Search Results (4,829)

Background/Objective: Neonatal respiratory distress syndrome (RDS) is a leading cause of morbidity and mortality in preterm infants. Interleukin-10 (IL-10) and endothelial nitric oxide synthase (eNOS, also known as NOS3) regulate inflammation and vascular tone, and genetic variants may influence the risk of RDS. To investigate the association between IL-10 rs1800872 (c.-149+1984T>G), IL-10 rs1800896 (c.-149+2474T>C), and NOS3 rs2070744 (c.-149+1691C>T), NOS3 rs1799983 (c.894T>G) variants and the risk of RDS in a Romanian cohort of preterm neonates. Methods: This case–control study included 340 preterm neonates (113 with RDS, 227 controls) born at <36 weeks of gestation. Genotyping was performed using TaqMan SNP assays. Logistic regression adjusted for gestational age and sex estimated odds ratios (ORs) and 95% confidence intervals (CIs). ROC analyses evaluated predictive performance. Results: No significant differences in genotype or allele distributions were observed between RDS and control groups for any variant. Haplotype analysis also revealed no association with RDS susceptibility or severity. NOS3:c.894T>G variant was associated with reduced risk of severe RDS after correction (adjusted p = 0.009), though survival analysis showed no significant genotype-specific effects. Epistatic genotype interaction was observed for the IL-10 T/G + T/C, present only in RDS (p = 0.0026). ROC analysis revealed a clinical prediction of RDS (AUC = 0.996), while the addition of genetic variants improved discrimination for severity (AUC = 0.865; 95% CI: 0.773–0.957) and mortality (AUC = 0.913; 95% CI: 0.791–1.000). Conclusions: IL-10 and NOS3 variants were not individually associated with overall RDS susceptibility. The observed epistatic interactions and the potential protective effect of NOS3:c.894T>G against severe forms can suggest modulatory roles in disease progression. Larger, ethnically homogeneous cohorts are needed to confirm these findings and assess their potential for informing personalized care for neonates. Full article

Background and Objectives: Early gastric cancer (EGC) has an excellent prognosis when detected, yet miss rates during endoscopy remain high. Narrow-band imaging (NBI) enhances mucosal and vascular visualization and is increasingly used, but its benefit over white-light imaging (WLI) in high-risk patients is uncertain. This study aimed to compare NBI with WLI for the detection of gastric neoplasia in patients undergoing gastroscopy. Materials and Methods: We conducted a systematic review and meta-analysis of randomized controlled trials (RCTs), registered in PROSPERO (CRD42025649908) and reported according to PRISMA 2020 guidelines. PubMed, Scopus, and CENTRAL were searched up to October 2024. Eligible RCTs randomized adults undergoing gastroscopy for cancer surveillance or red-flag symptoms to NBI or WLI. Data extraction and risk of bias assessment were performed independently by two reviewers. Pooled relative risks (RRs) with 95% confidence intervals (CIs) were calculated using a random-effects model, and certainty of evidence was graded with GRADE. Results: From 21 records, 3 RCTs comprising 6003 patients were included. NBI did not significantly increase gastric neoplasm detection compared with WLI (2.79% vs. 2.74%; RR = 0.98; 95% CI: 0.66–1.45; I² = 22%). Focal gastric lesion detection rates (14.73% vs. 15.50%; RR = 1.05; 95% CI: 0.72–1.52; I² = 87%) and positive predictive value (29.56% vs. 20.56%; RR = 1.29; 95% CI: 0.84–1.99; I² = 61%) also showed no significant differences. Risk of bias was high for blinding, and overall evidence certainty was low. In practical terms, both NBI and WLI detected gastric cancers at similar rates, indicating that while NBI enhances visualization, it does not increase the likelihood of finding additional cancers in high-risk patients. Conclusions: NBI did not significantly improve gastric neoplasm detection compared with WLI in high-risk patients, though it remains valuable for mucosal and vascular assessment. Larger, multicenter RCTs across diverse populations are required to establish its role in surveillance strategies. Full article

