vegetation indices at phenology metrics and their occurrence dates, were used for machine learning prediction of maize yield using Random Forest (RF) and Support Vector Machine (SVM) regression. The results suggested that the SVM method based on NDVI phenology metrics produced the highest accuracy for maize yield prediction (R² = 0.935, RMSE = 0.558 t ha⁻¹, MAE = 0.399 t ha⁻¹). Vegetation index values at greenup, dormancy and POS were the most important covariates for the prediction, while day of year (DOY) in which they occurred had only a minor effect on the prediction accuracy. This suggests that, despite its limitations regarding the saturation effect, NDVI outperformed NDVI3RE for maize yield prediction when combined with phenology metrics. Full article

Background/Objectives: Hypertension control remains a global challenge. Evidence on the association between adherence to the Dietary Approaches to Stop Hypertension (DASH) diet and blood pressure (BP) control in older Mediterranean populations is limited. We aimed to assess this association in Spanish older adults. Methods: This cross-sectional analysis included 371 participants (69 ± 9 years). Dietary intake was assessed using a validated 146-item food frequency questionnaire (FFQ), and DASH diet adherence was categorized as low, medium, or high. Multivariable logistic regression models were used to examine associations with BP control. Results: Among participants with hypertension (n = 218), 52.8% achieved adequate BP control and consumed significantly more low-fat dairy products (+56%) and less sodium (−11%) than those with uncontrolled BP. The low adherence group had lower proportion of participants with controlled BP (21%) than the medium and high adherence groups (36% and 39%, respectively) (p <0.05). Across increasing DASH diet adherence categories, systolic blood pressure (SBP) and diastolic blood pressure (DBP) were 4–5 mmHg and 3–4 mmHg lower, respectively. Medium adherence to the DASH diet was independently associated with substantially lower odds of uncontrolled BP (OR = 0.37; 95% CI: 0.16–0.82; p = 0.015). High adherence showed a similar magnitude of association but did not reach statistical significance. Conclusions: In this cohort of older Spanish adults, moderate adherence to the DASH diet was associated with meaningful improvements in BP control, suggesting that achievable, intermediate levels of DASH diet adherence may be sufficient to improve hypertension management in real-world settings. Longitudinal studies are needed to confirm causality and long-term cardiovascular benefits. Full article

This study focuses on classifying electromyographic (EMG) signals to identify seven specific hand movements, including complete hand closure, individual finger closures, and a pincer grip. Accurately distinguishing these movements is challenging due to overlapping muscle activation patterns. To address this, a methodology structured in five stages was developed: placement of electrodes on specific forearm muscles to capture electrical activity during movements; acquisition of EMG signals from twelve participants performing the seven types of movements; preprocessing of the signals through filtering and rectification to enhance quality, followed by the extraction of features from three distinct types of preprocessed signals—filtered, rectified, and envelope signals—to facilitate analysis in the temporal, frequency, and time–frequency domains; extraction of relevant features such as amplitude, shape, symmetry, and frequency variance; and classification of the signals using eight machine learning algorithms: support vector machine (SVM), multiclass logistic regression, k-nearest neighbors (k-NN), Bayesian classifier, artificial neural network (ANN), random forest, XGBoost, and LightGBM. The performance of each algorithm was evaluated using different sets of features derived from the preprocessed signals to identify the most effective approach for classifying hand movements. Additionally, the impact of various signal representations on classification accuracy was examined. Experimental results indicated that some algorithms, especially when an expanded set of features was utilized, achieved improved accuracy in classifying hand movements. These findings contribute to the development of more efficient control systems for myoelectric prostheses and offer insights for future research in EMG signal processing and pattern recognition. Full article