Search Results (29,559)

Background/Objectives: Fibromyalgia (FM) is a nociplastic pain disorder marked by altered central nervous system processing and abnormal sensory modulation. Diagnosis remains largely symptom-based and lacks objective biomarkers. Sensory modulation disorder (SMD)—impaired regulation of responses to non-noxious input—may represent a clinically relevant diagnostic dimension. This study aimed to estimate the prevalence/diagnostic value of SMD in FM, examine links with symptom severity and stress, and assess its potential for patient stratification. Methods: In this cross-sectional study, 182 adults were enrolled (104 FM; 78 controls). Standardized instruments included the Adolescent/Adult Sensory Profile (AASP), Fibromyalgia Impact Questionnaire (FIQ), and Perceived Stress Scale (PSS). Group comparisons, regression, and discriminant analyses evaluated SMD profiles. Results: Compared with controls, FM adults showed higher sensory sensitivity and avoidance (both p < 0.001), lower sensation seeking (p = 0.002), and modestly higher low registration (p = 0.027). Elevated SMD correlated with greater symptom severity and perceived stress. Stress significantly predicted FM’s impact (β = 0.57, p < 0.001). A discriminant model achieved 84% apparent in-sample accuracy for classifying FM severity from sensory/stress profiles. Conclusions: Sensory modulation abnormalities are highly prevalent in FM and show meaningful associations with symptom severity and stress, suggesting that SMD could represent a potential diagnostic dimension and stratification aid. These findings should be interpreted within an exploratory, cross-sectional design. Incorporating sensory modulation assessment into FM evaluation may improve diagnostic precision, reduce delays, and guide individualized management. Confirmation in larger longitudinal studies is warranted. Full article

Background/Objectives: This proof-of-concept study evaluated whether optical coherence tomography angiography (OCTA) can non-invasively capture micro-vascular alterations in non-melanoma skin cancer (NMSC) lesions during and after superficial orthovoltage radiotherapy (RT) using radiomics and vascular features analysis. Methods: Eight patients (13 NMSC lesions) received 36–50 Gy in 6–20 fractions. High-resolution swept-source OCTA volumes (1.1 × 10 × 10 mm³) were acquired from each lesion at three time points: pre-RT, immediately post-RT, and three months post-RT. Additionally, healthy skin baseline was scanned. After artifact suppression and region-of-interest cropping, (i) first-order and texture radiomics and (ii) skeleton-based vascular features were extracted. Selected features after LASSO (least absolute shrinkage and selection operator) were explored with principal-component analysis. An XGBoost model was trained to classify time points with 100 bootstrap out-of-bag validations. Kruskal–Wallis tests with Benjamini–Hochberg correction assessed longitudinal changes in the 20 most influential features. Results: Sixty-one OCTA volumes were analyzable. LASSO retained 47 of 103 features. The first two principal components explained 63% of the variance, revealing a visible drift of lesions from pre- to three-month post-RT clusters. XGBoost achieved a macro-averaged AUC of 0.68 ± 0.07. Six features (3 texture, 2 first order, 1 vascular) changed significantly across time points (adjusted p < 0.05), indicating dose-dependent reductions in signal heterogeneity and micro-vascular complexity as early as treatment completion, which deepened by three months. Conclusions: OCTA-derived radiomic and vascular signatures tracked RT-induced micro-vascular remodeling in NMSC. The approach is entirely non-invasive, label-free, and feasible at the point of care. As an exploratory proof-of-concept, this study helps to refine scanning and analysis protocols and generates knowledge to support future integration of OCTA into adaptive skin-cancer radiotherapy workflows. Full article