Measurement while drilling technology (MWD) has emerged as a pivotal approach for geological exploration. However, the accuracy of existing geological recognition models remains limited, primarily due to data fluctuations that result in high overlap rates and reduced reliability of drilling parameters. This study takes torque data as an example and analyzes the frequency distribution laws of torque responses across rock with varying strengths. A quantitative model of the frequency distribution characteristic interval is established, and a rock information prediction approach based on frequency distribution characteristics is proposed. The results indicate that torque frequency distributions for homogeneous rock exhibit a unimodal pattern, whereas those for composite rocks display multimodal characteristics. The boundaries of the frequency distribution characteristic intervals are mathematically defined as CIS = T_p|_{(dF/dT) = 0} ± σ and CIM = x_li ± 0.5∆x_i. The strength prediction model constructed using torque within the characteristic interval achieves an average accuracy of 85.3%. Furthermore, the frequency of torque within the characteristic interval enables the estimation of rock stratum thickness. This research contributes to enhancing the accuracy of rock information identification. Full article

Background: Patients after a transient ischemic attack (TIA) are at high risk of subsequent stroke. There are various scores that aim to accurately identify patients at the highest risk of stroke. However, without comparisons between these scores, it is still unknown which is the score with the best predictive utility. Our study aims to identify the risk stratification score with the highest utility to identify patients at high risk for stroke within 90 days after a TIA. Methods: The MEDLINE and Scopus databases were systematically searched on 1 December 2023 for observational cohort studies assessing the ability of a score to predict a stroke within the first 90 days from the index TIA event. Only studies that had a direct comparison of at least two scores were included. A random-effects network meta-analysis was performed. Sensitivity and specificity, along with relevant 95% credible intervals, and between-score and between-study heterogeneity were estimated. We also estimated relative sensitivities and relative specificities compared with the ABCD2 score. We ranked each score according to its predictive accuracy based on both sensitivity and specificity estimates, using the diagnostic odds ratio (DOR) and the summary receiver operating characteristic (SROC) curve. Results: Our systematic review highlighted 9 studies including 14 discrete cohorts. The performance of all scores to identify patients at high risk for stroke recurrence within 90 days following a TIA was low (pooled sensitivity range 48–64%, pooled specificity range 59–72%). In the network meta-analysis, we analyzed 6 studies with 11 discrete cohorts, including data from 8217 patients. The ABCD3-I score demonstrated the highest DOR, followed by the ESRS, ABCD, California, and ABCD2. The SROC curves demonstrate no significant differences in the performance of the scores, using the ABCD score as the common comparator. Conclusions: In this systematic review and network meta-analysis of observational cohort studies of patients who experienced TIA and were followed for the occurrence of subsequent stroke, we failed to identify a score performing significantly better for the prediction of stroke at 90 days. New models are needed for the prediction and stroke risk stratification following a TIA. Full article

Objectives: To investigate the correlation between intraoperative conventional ultrasound, SWE, and SMI ultrasound manifestations of glioma and the expression of immunohistochemical markers. Methods: Patients with single superficial supratentorial glioma scheduled for brain tumor resection in our neurosurgery department from October 2020 to October 2022 were prospectively included. High-grade glioma (HGG) and low-grade glioma (LGG) were classified by pathological histological grading, and the differences in conventional ultrasound, SWE Young’s modulus, and SMI intratumoral and peritumoral blood flow architecture between HGG and LGG were analyzed, and the SWE diagnostic cut-off value was calculated by the Youdon index. Logistic regression models were used to analyze the independent predictive ultrasound signs associated with the diagnosis of HGG. HGG and LGG were classified by pathological histological grading. IDH1 expression was measured by immunohistochemical methods to analyze the correlation between IDH1 expression in glioma and clinical and ultrasound characteristics. Results: Forty-eight patients with glioma admitted to our hospital from October 2020 to October 2022 were included in this study, including 30 (62.5%) with HGG and 18 (37.5%) with LGG. For conventional ultrasound, HGG was often associated with severe peritumoral edema compared with LGG (p = 0.048). The sensitivity of HGG was 88.9%, the specificity was 86.7%, and the AUC was 0.855 (95% confidence interval: 0.741–0.968, p = 0.001) using Young’s mode 13.90 kPa as the threshold. Logistic analysis showed that SWE Young’s modulus values, and peritumoral and intratumoral SMI blood flow structures, were associated with the diagnosis of HGG. Among the 48 gliomas, 22 (45.8%) were IDH1-positive and 26 (54.2%) were IDH1-negative, with no statistical difference in age between the two groups and a statistical difference in histological grading (p < 0.05). There was a statistical difference between IDH1 mutant and wild type in terms of peritumoral edema and SMI intratumoral and peritumoral tissue vascular architecture. Logistic regression models showed that intratumoral and peritumoral tissue SMI vascular architecture was a valid predictor of IDH1 positivity, with a classification accuracy of 81.3%, sensitivity of 90.9%, and specificity of 73.1%. Further group analysis of mutant Young’s modulus values in LGG were higher than wild-type Young’s modulus values (p = 0.031). Conclusions: Peritumoral and intratumoral tissue SMI vascular architecture was a valid predictor of IDH1 positivity. Based on intraoperative ultrasound multimodality images, we can preoperatively determine the expression of molecular markers of lesions, which is of clinical significance for optimizing surgical strategies and predicting prognosis. Full article

