Search Results (57,741)

Background: Millimetric ST-segment elevation (STEMI) rules miss more than half of angiographic coronary occlusions. Re-casting acute infarction as Occlusion MI (OMI) versus Non-Occlusion MI (NOMI) and embedding that paradigm in Bayesian reasoning could shorten time to reperfusion while limiting unnecessary activations. Methods: We derived age- and sex-specific baseline prevalences of OMI from national emergency-department surveillance data and contemporary angiographic series. Pre-test probabilities were adjusted with published likelihood ratios (LRs) for chest-pain descriptors and clinical risk factors, then updated again with either (1) the stand-alone accuracy of ST-elevation or (2) the pooled accuracy of a broader OMI ECG spectrum. Two decision thresholds were prespecified: post-test probability >10% to trigger catheterization and >75% to justify fibrinolysis when angiography was unavailable. The framework was applied to five consecutive real-world cases that had elicited diagnostic disagreement in clinical practice. Results: The Bayesian scaffold re-classified three “NSTEMI” tracings as intermediate or high-probability OMI (post-test 27–65%) and prompted immediate reperfusion; each was confirmed as a totally occluded artery. A fourth patient with crushing pain and a normal ECG retained a 17% post-ECG probability and was later found to have an occluded circumflex. The fifth case, an apparent South-African-Flag pattern, initially rose to 75% but fell after a normal bedside echo and normal troponins. Conclusions: Layering pre-test context with sign-specific LRs transforms ECG interpretation from a binary rule into a transparent probability calculation. The OMI/NOMI Bayesian framework detected occult occlusions that classic STEMI criteria missed. Full article

Background: Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by both motor and non-motor symptoms, including autonomic dysfunction. Structural alterations in the vagus nerve (VN) may contribute to PD pathophysiology, though existing data remain inconsistent. Objective: This study aimed to evaluate morphological changes in the VN using high-resolution ultrasound (USVN) and to investigate associations with autonomic symptoms, heart rate variability (HRV), and clinical characteristics in PD patients. Methods: A cross-sectional study was conducted involving 60 PD patients and 60 age- and sex-matched healthy controls. USVN was performed to assess VN cross-sectional area (CSA), echogenicity, and homogeneity bilaterally. Autonomic symptoms were measured using the Composite Autonomic Symptom Scale 31 (COMPASS-31). HRV parameters—SDNN, RMSSD, and pNN50—were obtained via 24 h Holter monitoring. Additional clinical data included Unified Parkinson’s Disease Rating Scale (UPDRS) scores, transcranial sonography findings, and third ventricle width. Results: PD patients showed significantly reduced VN CSA compared to controls (right: 1.90 ± 0.19 mm² vs. 2.07 ± 0.18 mm²; left: 1.74 ± 0.21 mm² vs. 1.87 ± 0.22 mm²; p < 0.001 and p < 0.02). Altered echogenicity and decreased homogeneity were also observed. Right VN CSA correlated with body weight, third ventricle size, and COMPASS-31 scores. Left VN CSA was associated with body size parameters and negatively correlated with RMSSD (p = 0.025, r = −0.21), indicating reduced vagal tone. Conclusions: USVN detects structural VN changes in PD, correlating with autonomic dysfunction. These findings support its potential as a non-invasive biomarker for early autonomic involvement in PD. Full article

Predictive maintenance (PdM) is a cornerstone of smart manufacturing, enabling the early detection of equipment degradation and reducing unplanned downtimes. This study proposes an advanced machine learning framework that integrates the Extreme Learning Machine (ELM) with a novel hybrid metaheuristic optimization algorithm, the Polar Lights Salp Cooperative Optimizer (PLSCO), to enhance predictive modeling in manufacturing processes. PLSCO combines the strengths of the Polar Light Optimizer (PLO), Competitive Swarm Optimization (CSO), and Salp Swarm Algorithm (SSA), utilizing a cooperative strategy that adaptively balances exploration and exploitation. In this mechanism, particles engage in a competitive division process, where winners intensify search via PLO and losers diversify using SSA, effectively avoiding local optima and premature convergence. The performance of PLSCO was validated on CEC2015 and CEC2020 benchmark functions, demonstrating superior convergence behavior and global search capabilities. When applied to a real-world predictive maintenance dataset, the ELM-PLSCO model achieved a high prediction accuracy of 95.4%, outperforming baseline and other optimization-assisted models. Feature importance analysis revealed that torque and tool wear are dominant indicators of machine failure, offering interpretable insights for condition monitoring. The proposed approach presents a robust, interpretable, and computationally efficient solution for predictive maintenance in intelligent manufacturing environments. Full article

