Search Results (25,084)

The definition of hypertension and the values of systolic and diastolic blood pressure (BP) that should be considered as therapeutic targets have changed over time and vary across scientific societies, which may generate uncertainty in the decision-making process among clinicians and patients. We traced the evolution and described the differences in all the 32 Clinical Practice Guidelines for the management of hypertension released by the following national and international scientific societies: World Health Organization—WHO; International Society of Hypertension—ISH; Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure—JNC; American Heart Association—AHA; American College of Cardiology—ACC; European Society of Cardiology—ESC; European Society of Hypertension—ESH; and UK National Institute for Health and Care Excellence—NICE. Throughout the decades, the BP values used for hypertension definition, treatment initiation, and targets to achieve started from SBP/DBP ≥ 160/95 mmHg, established at the end of the 70s, progressively decreased, and were differentiated by individual cardiovascular risk. In the last decade, a divergent approach emerged across scientific societies: while WHO/ISH and NICE recommended thresholds and targets for the general population at SBP/DBP < 140/90 mmHg, ESH/ESC and ACC/AHA guidelines further and markedly reduced both BP threshold values and therapeutic targets, recommending as ideal SBP/DBP values < 130/80 mmHg and encouraging an SBP < 120 mmHg. Discrepancies also emerged in the assessment of the quality of the evidence: although the methodological approaches largely improved over time and across all the institutions assessed, various degrees of incompleteness on the adopted scales were reported, and potentially conflicting situations emerged, particularly when weaker evidence was used to build strong recommendations. Although some degree of discrepancy among guidelines is expected, some of the differences are large and can lead to widely different approaches in the management of BP control. A standardization of the methodology and interpretation of the evidence supporting the guidelines may help to reduce the variability in order to provide the best possible guidance for clinical practice and patient health. Full article

Managing blood glucose in type 1 diabetes (T1D) remains a daily clinical challenge, and accurate short-term prediction of glucose levels can meaningfully improve insulin dosing decisions while reducing the risk of dangerous hypoglycaemic episodes. Although numerous machine learning approaches have been proposed for this task, comparing their relative merits is difficult because published studies differ widely in datasets, preprocessing choices, and evaluation criteria. In this work, we address this research gap by benchmarking ten machine learning methods—from a naïve persistence baseline through classical linear regressors, gradient-boosted ensembles, and recurrent neural networks to a novel hybrid that couples LightGBM with stochastic differential equation (SDE)-based glucose–insulin simulation—on two multi-patient datasets comprising 34 T1D subjects, across prediction horizons of 15, 30, 60, and 120 min. Every method is trained and tested under identical preprocessing and temporal splitting conditions to ensure a fair comparison. The proposed Hybrid LightGBM-SDE model consistently outperforms all alternatives, recording RMSE values of 22.42 mg/dL at 15 min, 28.74 mg/dL at 30 min, 33.89 mg/dL at 60 min, and 37.22 mg/dL at 120 min—an improvement of between 13.6% and 27.0% relative to standalone LightGBM. At the clinically important 30 min horizon, 99.7% of predictions lie within the acceptable A and B zones of the Clarke Error Grid. Wilcoxon signed-rank tests confirm that performance differences are statistically significant (p < 10⁻¹⁰), and SHAP-based analysis shows that the SDE-derived simulation features are among the most influential predictors, especially at longer horizons. All source code and evaluation scripts are publicly released to support reproducibility. Due to temporary data access constraints, all experiments reported here use physics-based synthetic datasets generated from the Bergman minimal model, replicating the structural properties of the D1NAMO and HUPA-UCM collections; validation on the original clinical recordings is planned. Among the two synthetic datasets, the D1NAMO-equivalent cohort (nine patients) proves more challenging, with systematically higher per-patient RMSE variance. The clinically acceptable prediction accuracy at the 30 min horizon (99.7% in Clarke zones A + B) suggests potential for integration into insulin dosing decision-support systems. Full article