Background: Peritonitis in peritoneal dialysis and cirrhosis remains common and leads to morbidity. Neutrophil gelatinase-associated lipocalin (NGAL) has been evaluated as a rapid adjunctive biomarker. Methods: Following PRISMA-DTA and PROSPERO registration (CRD420251105563), we searched MEDLINE, Embase, Cochrane Library, LILACS, Scopus, and Web of Science from inception to 31 December 2024, and ran an update on 30 June 2025 (no additional eligible studies). Diagnostic accuracy studies measuring NGAL in peritoneal/ascitic fluid against guideline reference standards were included. When 2 × 2 data were not reported, we reconstructed cell counts from published metrics using a prespecified, tolerance-bounded algorithm (two studies). Accuracy was synthesized with a bivariate random effects (Reitsma) model; 95% prediction intervals (PIs) were used to express heterogeneity; small-study effects were assessed by Deeks’ test. Results: Thirteen studies were included qualitatively and ten were entered into a meta-analysis (573 cases; 833 controls). The pooled sensitivity was 0.95 (95% CI, 0.90–0.97) and specificity was 0.86 (0.70–0.94); likelihood ratios were LR+ ≈7.0 and LR− 0.06. Between-study variability was concentrated on specificity: the PI for a new setting was 0.75–0.98 for sensitivity and 0.23–0.99 for specificity. Deeks’ test showed evidence of small-study effects in the primary analysis; assay/platform and thresholding contributed materially to heterogeneity. Conclusions: NGAL in peritoneal/ascitic fluid demonstrates high pooled sensitivity but variable specificity across settings. Given the wide prediction intervals and the signal for small-study effects, NGAL should be interpreted as an adjunct to guideline-based criteria—not as a stand-alone rule-out test. Standardization of pre-analytics and assay-specific, locally verified thresholds, together with prospective multicenter validations and impact/economic evaluations, are needed to define its clinical role. Full article

Background: Non-ischemic dilated cardiomyopathy (DCM) is a heterogeneous myocardial disease associated with variable progression and an increased risk of major adverse cardiovascular events (MACEs). Cardiovascular magnetic resonance (CMR) allows the comprehensive evaluation of myocardial structure, function, and fibrosis. This prospective study aimed to assess the prognostic value of CMR-derived global longitudinal strain (GLS) and left ventricular (LV) torsion in patients with DCM. Methods: We prospectively enrolled 150 patients with newly diagnosed non-ischemic DCM and 100 age- and sex-matched healthy controls. All participants underwent standardized CMR protocols including cine imaging, late gadolinium enhancement (LGE), and feature-tracking analysis for myocardial deformation. LV volumes, ejection fraction (LVEF), GLS, and LV torsion were quantified. The primary endpoint was the first occurrence of MACE, defined as cardiac death, sustained ventricular arrhythmia, or heart failure hospitalization. The median follow-up was 33 months. Results: Compared to controls, DCM patients had significantly impaired LV function and myocardial mechanics: lower LVEF (35.1% vs. 65.2%, p < 0.001), reduced GLS (−9.2% vs. −19.7%, p < 0.001), and diminished LV torsion (1.04 vs. 1.95 °/cm, p < 0.001). GLS ≤ −8.6% was independently associated with increased MACE risk (adjusted hazard ratio [HR]: 1.09; 95% confidence interval [CI]: 1.01–1.61; p < 0.01). Similarly, reduced LV torsion predicted adverse events (adjusted HR: 1.37; 95% CI: 1.03–1.81; p < 0.01). The presence of LGE (42% of patients) further stratified risk (HR: 2.86; 95% CI: 1.48–12.52; p < 0.001). Conclusions: CMR-derived GLS and LV torsion are strong, independent predictors of adverse outcomes in DCM. Their integration into routine imaging protocols enhances risk stratification beyond conventional metrics such as LVEF and LGE. These findings support the use of myocardial deformation analysis in the comprehensive evaluation of patients with DCM. Full article