Background: The majority of injuries among older adults occur due to unexpected and sudden incidents in the home environment. This study aimed to develop a protocol for the design of the health belief model-based program for preventing unintentional home injuries in older adults and to evaluate the effectiveness of the program. Methods: The study proposed in this protocol, Safety and Home Injury Prevention for Seniors (SHIPs), is a single-blind, parallel-group, randomized controlled trial. A total of 54 Korean older adults (≥65 years) will be randomly assigned to either (1) the intervention group (n = 27), which will receive the SHIPs program, or (2) the control group (n = 27), which will attend four lecture-only sessions. The efficacy of the program will be assessed via tests performed at baseline, 1 week after program completion, and 1 month after program completion, and analyses of the changes in injury occurrences, risk factors, preventive behaviors, beliefs about safety and injury prevention, psychological health, physiological function, and health-related quality of life. Expected Results: The SHIPs intervention is expected to reduce home injuries and enhance awareness and preventive behaviors among community-dwelling older adults. It may also improve their physical and psychological health and overall quality of life. Conclusions: The SHIPs intervention may serve as an effective community-based strategy to promote injury prevention and improve the overall well-being of older adults. Full article

The ageing of polymer-modified bitumen (PMB) significantly affects its rheological performance and service life in asphalt pavements. In this study, experimental data PMB 25/55–60 aged under a modified Thin Film Oven Test (TFOT) were restructured into a tidy dataset and analyzed using machine learning techniques. The input variables included temperature, angular frequency, and ageing condition, while the output variable was the complex shear modulus (G*). Two state-of-the-art regression models, Random Forest (RF) and Gradient Boosting Regressor (GBR), were trained and evaluated. Performance assessment revealed that GBR outperformed RF, achieving R² = 0.992, MAE = 1.07 × 10⁶ Pa, and RMSE = 2.04 × 10⁶ Pa, compared to RF with R² = 0.962. Condition-wise analysis further confirmed the robustness of GBR across different TFOT scenarios. Feature importance analysis identified temperature as the dominant factor influencing rheological behavior, followed by frequency and ageing condition. These findings demonstrate the potential of gradient boosting approaches for accurately predicting the rheological properties of aged PMB, providing a reliable tool for performance evaluation and supporting the development of predictive frameworks for pavement materials. Full article

Hyaluronic acid (HA) hydrogels are promising biomaterials for tissue engineering and drug delivery due to their biocompatibility and biodegradability. The objective of this study was to develop a novel HA-based hydrogel for the controlled release of basic fibroblast growth factor (bFGF) to promote angiogenesis. A series of PEG-grafted HA hydrogels with varying PEG grafting ratios were synthesized and characterized. We evaluated their physicochemical properties, including swelling ratio, cross-linking density, and enzymatic degradation behavior, and assessed their ability to control bFGF release and induce angiogenesis in a mouse model. The results showed that the PEG-grafting ratio significantly affected the gel properties. Notably, the PEG60-graft-HA hydrogel exhibited a higher swelling ratio and more rapid degradation, suggesting a non-uniform and highly porous structure. In vitro release studies confirmed that while PEG5-graft-HA and PEG15-graft-HA gels showed burst release, the PEG60-graft-HA hydrogel demonstrated sustained release of bFGF over time. Furthermore, in vivo experiments revealed a significant increase in angiogenesis with the PEG60-graft-HA hydrogel, likely due to the prolonged release of active bFGF. These findings suggest that PEG-grafted HA hydrogels, particularly those with a higher PEG grafting ratio, are promising biomaterials for the controlled release of growth factors and applications in tissue regeneration. Full article

Objective: This study aimed to develop eco-friendly zinc oxide nanoparticles (ZnO NPs) using Punica granatum (pomegranate) peel extract and to evaluate their antioxidant, antimicrobial, and photoprotective potential. Method: ZnO NPs were synthesized via a green chemistry route employing polyphenol- and flavonoid-rich peel extract as reducing and stabilizing agents. The nanoparticles were characterized using FTIR, SEM, XRD, DSC, DLS, and UV–Vis spectroscopy. Biological activities were assessed through in vitro assays including antioxidant (DPPH), anti-collagenase, anti-elastase, anti-tyrosinase, antimicrobial activity, and SPF determination. In vivo photoprotective efficacy was further evaluated in UVB-irradiated rat models, with histological analysis to confirm structural skin changes. Results: The optimized ZnO NPs exhibited an average particle size of ~194 nm with a zeta potential of −18.2 mV, indicating good stability. They demonstrated notable antioxidant activity (DPPH IC₅₀ = 52.91 µg/mL), substantial tyrosinase inhibition (72% at 200 µg/mL), and antibacterial activity with inhibition zones up to 19 mm against S. aureus and 17 mm against E. coli. The nanoparticles also showed excellent UV absorption, with an SPF value of 29.8, exceeding the FDA threshold for effective sun protection. In vivo, topical application of ZnO NPs in UVB-exposed rats led to a 69% reduction in epidermal thickness and preservation of collagen fibers compared with UV controls. Conclusions: These findings confirm that P. granatum peel extract–mediated ZnO NPs possess significant antioxidant, antimicrobial, and photoprotective activities. Full article