Background/Objectives: Measures of excess body fat are often more informative for predicting health risk than body mass index (BMI) alone. With obesity prevalence increasing among young adults, this study evaluated whether adding dominant handgrip strength improves prediction of body fat percentage (BF%) and BF%-defined obesity in a university population. Methods: Cross-sectional data from 895 students (401 women, 494 men; mean age 19.9 years; fall 2015–spring 2016) in Kentucky, USA were analyzed. BMI was calculated from self-reported height and weight. BF% was estimated using bioelectrical impedance analysis (BIA), and dominant handgrip strength was measured with a hydraulic hand grip dynamometer. Sex-specific linear and logistic regression models assessed associations among BMI, grip strength, relative grip strength, and BF%. Results: BMI was a strong predictor of BF% in linear models (R² = 0.74 in women; 0.68 in men). Grip strength alone was not associated with BF% but showed an inverse association when combined with BMI. For BF%-defined obesity, BMI remained the most influential predictor, with grip strength contributing additional predictive value. Among men, age significantly modified these relationships, with marked differences between those aged 18–19 years versus older participants. Conclusions: BMI strongly predicted BF% and BF%-based obesity in this cross-sectional study of a predominantly white young adult population. Incorporating handgrip strength modestly improved classification, particularly among women, suggesting that a functional measure like hand grip strength may enhance obesity screening and health communication in young adults. Full article

The increasing penetration of wind generation requires performance evaluation methods that extend beyond average annual energy production. Temporal delivery characteristics, such as monthly dispersion and exposure to low-production periods, can influence both technical robustness and economic sensitivity. Building upon a previously developed probabilistic and entropy-based assessment framework, this study evaluates the robustness of delivery-oriented performance metrics for onshore and offshore wind units under parametric and economic uncertainty. Using high-resolution operational data from four wind units (three onshore and one offshore), the analysis incorporates percentile sensitivity, threshold variation in low-production exposure, bootstrap-based uncertainty intervals, and Monte Carlo simulation of economic inputs including CAPEX, operation and maintenance costs, and discount rate. The results indicate that variations in percentile definitions and stochastic economic assumptions modify absolute performance values but do not substantially alter the relative positioning between offshore and onshore units. Averaged over 2022–2024, the analyzed offshore unit exhibited a lower monthly energy dispersion coefficient ($C{V}_E=0.255$) [Reviewer2]than the analyzed onshore units ($C{V}_E=0.368$), [Reviewer2]corresponding to an approximate 30% reduction in relative variability. The offshore unit also showed lower mean low-production exposure ($LPE=0.526$ versus $0.581$ for onshore units) [Reviewer2]and consistently lower amplification of robustness-adjusted LCOE under conservative delivery assumptions. These results indicate that the analyzed offshore unit retains stronger delivery robustness and lower economic sensitivity across the tested parameter ranges. The proposed robustness-validation framework complements conventional yield-based assessments and provides additional insight for risk-aware evaluation of wind generation assets in renewable-dominated power systems. Full article

Quality by design (QbD) is a systematic approach focused on achieving consistent, predictable quality based on predefined objectives. Unlike traditional methods, QbD prioritizes risk assessment and management, which significantly enhances the robustness of the analytical method. In this study, we initiated factor screening using a three-factor, two-level design to evaluate three independent variables: flow rate, pH, and mobile phase composition. To further investigate the interaction of these variables, we employed Central Composite Design (CCD). This allows us to apply response surface methodology to the Critical Analytical Attributes (CAAs), specifically retention time, peak area, and symmetry factor, by conforming to the method’s robustness. The combination of pregabalin and duloxetine hydrochloride (HCl) dosage form was determined using a straightforward, exact, specific, and accurate reverse-phase HPLC approach. The results showed retention times of 3.265 min and 4.318 min for duloxetine HCl and pregabalin, respectively. Pregabalin demonstrated linearity from 100 to 200 μg/mL (R² = 0.998), whilst duloxetine HCl demonstrated linearity between 20 and 120 μg/mL (R² = 0.997). Lower LOD values of 0.925 µg/mL and 0.853 μg/mL and LOQ values of 2.809 μg/mL and 2.587 μg/mL of pregabalin and duloxetine HCl, respectively, suggest good sensitivity for the technique. The drug content of the commercial formulation may thus be determined using the recommended method. This technique can be used for standard quality control studies to simultaneously estimate pregabalin and duloxetine HCl. The novelty of the present studies lies in the development of a robust RP-HPLC method for simultaneous estimation of pregabalin and duloxetine HCl using a systematic AQbD approach, enhancing robustness, reproducibility, and reliability, making it highly suitable for routine quality control and regulatory applications. Full article