Conventional breast cancer screening programs are predominantly age-based, applying uniform intervals and modalities across broad populations. While this model has reduced mortality, it entails harms—including overdiagnosis, false positives, and missed interval cancers—prompting interest in risk-stratified approaches. In recent years, artificial intelligence (AI) has emerged as a critical enabler of this paradigm shift. This narrative review examines how AI-driven tools are advancing breast cancer screening toward personalization, with a focus on mammographic risk models, multimodal risk prediction, and AI-enabled clinical decision support. We reviewed studies published from 2015 to 2025, prioritizing large cohorts, randomized trials, and prospective validations. AI-based mammographic risk models generally improve discrimination versus classical models and are being externally validated; however, evidence remains heterogeneous across subtypes and populations. Emerging multimodal models integrate genetics, clinical data, and imaging; AI is also being evaluated for triage and personalized intervals within clinical workflows. Barriers remain—explainability, regulatory validation, and equity. Widespread adoption will depend on prospective clinical benefit, regulatory alignment, and careful integration. Overall, AI-based mammographic risk models generally improve discrimination versus classical models and are being externally validated; however, evidence remains heterogeneous across molecular subtypes, with signals strongest for ER-positive disease and limited data for fast-growing and interval cancers. Prospective trials demonstrating outcome benefit and safe interval modification are still pending. Accordingly, adoption should proceed with safeguards, equity monitoring, and clear separation between risk prediction, lesion detection, triage, and decision-support roles Full article

Background: Left ventricular myocardial work (MW) derived from non-invasive pressure–strain loops has emerged as a load-adjusted index of contractile performance. Its value for risk stratification in advanced heart failure (HF) remains uncertain. Methods: We retrospectively studied 151 consecutive patients with advanced HF undergoing comprehensive evaluation at our tertiary centre between January 2016 and December 2022. MW parameters—left ventricular global work index (LVGWI), global constructive work (LVGCW), global wasted work (LVGWW) and global work efficiency (LVGWE)—were derived from speckle-tracking echocardiography integrated with brachial blood pressure. Cardiopulmonary exercise testing (CPET), right heart catheterisation (RHC) and biochemical markers were obtained. Patients were stratified according to an LVGWI threshold of 600 mmHg%, identified by receiver operating characteristic (ROC) analysis for predicting the combined end point of cardiovascular mortality or HF hospitalisation. Correlations between MW and traditional indices were assessed, and event-free survival was analysed by Kaplan–Meier curves. Results: LVGWI correlated modestly with pVO₂ (r = 0.35, p = 0.01) and left ventricular ejection fraction (r = 0.42, p < 0.001) and inversely with NT-proBNP (r = −0.30, p = 0.03). LVGWI displayed the largest area under the curve (AUC 0.76 [95% confidence interval 0.65–0.85]) for predicting the combined end point compared with pVO₂ (AUC 0.73) and LVEF (AUC 0.67). Dichotomisation by LVGWI ≤ 600 mmHg% identified a high-risk group (Group A) with worse NYHA class, lower systolic blood pressure and reduced exercise capacity. After a median follow-up of 24 months, Group A exhibited significantly lower event-free survival (log-rank p = 0.02). Multivariable analysis was not performed owing to the limited sample size; therefore, findings should be interpreted with caution. Conclusions: In patients with advanced HF, left ventricular myocardial work, particularly LVGWI, provides incremental prognostic information beyond conventional markers. An LVGWI cut-off of 600 mmHg% derived from ROC analysis identified patients at increased risk of cardiovascular events and may inform timely referral for mechanical circulatory support or transplantation. Larger prospective studies are warranted to confirm these observations and to establish standardised thresholds across vendors. Full article