Water loss during storage is a major cause of postharvest quality deterioration in cucumber, yet existing methods to monitor hydration are often destructive or require expensive instrumentation. We developed a low-cost, non-destructive approach for estimating fruit relative water content (RWC) using visible-light color imaging combined with an ensemble machine-learning model (Random Forest). A total of 1200 fruits were greenhouse-grown, harvested at market maturity, and equally divided between optimal and ambient storage temperature (10 and 25 °C, respectively). Digital images were acquired at harvest and at 7 d intervals during storage, and color parameters from four standard color systems (RGB, CMYK, CIELAB, HSV) were extracted separately for the neck, mid, and blossom regions as well as for the whole fruit. During storage, fruit RWC decreased from 100% (fully hydrated condition) to 15.3%, providing a broad dynamic range for assessing color–hydration relationships. Among the 16 color features evaluated, the mean cyan component (μC) of the CMYK space showed the strongest relationship with measured RWC (R² up to 0.70 for whole-fruit averages), reflecting the cyan region’s heightened sensitivity to dehydration-induced changes in pigments, cuticle properties and surface scattering. The Random Forest regression model trained on these features achieved a higher predictive accuracy (R² = 0.89). Predictive accuracy was also consistently higher when μC was calculated over the entire fruit surface rather than for individual anatomical regions, indicating that whole-fruit color information provides a more robust hydration signal than region-specific measurements. Our findings demonstrate that simple visible-range imaging coupled with ensemble learning can provide a cost-effective, non-invasive tool for monitoring postharvest hydration of cucumber fruit, with direct applications in quality control, shelf-life prediction and waste reduction across the fresh-produce supply chain. Full article

In this paper, heat dissipation through a passive vertical plate fin heat sink via natural convection was numerically investigated. The influence of two nondimensional geometric parameters, fin spacing-to-thickness ratio and fin height-to-spacing ratio, on the heat sink’s thermal performance was evaluated. A mathematical model describing the three-dimensional steady-state problem of buoyancy-driven flow and heat transfer was formulated. The solution was obtained numerically using the finite volume method in Ansys Fluent. The model and numerical procedure were validated by comparing the numerical predictions with measurements acquired on a constructed experimental apparatus. The heat sink thermal performance was assessed based on a series of performance metrics: heat dissipation rate, heat transfer coefficient, overall thermal resistance, and fin efficiency. Fin spacing-to-thickness ratio was varied between 1.86 and 4.8. Heat dissipation rate displayed a clear peak at a value of approximately 2.6, which coincided with a minimum in the overall thermal resistance. The heat transfer coefficient initially grew steadily, but at higher values of fin spacing-to-thickness ratio, it began to stagnate. Fin efficiency consistently decreased across the investigated range. Fin height-to-spacing ratio was varied between 1.11 and 7.78. The heat dissipation rate increased almost linearly across this range, but when the mass-specific heat dissipation rate was considered, it yielded diminishing returns. The heat transfer coefficient likewise exhibited a plateauing trend, while fin efficiency decreased steadily across the investigated range of fin height-to-spacing ratio. The obtained numerical results provide guidelines for geometry selection and can serve as a foundation for further analyses and optimizations of passive heat sinks’ thermal performance. Full article