Understory-cultivated Panax ginseng possesses high pharmacological and economic value; however, its visual quality grading predominantly relies on subjective manual assessment, constraining industrial scalability. To address challenges including fine-grained morphological variations, boundary ambiguity, and complex natural backgrounds, this study proposes Ginseng-YOLO, a lightweight and deployment-friendly object detection model for automated ginseng grade classification. The model is built on the YOLOv11n (You Only Look Once11n) framework and integrates three complementary components: (1) C2-LWA, a cross-stage local window attention module that enhances discrimination of key visual features, such as primary root contours and fibrous textures; (2) ADown, a non-parametric downsampling mechanism that substitutes convolution operations with parallel pooling, markedly reducing computational complexity; and (3) Slide Loss, a piecewise IoU-weighted loss function designed to emphasize learning from samples with ambiguous or irregular boundaries. Experimental results on a curated multi-grade ginseng dataset indicate that Ginseng-YOLO achieves a Precision of 84.9%, a Recall of 83.9%, and an mAP@50 of 88.7%, outperforming YOLOv11n and other state-of-the-art variants. The model maintains a compact footprint, with 2.0 M parameters, 5.3 GFLOPs, and 4.6 MB model size, supporting real-time deployment on edge devices. Ablation studies further confirm the synergistic contributions of the proposed modules in enhancing feature representation, architectural efficiency, and training robustness. Successful deployment on the NVIDIA Jetson Nano demonstrates practical real-time inference capability under limited computational resources. This work provides a scalable approach for intelligent grading of forest-grown ginseng and offers methodological insights for the design of lightweight models in medicinal plants and agricultural applications. Full article

Background/Objective: Sacubitril/Valsartan are a combination drug approved for heart failure treatment, known to enhance natriuretic peptide activity and inhibit the renin–angiotensin–aldosterone system (RAAS). While its clinical efficacy is well-established, its broader impact on human metabolism remains insufficiently characterized. This study aimed to explore the time-resolved metabolic changes induced by Sacubitril/Valsartan in healthy individuals using an untargeted metabolomics approach. Methods: Fourteen healthy male volunteers received a single oral dose of Sacubitril/Valsartan (200 mg; 97.2 mg Sacubitril and 102.8 mg Valsartan) across two phases separated by a two-week washout period. Plasma samples were collected at eight individualized time points based on pharmacokinetic profiles. Metabolites were extracted and analyzed using high-resolution liquid chromatography–mass spectrometry (LC-QToF HRMS). Data processing included peak alignment, annotation via HMDB and METLIN, and statistical modeling through multivariate (PLS-DA, OPLS-DA) and univariate (ANOVA with FDR correction) analyses. Results: Out of 20,472 detected features, 13,840 were retained after quality filtering. A total of 315 metabolites were significantly dysregulated (FDR p < 0.05), of which 31 were confidently annotated as endogenous human metabolites. Among these, key changes were observed in the pyrimidine metabolism pathway, particularly elevated levels of uridine triphosphate (UTP) associated with cellular proliferation and metabolic remodeling. OPLS-DA models demonstrated clear separation between pre-dose and Cmax samples (R²Y = 0.993, Q² = 0.768), supporting the robustness of the time-dependent effects. Conclusions: This is the first study to characterize the dynamic metabolomic signature of Sacubitril/Valsartan in healthy humans. The findings reveal a distinctive perturbation in pyrimidine metabolism, suggesting possible links to drug mechanisms relevant to cardiac cell cycle regulation. These results underscore the utility of untargeted pharmacometabolomics in uncovering systemic drug effects and highlight potential biomarkers for monitoring therapeutic response or guiding precision treatment strategies in heart failure. Full article