Background: Postoperative wound complications following total joint arthroplasty (TJA) significantly impact patient outcomes and healthcare costs. Reliable preoperative biomarkers for identifying patients at increased risk are critical for optimizing patient management and reducing complication rates. This study evaluated the predictive utility of the C-reactive protein to albumin ratio (CAR) and the prognostic nutritional index (PNI) for periprosthetic joint infection (PJI) and postoperative wound complications in patients undergoing total hip arthroplasty (THA) and total knee arthroplasty (TKA). Methods: We retrospectively studied patients who underwent primary THA and TKA in our department from March 2019 to April 2024. The study included a total of 842 patients (568 knees and 274 hips). Preoperative blood samples were assessed for serum CRP, albumin, and total lymphocyte count, facilitating the calculation of CAR and PNI values. Patient outcomes were monitored, identifying PJI and aseptic wound complications such as persistent wound drainage, hematoma, seroma, skin erosion, and wound dehiscence within 2 weeks post-surgery. Results: The average follow-up time for patients was 39.2 months (range 13–73 months). PJI was significantly linked with elevated admission CAR and diminished PNI ratio (p < 0.001 and p < 0.001). ROC analysis demonstrated optimal predictive cut-off values for CAR at 3.1 (Area under curve [AUC]: 0.92, specificity 97.4%, sensitivity 92.3%) and PNI at 49.4 (AUC: 0.93, specificity 94.7%, sensitivity 91.7%). Furthermore, both CAR (Odds ratio [OR]: 3.84, 95% confidence interval [CI]: 1.6–9.1, p = 0.002) and PNI (OR: 21.8, 95% CI: 9–48.6, p < 0.001) were identified as two independent risk factors associated with the development of PJI following THA or TKA. Further subgroup analysis revealed distinct predictive thresholds for CAR and PNI according to surgical procedure type (TKA and THA), enhancing diagnostic accuracy. Conclusions: Preoperative admission elevated CAR and decreased PNI effectively predict PJI and postoperative wound complications in THA and TKA, supporting their utility as simple, cost-effective biomarkers in clinical practice. Incorporating CAR and PNI evaluations into preoperative assessments can enhance patient stratification and preventive strategies, thus mitigating risks and improving surgical outcomes. Full article

To address the challenges of complex harmonic characteristics, multi-source coupling, and strong time variability in aggregated loads downstream of high-voltage substations, this paper proposes an Adaptive Multi-Task Gaussian Process Regression (AMT-GPR) method for harmonic modeling. First, field measurements from the medium-voltage side of a 500 kV substation are denoised and analyzed using Fourier transform to reveal the dynamic patterns and interdependencies of harmonic current magnitudes. Then, a multi-task GPR framework is constructed, incorporating task correlation modeling and adaptive kernel functions to capture inter-task coupling and differences in feature scales. Finally, a probabilistic harmonic model is developed based on multiple sets of measured data, and the modeling performance of AMT-GPR is compared with single-task GPR, conventional MT-GPR, and mainstream machine learning approaches including RBF, LS-SVM, and LSTM. Simulation results demonstrate that traditional harmonic modeling methods are insufficient to capture the dynamic behavior and uncertainty of aggregated loads and AMT-GPR maintains strong robustness under small-sample conditions, significantly reduces prediction errors, and yields narrower uncertainty intervals, outperforming the baseline models. These findings validate the effectiveness of the proposed method in modeling harmonics of aggregated loads in high-voltage substations and provide theoretical support for subsequent harmonic assessment and mitigation strategies. Full article

Integration of IoT and predictive modeling is critical for optimizing microclimate management in urban-agglomeration vertical farming. In this study, we present a hybrid digital twin approach that combines a physical microclimate model with a distributed IoT monitoring system to simulate and control the phytotron environment. A set of heat- and mass-balance equations governing the dynamics of temperature, humidity, and transpiration was implemented and parameterized using a genetic algorithm (GA)—an evolutionary optimization method—with real-time data collected over three intervals (72 h, 90 h, and 110 h) from LoRaWAN sensors (temperature, humidity, CO₂) and Wi-Fi-connected power meters managed by Home Assistant. The optimized model achieved mean temperature deviations ≤ 0.1 °C, relative humidity errors ≤ 2%, and overall energy consumption accuracy of 99.5% compared to measured values. The digital twin reliably tracked daily climate fluctuations and system energy use, confirming the accuracy of the hybrid approach. These results demonstrate that the proposed framework effectively integrates theoretical models with IoT-derived data to deliver precise environmental control and energy-use optimization in vertical farming, while also laying the groundwork for scalable digital twins in controlled-environment agriculture. Full article