Dengue is a life-threatening disease that is transmitted by mosquitoes. Dengue fever has no proper treatment. Early, proper diagnosis is essential to minimize complications and enhance outcomes in patients. This research uses a clinical and hematological dataset of dengue to assess the effectiveness of the Gradient Boosting (GB) classification model with and without feature selection. It initially employs a standalone GB model, achieving impeccable results for classification, at 100% accuracy, F1-score, precision, and recall. In addition, the Bird Swarm Algorithm (BSA)-based metaheuristic technique is implemented on the GB classifier to execute wrapper-based feature selection so that features are reduced and achieve better results. The BSA-GB model yielded an accuracy of 99.49%, F1-score of 99.62%, recall of 99.24%, and precision of 100%, but it only selected five features in total. An additional test with a five-fold cross-validation was employed for better performance and model evaluation. Folds 1 and 2 showed especially good results. Although fold 2 selected only four features, it still showed high results, compared to fold 1, which selected five features. In this context, fold 2 achieved an accuracy of 99.49%, F1-score of 99.65%, recall of 99.30%, and precision of 100%. Means of hyperparameters were also calculated across folds to make a generalized GB model, which maintained 99.49% of accuracy with just three features, namely, Hemoglobin, WBC Count, and Platelet Count. To enhance transparency, counterfactual explanations were performed to analyze the misclassified cases, which indicated that minimum changes in input features modify the predictions. Also, an evaluation of the SHAP value result designated WBC Count and Platelet Count as the most important features. Full article

Background: Epithelial ovarian cancer is an aggressive malignancy with poor prognosis despite advances in multimodal treatment. The systemic immune-inflammation index (SII) has emerged as a prognostic biomarker in various cancers; however, the impact of surgery-induced inflammatory changes remains unclear. Methods: This study evaluated the prognostic significance of postoperative changes in SII among patients with epithelial ovarian cancer undergoing primary surgery. Data from 374 patients treated at Samsung Medical Center and Kangbuk Samsung Hospital between 2016 and 2021 were retrospectively reviewed. SII was calculated from complete blood counts obtained within one month before surgery and on postoperative day 1. The percentage change in SII was analyzed, and the optimal cutoff was determined using receiver operating characteristic curve analysis. Survival outcomes were assessed using Kaplan–Meier and multivariable Cox regression models. Results: Patients with a postoperative SII increase > 98.4% (Group 2) had significantly poorer overall (HR = 1.86, p = 0.009) and progression-free survival (HR = 1.30, p = 0.112) compared with those with smaller changes (Group 1). Discussion: High-grade histology, serous subtype, and greater intraoperative blood loss were associated with higher postoperative SII. A marked postoperative increase in SII independently predicted poor survival, suggesting that dynamic inflammatory responses rather than static baseline levels provide additional prognostic information. Conclusions: Perioperative SII monitoring, easily obtainable from routine blood tests, may help identify high-risk patients who could benefit from intensified surveillance or adjuvant treatment. Prospective multicenter studies are warranted to validate these findings and explore whether perioperative modulation of systemic inflammation can improve outcomes. Full article

Accurate estimation of lithium-ion battery (LIB) state of health (SOH) underpins safe operation, predictive maintenance, and lifetime-aware energy management. Despite recent advances in machine learning (ML), systematic benchmarking across heterogeneous real-world cells remains limited, often confounded by data leakage and inconsistent validation. Here, we establish a leakage-averse, cross-battery evaluation framework encompassing 32 commercial LIBs (B5–B56) spanning diverse cycling histories and temperatures (≈4 °C, 24 °C, 43 °C). Models ranging from classical regressors to ensemble trees and deep sequence architectures were assessed under blocked 5-fold GroupKFold splits using RMSE, MAE, R² with confidence intervals, and inference latency. The results reveal distinct stratification among model families. Sequence-based architectures—CNN–LSTM, GRU, and LSTM—consistently achieved the highest accuracy (mean RMSE ≈ 0.006; per-cell R² up to 0.996), demonstrating strong generalization across regimes. Gradient-boosted ensembles such as LightGBM and CatBoost delivered competitive mid-tier accuracy (RMSE ≈ 0.012–0.015) yet unrivaled computational efficiency (≈0.001–0.003 ms), confirming their suitability for embedded applications. Transformer-based hybrids underperformed, while approximately one-third of cells exhibited elevated errors linked to noise or regime shifts, underscoring the necessity of rigorous evaluation design. Collectively, these findings establish clear deployment guidelines: CNN–LSTM and GRU are recommended where robustness and accuracy are paramount (cloud and edge analytics), while LightGBM and CatBoost offer optimal latency–efficiency trade-offs for embedded controllers. Beyond model choice, the study highlights data curation and leakage-averse validation as critical enablers for transferable and reliable SOH estimation. This benchmarking framework provides a robust foundation for future integration of ML models into real-world battery management systems. Full article