Non-cooperative spatial target detection plays a vital role in enabling autonomous on-orbit servicing and maintaining space situational awareness (SSA). However, due to the limited computational resources of onboard embedded systems and the complexity of spaceborne imaging environments, where spacecraft images often contain small targets that are easily obscured by background noise and characterized by low local information entropy, many existing object detection frameworks struggle to achieve high accuracy with low computational cost. To address this challenge, we propose YOLO-GRBI, an enhanced detection network designed to balance accuracy and efficiency. A reparameterized ELAN backbone is adopted to improve feature reuse and facilitate gradient propagation. The BiFormer and C2f-iAFF modules are introduced to enhance attention to salient targets, reducing false positives and false negatives. GSConv and VoV-GSCSP modules are integrated into the neck to reduce convolution operations and computational redundancy while preserving information entropy. YOLO-GRBI employs the focal loss for classification and confidence prediction to address class imbalance. Experiments on a self-constructed spacecraft dataset show that YOLO-GRBI outperforms the baseline YOLOv8n, achieving a 4.9% increase in mAP@0.5 and a 6.0% boost in mAP@0.5:0.95, while further reducing model complexity and inference latency. Full article

Background/Objectives: Hepatocellular carcinoma (HCC) remains a leading cause of global cancer mortality, with increasing incidence and persistently poor survival. Hepatitis C virus (HCV) is a major risk factor for HCC, and while the advent of direct-acting antivirals (DAAs) has significantly altered HCV-related hepatocellular cancer (HCC-HCV) risk, the global burden remains substantial. With the World Health Organization (WHO) aiming to reduce hepatitis-related deaths by 2030, we set out to evaluate global HCC-HCV trends from 1990 to 2021, stratified by sex, WHO region, and sociodemographic index (SDI), using data from the Global Burden of Disease (GBD) 2021 study. Methods: We analyzed age-standardized incidence (ASI), deaths, and disability-adjusted life years (DALYs) due to HCV-HCC from 1990 to 2021 using GBD 2021 data. Trends were stratified by WHO region, sociodemographic index (SDI), and sex. Joinpoint regression modeling was used to identify statistically significant temporal inflection points and calculate the annual percent change (APC) in unique time segments and average annual percent change (AAPC) over the entire study period (1990 to 2021). Results: Globally, deaths and DALYs attributable to HCV-HCC increased over the study period while ASI declined modestly. The region of the Americas exhibited the highest AAPC in all three metrics, potentially driven by an aging HCV-infected population, rising comorbidities (e.g., obesity, diabetes), and improved case detection. Nevertheless, on a global level, high-SDI regions showed the most favorable trends, particularly after 2016, likely reflecting the earlier adoption of DAAs and a differential success of WHO goals. Lower-SDI regions continued to exhibit increasing burden. Notably, ASI began to rise again between 2019 and 2021, suggesting an ongoing need to critically evaluate and restructure our approach to reducing HCV and HCV-HCC. Conclusions: Our findings underscore the urgent need for equity-driven, region-specific strategies to achieve better control of this highly morbid disease. Full article

Given the critical importance of timely forest fire detection to mitigate environmental and socio-economic consequences, this research aims to achieve high detection accuracy while maintaining real-time operational efficiency, with a particular focus on minimizing computational complexity. We propose a novel framework that systematically integrates normalization, feature selection, adaptive oversampling, and classifier optimization to enhance detection performance while minimizing computational overhead. The evaluation is conducted using three distinct Canadian forest fire datasets: Alberta Forest Fire (AFF), British Columbia Forest Fire (BCFF), and Saskatchewan Forest Fire (SFF). Initial classifier benchmarking identified the best-performing tree-based model, followed by normalization and feature selection optimization. Next, four oversampling methods were evaluated to address class imbalance. An ablation study quantified the contribution of each module to overall performance. Our targeted, stepwise strategy eliminated the need for exhaustive model searches, reducing computational cost by 97.75% without compromising accuracy. Experimental results demonstrate substantial improvements in F1-score, AFF (from 69.12% to 82.75%), BCFF (61.95% to 77.91%), and SFF (90.03% to 96.18%) alongside notable reductions in False Negative Rates compared to baseline models. Full article