Biogenic amines are nitrogenous compounds that may occur in foods through plant metabolism, bee enzymatic activity, or microbial decarboxylation. This study evaluated biogenic amines content in monofloral honeys from Algeria and Morocco, integrating compositional analysis, quality indices, and dietary exposure assessment within a toxicological risk characterization framework. Eight amines were quantified by HPLC-FLD, and Estimated Daily Intake (EDI) was calculated under adult and pediatric low- and high-consumption scenarios. Composite indices, including Total Biogenic Amines (TBA), Biogenic Amine Index (BAI), Vasoactive Amine Load (VAL), Potentiation Index (PI), and Quality Index (QI), were determined. Marked intra- and inter-city variability was observed, particularly for serotonin, tryptamine, and tyramine. Algerian Euphorbia orientalis L. samples showed the highest TBA and VAL values. However, histamine concentrations generally remained below 1 mg/kg, and tyramine levels were markedly lower than doses associated with hypertensive effects. Worst-case EDI values were in the order of 10⁻³–10⁻⁴ mg/kg body weight/day, including high-consumption pediatric scenarios. PI values were low, indicating limited synergistic amplification by diamines. Overall, despite botanical and geographical variability, the analyzed honeys exhibit a wide safety margin and based on the applied screening-level assessment, no immediate risk is indicated under the considered scenarios. Full article

Seasonal snow cover in mountainous regions represents a critical natural freshwater reserve for arid and semi-arid areas of Central Asia. This study evaluates the long-term (2000–2024) spatiotemporal dynamics of snow cover in the Arys River basin, located within the Western Tien Shan. The research utilizes daily satellite data from MODIS Terra and Aqua, along with data from the MODSNOW automated processing system. Terra-Aqua composite imagery was employed to minimize cloud cover effects. Satellite-derived estimates were validated against observational data from five meteorological stations of the Republican State Enterprise (RSE) “Kazhydromet”. The results indicate significant interannual variability in snow cover extent: the snow-covered area during the cold season ranged from 16.2% to 54.1%, with a mean value of 34.4%. Trend analysis revealed a weak negative trend, while Sen’s slope estimator showed an average annual reduction in snow cover area of 0.37% per year. The most pronounced decline in snow accumulation was observed in mid-elevation mountain zones. These findings suggest potential increased risks to seasonal water availability in the Arys River basin and, more broadly, across the Syr Darya basin under ongoing climate change conditions. The results provide a scientific basis for quantifying climate impacts and developing adaptation strategies for integrated water resources management in Central Asia. Full article

Background/Objectives: Although certain established prognostic factors may occasionally fail to provide precise risk prediction in renal cell carcinoma (RCC), valosin-containing protein (VCP)/p97 has been implicated in a poor prognosis in various cancers, while its prognostic value in clear cell renal cell carcinoma (ccRCC) remains unknown. This study aimed to determine the independent prognostic value of VCP/p97 expression in ccRCC. Methods: This retrospective study included 137 ccRCC patients, and VCP/p97 expression was analyzed by immunohistochemistry and classified into either low or high expression based on the intensity of the staining in relation to the expression in endothelial cells. Results: High expression of VCP/p97 was significantly correlated with large tumor size ($p<0.001$), Fuhrman nuclear grade ($p=0.003$), advanced TNM stage ($p<0.001$), and distant metastasis ($p<0.001$). Kaplan–Meier analysis showed that the survival of patients with high expression of VCP/p97 was significantly reduced, and multivariate analysis revealed that high expression of VCP/p97 independently predicted poor survival (HR $2.09$, $95\%$ CI 1.06–4.15, $p=0.034$) in addition to age, Fuhrman grade, and TNM stage. Conclusions: This study demonstrated that VCP/p97 expression, a newly identified prognostic factor, independently predicted a poor prognosis in ccRCC, and its expression may be a useful tool in identifying ccRCC patients with a poor prognosis. Full article

Background/Objectives: Percutaneous coronary intervention (PCI) effectively restores coronary flow in acute myocardial infarction (AMI), but myocardial ischemia–reperfusion injury (MIRI) remains a major prognostic determinant. Mitochondrial open reading frame of the 12S rRNA-c (MOTS-c) has shown cardiovascular protective effects, yet its association with MIRI is unclear. This study aimed to investigate the relationship between serum MOTS-c levels and MIRI in AMI patients. Methods: Seventy-two AMI patients undergoing PCI were enrolled and divided into MIRI (n = 34) and non-MIRI (n = 38) groups. Clinical data and MOTS-c levels in peripheral serum and intracoronary blood were compared. Multivariate logistic regression and receiver operating characteristic (ROC) analysis were performed to identify MIRI predictors. Results: The MIRI group exhibited lower systolic blood pressure, preoperative thrombolysis in myocardial infarction (TIMI) grade, and HDL-C, but higher total ischemic time, door-to-balloon time, culprit vessel stenosis severity, Killip grade and adverse event incidence (all p < 0.05). Postoperative peripheral serum MOTS-c levels were significantly lower in the MIRI group than in the non-MIRI group (p < 0.05), while preoperative peripheral and intracoronary MOTS-c levels showed no significant differences between groups. Multivariate logistic regression identified postoperative peripheral MOTS-c levels (OR = 0.986, 95%CI: 0.976–0.996) and preoperative TIMI grade ≥ 1 (OR = 0.036, 95%CI: 0.004–0.309) as independent protective factors for MIRI, whereas serum creatinine was identified as an independent risk factor. ROC analysis demonstrated that postoperative peripheral MOTS-c levels predicted MIRI with an area under the curve of 0.648. Conclusions: Postoperative peripheral serum MOTS-c levels represent an independent protective factor against MIRI in patients with acute myocardial infarction and suggest a potential predictive value for MIRI, although its clinical utility as a standalone predictor requires further validation through dynamic monitoring and larger-scale studies. This finding may offer a potential novel biomarker and therapeutic direction for MIRI. Full article