Background: Neoadjuvant chemotherapy (NAC) is a standard preoperative intervention for early-stage breast cancer (BC). Dynamic contrast-enhanced magnetic resonance imaging (CE-MRI) has emerged as a critical tool for evaluating treatment response and pathological complete response (pCR) following NAC. Computational modeling offers a robust framework to simulate tumor growth dynamics and therapy response, leveraging patient-specific data to enhance predictive accuracy. Despite this potential, integrating imaging data with computational models for personalized treatment prediction remains underexplored. This case study presents a proof-of-concept prognostic tool that bridges oncology, radiology, and computational modeling by simulating BC behavior and predicting individualized NAC outcomes. Methods: CE-MRI scans, clinical assessments, and blood samples from three retrospective NAC patients were analyzed. Tumor growth was modeled using a system of partial differential equations (PDEs) within a reaction–diffusion mass transfer framework, incorporating patient-specific CE-MRI data. Tumor volumes measured pre- and post-treatment were compared with model predictions. A 20% error margin was applied to assess computational accuracy. Results: All cases were classified as true positive (TP), demonstrating the model’s capacity to predict tumor volume changes within the defined threshold, achieving 100% precision and sensitivity. Absolute differences between predicted and observed tumor volumes ranged from 0.07 to 0.33 cm³. Virtual biomarkers were employed to quantify novel metrics: the biological conversion coefficient ranged from 4 × 10⁻⁷ to 6 × 10⁻⁶ s^-1, while the pharmacodynamic efficiency coefficient ranged from 1 × 10⁻⁷ to 4 × 10⁻⁴ s^-1, reflecting intrinsic tumor biology and treatment effects, respectively. Conclusions: This approach demonstrates the feasibility of integrating CE-MRI and computational modeling to generate patient-specific treatment predictions. Preliminary model training on retrospective cohorts with matched BC subtypes and therapy regimens enabled accurate prediction of NAC outcomes. Future work will focus on model refinement, cohort expansion, and enhanced statistical validation to support broader clinical translation. Full article

Deteriorating check dams pose significant threats to human safety and property, while impeding eco-environmental restoration in soil–water conservation systems in vulnerable watersheds like the Jiuyuangou Basin on China’s Loess Plateau. This study aimed to develop a comprehensive risk assessment framework for the check dam system in the Jiuyuangou Basin, China, to mitigate its threats to safety and eco-environmental restoration. A multi-index and multilevel risk evaluation system was established for check dam systems in the Jiuyuangou Basin, utilizing data gathering, hydrological statistics, numerical computation, and various methodologies. The index weights were determined via the fuzzy analytic hierarchy process with an integrated modeling framework for key parameters. Finally, the risk level of the check dam system in the Jiuyuangou Basin was assessed based on the comprehensive score. The results show that (1) nearly half of the check dams are at mild risk, approximately 25% are at moderate risk, and a few are basically safe. (2) Among various types of risk, the distribution of engineering risk is relatively uniform, environmental risk is generally high, loss risk is relatively concentrated, and management risk is particularly prominent. This research provides a scientific foundation for optimizing check dam governance, enhancing sediment control, and strengthening ecological service functions in vulnerable watersheds. Full article