A key requirement in containing contagious diseases, like the COVID-19 pandemic, is the ability to efficiently carry out mass diagnosis over large populations, especially when testing resources are limited and rapid identification is essential for outbreak control. Some of the leading testing procedures, such as those utilizing qualitative polymerase chain reaction, involve using dedicated machinery which can simultaneously process a limited amount of samples. A candidate method to increase the test throughput is to examine pooled samples comprised of a mixture of samples from different patients. In this work, we study pooling-based tests which operate in a one-shot fashion, while providing an indication not solely on the presence of infection, but also on its level, without additional pool-tests, as often required in COVID-19 testing. As these requirements limit the application of traditional group-testing (GT) methods, we propose a multi-level GT scheme, which builds upon GT principles to enable accurate recovery using much fewer tests than patients, while operating in a one-shot manner and providing multi-level indications. We provide a theoretical analysis of the proposed scheme and characterize conditions under which the algorithm operates reliably and at affordable computational complexity. Our numerical results demonstrate that multi-level GT accurately and efficiently detects infection levels, while achieving improved performance and less pooled tests over previously proposed oneshot COVID-19 pooled-testing methods. Our simulations show that the efficient method proposed in this work can correctly identify the infected items and their infection levels with high probability at the known upper bound (for a maximum likelihood decoder in GT) on the number of tests. We also show that the method works well in practice when the number of infected items is not assumed to be known in advance. Full article

Lead contamination remains a critical global concern due to its persistent toxicity, bioaccumulative nature, and widespread occurrence in water, food, and industrial environments. The accurate, cost-effective, and rapid detection of lead ions (Pb²⁺) is essential for protecting public health and ensuring environmental safety. Among the available techniques, potentiometric sensors, particularly ion-selective electrodes (ISEs), have emerged as practical tools owing to their simplicity, portability, low power requirements, and high selectivity. This review summarizes recent progress in lead-selective potentiometry, with an emphasis on electrode architectures and material innovations that enhance analytical performance. Reported sensors achieve detection limits as low as 10⁻¹⁰ M, broad linear ranges typically spanning 10⁻¹⁰–10⁻² M, and near-Nernstian sensitivities of ~28–31 mV per decade. Many designs also demonstrate reproducible responses in complex matrices. Comparative analysis highlights advances in traditional liquid-contact electrodes and modern solid-contact designs modified with nanomaterials, ionic liquids, and conducting polymers. Current challenges—including long-term stability, calibration frequency, and selectivity against competing metal ions—are discussed, and future directions for more sensitive, selective, and user-friendly Pb²⁺ sensors are outlined. Full article

With the rapid expansion of multi-platform remote sensing applications, cloud contamination significantly impedes cross-platform data utilization. Current cloud detection methods face critical technical challenges in cross-platform settings, including neglect of atmospheric radiative transfer mechanisms, inadequate multi-scale structural decoupling, high intra-class variability coupled with inter-class similarity, cloud boundary ambiguity, cross-modal feature inconsistency, and noise propagation in pseudo-labels within semi-supervised frameworks. To address these issues, we introduce a Physics-Guided Multi-Representation Network (PGMRN) that adopts a student–teacher architecture and fuses tri-modal representations—Pseudo-NDVI, structural, and textural features—via atmospheric priors and intrinsic image decomposition. Specifically, PGMRN first incorporates an InfoNCE contrastive loss to enhance intra-class compactness and inter-class discrimination while preserving physical consistency; subsequently, a boundary-aware regional adaptive weighted cross-entropy loss integrates PA-CAM confidence with distance transforms to refine edge accuracy; furthermore, an Uncertainty-Aware Quadruple Consistency Propagation (UAQCP) enforces alignment across structural, textural, RGB, and physical modalities; and finally, a dynamic confidence-screening mechanism that couples PA-CAM with information entropy and percentile-based thresholding robustly refines pseudo-labels. Extensive experiments on four benchmark datasets demonstrate that PGMRN achieves state-of-the-art performance, with Mean IoU values of 70.8% on TCDD, 79.0% on HRC_WHU, and 83.8% on SWIMSEG, outperforming existing methods. Full article