Wildfire monitoring systems increasingly rely on satellite-derived risk surfaces to support resource-constrained prioritization. However, less attention has been paid to how spatial aggregation interacts with alarm sparsity in shaping event-level wildfire capture. This study conducts a retrospective evaluation of percentile-based wildfire alarm regimes in California during the 2024 fire season. Using VIIRS-derived risk surfaces and MTBS burned-area perimeters, the analysis examines three aggregation scales (375, 1000, and 5000 m) under fixed alarm budgets (top 1%, top 5%, and top 10%). Event-level capture was evaluated by aggregating row-level capture values within each MTBS event, with the primary specification based on maximum event-level capture and a threshold of 0.02. Across 2078 unique wildfire events, the effect of spatial aggregation was conditional on alarm sparsity. Under the most restrictive budget (top 1%), scale effects were weak and non-monotonic. In contrast, under the top 5% and top 10%, the coarsest scale (5000 m) consistently produced the highest event-level threshold-exceedance rates. Robustness checks using mean event-level capture and a stricter threshold of 0.05 yielded qualitatively similar patterns under moderate alarm budgets. These findings indicate that the effect of spatial aggregation cannot be interpreted independently of alarm-budget design. Rather than treating spatial resolution as inherently beneficial or detrimental, the study shows that its implications depend on how event-level capture is evaluated under constrained alarm allocation. Full article

Cardiovascular disease (CVD) risk assessment in older adults requires models that are accurate, clinically interpretable, and able to retain performance in independent populations. This study developed an interpretable machine-learning framework for CVD risk stratification in individuals aged 65 years and older using routinely available clinical factors and a selected biochemical extension and then evaluated its performance in a substantially larger independent external cohort. Model development used a development cohort of 100 patients (Almaty, age ≥ 65) with leakage-free nested cross-validation and out-of-fold (OOF) probabilities. Three internally evaluated configurations were compared: a clinical logistic regression baseline (LR clinical), a biomarker-augmented logistic regression (LR selected), and a nonlinear random forest on the selected feature set (RF selected). Discrimination was assessed using ROC-AUC and PR-AUC; probabilistic accuracy using Brier score and log loss. Calibration was examined using OOF calibration curves with sigmoid calibration for selected models. Decision-analytic utility and exploratory operational thresholds were assessed using Decision Curve Analysis (DCA), yielding a three-tier scale with thresholds t__low = 0.23 and t__high = 0.40. In nested cross-validation, LR clinical achieved ROC-AUC 0.9425 ± 0.0188 and PR-AUC 0.9574 ± 0.0092 with Brier 0.1004 ± 0.0215 and log loss 0.3634 ± 0.0652; LR selected performed worse, while RF selected showed competitive discrimination. External validation on an independent cohort (n = 695) showed retained discrimination (ROC-AUC 0.8355; PR-AUC 0.9376) with acceptable probabilistic accuracy (Brier 0.1131; log loss 0.3760), and recalibration (intercept + slope) slightly improved probability metrics. Explainability analyses (odds ratios, permutation importance, SHAP) consistently identified heredity, BMI, physical activity, and diabetes as influential model-associated factors, with clinically plausible directionality. The results suggest that an interpretable model trained on a small geriatric cohort can retain meaningful predictive performance on a substantially larger external cohort, supporting the potential value of transparent risk stratification in older adults, while broader prospective and multi-center validation remains necessary before routine clinical implementation. Full article