Objective/Background: Pupil diameter varies across individuals, limiting its reliability in assessing cerebral pathologies, particularly in comatose patients following subarachnoid haemorrhage (SAH). The Dangerous Alarming Diameter Assessment (DADA) index, defined as the ratio of iris surface to pupil surface, may offer a more precise diagnostic tool. This study evaluates the efficacy of the DADA index compared to pupil diameter in predicting neurodegeneration in the Edinger–Westphal nucleus (EWN) and diagnosing brain death in an SAH model. Methods: Twenty-three rabbits were divided into Control (n = 5), Sham (n = 5), and Study (SAH, n = 12) groups. Pupil diameter and DADA index values were measured via spectral-domain optical coherence tomography (SD-OCT) in groups at post-intervention (0.75 cc serum physiologic injection for Sham, 0.75 cc autologous blood injection for Study). After one week, animals were sacrificed, and EWN degenerated neuron density was quantified using stereological methods. Data were analysed with Kruskal–Wallis and Mann–Whitney U tests, with correlations assessed for pupil diameter and DADA index against EWN neurodegeneration. Results: Pupil diameter assessment classified all 12 study group subjects as deceased, primarily due to fixed and dilated pupils. In contrast, the DADA index identified only 8 of these 12 subjects as deceased, with EWN degenerated neuron density exceeding 80%, while the remaining 4 subjects showed less than 80% neurodegeneration, indicating viability. Strong negative correlations were observed between pupil diameter (r = −0.972, p < 0.001) and DADA index (r = −0.977, p < 0.001) with EWN neurodegeneration. The DADA index demonstrated superior precision in distinguishing severe neurodegeneration, suggesting its potential as a criterion for brain death assessment. Conclusions: The DADA index provides a more accurate and nuanced evaluation of EWN neurodegeneration compared to pupil diameter, offering a promising diagnostic tool for brain death in SAH-induced comatose states, with potential implications for future brain transplantation diagnostics. Full article

Objective: The P-POSSUM scale is widely used in predicting perioperative morbidity and mortality. Evidence on the performance of P-POSSUM in predicting outcomes after cytoreductive surgery (CRS) for ovarian cancer (OC) is limited. In this study, we assess how well P-POSSUM predicts morbidity in OC CRS and explore whether incorporating additional clinical variables can enhance its predictive accuracy. We retrospectively collected data on consecutive patients undergoing OC CRS within a tertiary gynaecologic oncology network. The collected information included demographic characteristics, P-POSSUM morbidity and mortality scores, Edmonton Frail Scale (EFS) scores, preoperative serum albumin levels, and observed 30-day postoperative morbidity and mortality, classified using the Clavien–Dindo (CD) scale. The predictive performance of P-POSSUM was evaluated using receiver operating characteristic (ROC) curves to calculate sensitivity and specificity. A stepwise regression analysis was then applied to identify additional variables that could improve model performance, incorporating preoperative covariates. The final model incorporated parameters chosen through bootstrap investigation of the model variability (stepAIC). Predicted versus observed morbidity was calibrated and performance compared between P-POSSUM and the final model. Results: Of 161 sequential OC patients, 95 (59%) underwent primary, 45 (28%) interval, and 21 (13%) delayed CRS. The mean age was 66.4 (95%CI: 60–75) and duration of surgery was 223 mins (95%CI: 142–279). Sixty-five (40.3%) patients had ≥1 postoperative complication. Two deaths were reported. Among the observed complications, 4 patients (6.1%) experienced CD4, 10 patients (15.3%) CD3, 38 patients (58.5%) CD2, and 11 patients (16.9%) CD1 events. The mean P-POSSUM-predicted morbidity and mortality were 59.5% (95%CI: 56.7–62.3%) and 5.86% (95%CI: 5.02–6.70%), respectively. The area under the curve (AUC) for P-POSSUM in predicting morbidity and mortality was 0.539 (p = 0.401) and 0.569 (p = 0.137), respectively. Given the small number of deaths, no robust conclusions regarding mortality are possible. EFS and BMI emerged as significant predictors of observed morbidity using a stepwise-model selection process. The AIC of this final model was 211.44. Our final model of PPOSSUM + EFS + BMI had AUC = 0.6551 (Delong’s Z = 1.8845, p-value = 0.05949). Conclusions: The P-POSSUM scale shows poor performance for predicting morbidity in OC CRS. New validated and accurate model(s) are necessary for predicting surgical morbidity. Our proposed model incorporates additional variables to improve P-POSSUM’s performance. This requires further development and validation. Full article