Background: Dual antiplatelet therapy (DAPT) with a potent P2Y12 inhibitor is recommended for patients with acute coronary syndrome (ACS) following percutaneous coronary intervention (PCI). On-treatment platelet reactivity has been associated with ischemic endpoints and may vary between male and female patients. We, therefore, investigated sex-related differences in on-treatment platelet reactivity in ACS patients receiving ticagrelor or prasugrel. Methods: Maximal platelet aggregation by light-transmission aggregometry (LTA) and platelet surface P-selectin expression in response to arachidonic acid (AA), ADP, collagen, TRAP (a protease-activated receptor [PAR-1] agonist), and AYPGKF (a PAR-4 agonist) were assessed in 80 prasugrel- and 77 ticagrelor-treated patients 3 days after PCI. Results: In the overall study population (n = 157), women were older and had lower serum creatinine, hemoglobin, and hematocrit levels than men (all p < 0.05). Women exhibited higher ADP-inducible platelet aggregation in response to both 10 μM and 5 μM of ADP (both p < 0.05), while no sex-related differences were observed for AA-, TRAP-, collagen-, or AYPGKF-inducible platelet aggregation and agonist-inducible platelet surface P-selectin expression. In prasugrel-treated patients, women had higher ADP-inducible platelet aggregation and P-selectin expression compared with men (both p < 0.05), whereas no sex-related differences were found in ticagrelor-treated patients. In the multivariate linear regression analyses, female sex remained an independent predictor of higher platelet aggregation in response to 5 μM of ADP in prasugrel-treated patients (p < 0.05). High on-treatment residual platelet reactivity (HRPR) in response to AA was detected in four patients, and HRPR ADP was seen in seven patients, with no significant differences between female and male ACS patients (both p > 0.05). Low on-treatment residual platelet reactivity (LRPR) in response to AA was identified in 153 patients and LRPR ADP was present in 80 patients, with a higher prevalence of LRPR ADP in men (p = 0.01). Conclusions: Female ACS patients on prasugrel exhibited higher ADP-inducible platelet aggregation than male patients, while no sex-related differences were observed in patients on ticagrelor. Full article

Active galactic nuclei (AGNs) often exhibit broad-line regions (BLRs), populated by high-velocity clouds in approximately Keplerian orbits around the central supermassive black hole (SMBH) at subparsec scales. During episodes of intense accretion at super-Eddington rates, the accretion disk can launch a powerful, radiation-driven wind. This wind may overtake the BLR clouds, forming bowshocks around them. Two strong shocks arise: one propagating into the wind, and the other into the cloud. If the shocks are adiabatic, electrons and protons can be efficiently accelerated via a Fermi-type mechanism to relativistic energies. In sufficiently dense winds, the resulting high-energy photons are absorbed and reprocessed within the photosphere, while neutrinos produced in inelastic $pp$ collisions escape. In this paper, we explore the potential of super-accreting AGNs as neutrino sources. We propose a new class of neutrino emitter: an AGN lacking jets and gamma-ray counterparts, but hosting a strong, opaque, disk-driven wind. As a case study, we consider a supermassive black hole with $M_{\mathrm{BH}}={10}^6{M}_{\odot }$ and accretion rates consistent with tidal disruption events (TDEs). We compute the relevant cooling processes for the relativistic particles under such conditions and show that super-Eddington accreting SMBHs can produce detectable neutrino fluxes with only weak electromagnetic counterparts. The neutrino flux may be observable by the next-generation IceCube Observatory (IceCube-Gen2) in nearby galaxies with a high BLR cloud filling factor. For galaxies hosting more massive black holes, detection is also possible with moderate filling factors if the source is sufficiently close, or at larger distances if the filling factor is high. Our model thus provides a new and plausible scenario for high-energy extragalactic neutrino sources, where both the flux and timescale of the emission are determined by the number of clouds orbiting the black hole and the duration of the super-accreting phase. Full article