Background/Objectives: Tumor–stroma ratio (TSR) and tumor-infiltrating lymphocytes (TILs) were suggested as prognostic markers in oral squamous cell carcinoma (OSCC). Identification of markers assessable in preoperative biopsies that could guide treatment planning is of great importance. This study aimed to evaluate the concordance and prognostic impact of TSR and TILs in preoperative biopsies and matched resection specimens of OSCC. Methods: This study included 100 patients with OSCC. TSR and stromal TILs were evaluated on hematoxylin and eosin-stained slides of biopsies and paired resection specimens and categorized (into low TSR and high TSR; high TILs and low TILs). The agreement between resections and biopsies, and the prognostic significance and clinicopathological correlations of TSR and TILs, were investigated. Results: For TSR, substantial agreement between preoperative biopsies and surgical specimens (kappa correlation coefficient 0.713) was demonstrated. The assessment of TILs showed poor concordance between biopsies and resections (kappa correlation coefficient 0.372). For both biopsies and resections, Cox regression showed an independent negative prognostic impact of low TSR on disease-free, disease-specific, and overall survival. Independent prognostic value of TILs evaluated in biopsies was not found, and the negative prognostic impact of low TILs on disease-free and overall survival was observed only in the main resection specimens. Conclusions: TSR evaluated in preoperative biopsies was highly concordant with results in main resection specimens and may provide significant information for OSCC prognostication, risk stratification, and treatment decisions. In contrast, TILs evaluated in biopsies showed poor concordance with main resection specimens and failed to demonstrate prognostic significance. Full article

Background/Objectives: Prostate cancer (PCa) incidence is rising, with radical prostatectomy (RP) as the main curative surgery for localised cases, which includes removing seminal vesicles (SV). SV invasion (SVI) predicts poor oncological outcomes, making accurate preoperative staging to identify SVI crucial for surgical planning. This ensures oncological safety by enabling wide excision when needed, while preserving tissue to maintain function. This review synthesises current evidence on pre-biopsy MRI findings and/or clinicopathological parameters to diagnose SVI in PCa. Methods: A literature search (2005–2025) using OVID for studies assessing pre-biopsy MRI findings, with a priori eligibility for clinicopathological or combined MRI–clinicopathological models (index tests), for detecting SVI (outcome) compared to RP histopathology (standard reference) in patients with primary localised PCa (patients). This systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Risk of bias was evaluated with QUADAS-2, and pooled diagnostic accuracy metrics and study heterogeneity were analysed. Results: Five studies qualified, while three used binary mpMRI classification and were quantitatively analysed. No eligible studies assessed clinicopathological predictors or combined MRI–clinicopathological models; all included studies evaluated pre-biopsy MRI findings only, and none included high-dimensional radiomics. The pooled sensitivity was 0.66 (95% CI: 0.52–0.78), specificity 0.94 (0.89–0.97), positive predictive value (PPV) 0.76 (0.60–0.87), negative predictive value (NPV) 0.92 (0.85–0.94), and diagnostic odds ratio 30.13 (12.36–73.47), with moderate heterogeneity. All included studies were retrospective cohorts with considerable risk of bias. Conclusions: In the small number of heterogeneous, single-centre retrospective studies available, pre-biopsy MRI findings show high specificity and NPV for preoperative detection of SVI but only moderate sensitivity, which limits its reliability as a standalone tool. The pooled diagnostic accuracy estimates should be interpreted as exploratory. These findings should therefore be interpreted cautiously. Future studies must integrate MRI with clinicopathological data, addressing this key evidence gap before firm conclusions can be drawn or clinical practice changed. Full article

The millisecond-level volatile fluctuations in workloads in large-scale intelligent computing clusters pose significant challenges to traditional electricity markets. Constrained by optical–electrical–optical conversion bottlenecks, these markets struggle to achieve real-time response and risk substantial social welfare loss. Leveraging existing fiber-optic infrastructure to build All-Optical Networks (AONs) presents a cost-effective evolutionary path. This paper develops a distributed energy trading strategy based on all-optical multicast. By utilizing the physical multicast properties of the underlying light-tree architecture instead of traditional protocols, the proposed strategy bypasses end-to-end latency constraints. This enables rapid transaction synchronization and dynamic tracking of social welfare optima within millisecond-level time-slots. Simulation results demonstrate that the proposed scheme elevates the transaction saturation threshold by two orders of magnitude compared with traditional strategies, effectively breaking the physical locking effect of latency on system throughput. Across various topologies, the social welfare gains exceed those of conventional schemes by more than 20 times. This study validates the engineering value of all-optical architectures for high-frequency trading and provides critical technical support for ultra-dynamic power trading algorithms. Full article