For the first time, this study presents a cyber–physical framework that reconciles the long-standing conflict between transparent queue analytics and adaptive machine learning in flexible manufacturing systems. Deterministic indicators, utilization, expected queue length, waiting time, and idle probability, are fused with topological embeddings of the routing graph and ingested by a graph convolutional network that predicts station congestion with calibrated confidence intervals. Shapley additive explanations decompose every forecast into causal contributions, and these vectors, together with a percentile-based risk metric, steer a mixed-integer genetic optimizer toward schedules that lift throughput without breaching statistical congestion limits. A cloud dashboard streams forecasts, risk bands, and color-coded explanations, allowing supervisors to accept or modify suggestions; each manual correction is logged and injected into nightly retraining, closing a socio-technical feedback loop. Experiments on an 8704-cycle production census demonstrate a 38 percent reduction in average queue length and a 12 percent rise in throughput while preserving full audit traceability, enabling one-minute rescheduling on volatile shop floors. The results confirm that transparency and adaptivity can coexist when analytical priors, explainable learning, and risk-aware search are unified in a single containerized control stack. Full article

Electric vehicles (EVs) and thermostatically controlled loads (TCLs) are key demand-side resources for load regulation in modern power systems. However, effective load regulation faces significant challenges due to the stochastic nature of EV travel times and environmental uncertainties, such as temperature and solar irradiation fluctuations affecting TCL performance. Additionally, load rebound effects, caused by TCLs increasing power consumption to restore preset indoor temperatures after regulation, may induce secondary demand peaks, thereby offsetting regulation benefits. To address these challenges, this study aims to meet regulation requirements under such uncertainties while mitigating rebound-induced peaks. A rolling-horizon co-optimization method for EV and TCL clusters is proposed, which explicitly considers both uncertainties, load rebound effects and economic losses. First, to address the limited regulation capacity of individual EVs and TCLs, a user clustering mechanism is developed based on willingness to participate in demand response across multiple time intervals. A load rebound evaluation model for TCL clusters is developed to characterize post-regulation load variations and assess the rebound intensity. Subsequently, a load rebound-aware co-optimization model is proposed and solved within a rolling-horizon optimization approach, which performs rolling optimization within each prediction horizon to determine the participating clusters and their regulation capacities for each execution time slot under uncertainties. Simulation results demonstrate that the proposed method, compared with conventional day-ahead and robust optimization, not only meets load regulation requirements under uncertainty, but also effectively mitigates rebound-induced secondary peaks while achieving economic benefits. Full article

The Maxwell–Boltzmann (MB) distribution is fundamental in statistical physics, providing an exact description of particle speed or energy distributions. In this study, a discrete formulation derived via the survival function discretization technique extends the MB model’s theoretical strengths to realistically handle lifetime and reliability data recorded in integer form, enabling accurate modeling under inherently discrete or censored observation schemes. The proposed discrete MB (DMB) model preserves the continuous MB’s flexibility in capturing diverse hazard rate shapes, while directly addressing the discrete and often censored nature of real-world lifetime and reliability data. Its formulation accommodates right-skewed, left-skewed, and symmetric probability mass functions with an inherently increasing hazard rate, enabling robust modeling of negatively skewed and monotonic-failure processes where competing discrete models underperform. We establish a comprehensive suite of distributional properties, including closed-form expressions for the probability mass, cumulative distribution, hazard functions, quantiles, raw moments, dispersion indices, and order statistics. For parameter estimation under Type-II censoring, we develop maximum likelihood, Bayesian, and bootstrap-based approaches and propose six distinct interval estimation methods encompassing frequentist, resampling, and Bayesian paradigms. Extensive Monte Carlo simulations systematically compare estimator performance across varying sample sizes, censoring levels, and prior structures, revealing the superiority of Bayesian–MCMC estimators with highest posterior density intervals in small- to moderate-sample regimes. Two genuine datasets—spanning engineering reliability and clinical survival contexts—demonstrate the DMB model’s superior goodness-of-fit and predictive accuracy over eleven competing discrete lifetime models. Full article