We propose a novel telescope concept based on Earth’s gravitational lensing effect, optimized for the detection of distant dark matter sources, particularly axion-like particles (ALPs). When a unidirectional flux of dark matter passes through Earth at sufficiently high velocity, gravitational lensing can concentrate the flux at a distant focal region in space. Our method combines this lensing effect with stimulated backward reflection (SBR), arising from ALP decays that are induced by directing a coherent electromagnetic beam toward the focal point. The aim of this work is to numerically analyze the structure of the focal region and to develop a framework for estimating the sensitivity to ALP–photon coupling via this mechanism. Numerical calculations show that, assuming an average ALP velocity of 520 km/s—as suggested by the observed stellar stream S1—the focal region extends from $9\times {10}^9$ m to $1.4\times {10}^{10}$ m, with peak density near $9.6\times {10}^9$ m. For a conservative point-like ALP source located approximately 8 kpc from the solar system, based on the S1 stream, the estimated sensitivity in the eV mass range reaches $g/M=\mathcal{O}\left({10}^{-22}\right){\mathrm{GeV}}^{-1}$. This concept thus opens a path toward a general-purpose, space-based ALP observatory that could, in principle, detect more distant sources—well beyond $\mathcal{O}\left(10\right)\mathrm{kpc}$—provided that ALP–photon coupling is sufficiently strong, that is, $M\ll M_{\mathrm{Planck}}$. Full article

Mountain glaciers are the most direct and sensitive indicators of climate change. In the context of global warming, monitoring changes in glacier elevation has become a crucial issue in modern cryosphere research. The Global Ecosystem Dynamics Investigation (GEDI) is a full-waveform laser altimeter with a multi-beam that provides unprecedented measurements of the Earth’s surface. Many studies have investigated its applications in assessing the vertical structure of various forests. However, few studies have assessed GEDI’s performance in detecting variations in glacier elevation in land ice in high-mountain Asia. To address this limitation, we selected the Southeastern Tibetan Plateau (SETP), one of the most sensitive areas to climate change, as a test area to assess the feasibility of using GEDI to monitor glacier elevation changes by comparing it with ICESat-2 ATL06 and the reference TanDEM-X DEM products. Moreover, this study further analyzes the influence of environmental factors (e.g., terrain slope and aspect, and altitude distribution) and glacier attributes (e.g., glacier area and debris cover) on changes in glacier elevation. The results show the following: (1) Compared to ICESat-2, in most cases, GEDI overestimated glacier thinning (i.e., elevation reduction) to some extent from 2019 to 2021, with an average overestimation value of about −0.29 m, while the annual average rate of elevation change was relatively close, at −0.70 ± 0.12 m/yr versus −0.62 ± 0.08 m/yr, respectively. (2) In terms of time, GEDI reflected glacier elevation changes at interannual and seasonal scales, and the trend of change was consistent with that found with ICESat-2. The results indicate that glacier accumulation mainly occurred in spring and winter, while the melting rate accelerated in summer and autumn. (3) GEDI effectively monitored and revealed the characteristics and patterns of glacier elevation changes with different terrain features, glacier area grades, etc.; however, as the slope increased, the accuracy of the reported changes in glacier elevation gradually decreased. Nonetheless, GEDI still provided reasonable estimates for changes in mountain glacier elevation. (4) The spatial distribution of GEDI footprints was uneven, directly affecting the accuracy of the monitoring results. Thus, to improve analyses of changes in glacier elevation, terrain factors should be comprehensively considered in further research. Overall, these promising results have the potential to be used as a basic dataset for further investigations of glacier mass and global climate change research. Full article