Search Results (7,394)

Objective: Visceral adipose tissue is a primary driver of insulin resistance and dysglycemia in type 2 diabetes (T2D), yet its clinical assessment remains challenging. This study aimed to validate neck circumference (NC) as a novel, practical anthropometric biomarker for estimating visceral fat area (VFA) and identifying metabolic risk in a T2D cohort, facilitating its integration into public health and primary care screening strategies. Methods: In a cross-sectional study of 1139 T2D patients, we collected data on NC, biochemical parameters (fasting plasma glucose, hemoglobin A1c, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, triglycerides), and precisely measured VFA and subcutaneous fat area (SFA) via bioelectrical impedance analysis (Omron HDS-2000). We employed Pearson’s correlation and multivariate logistic regression to analyze the relationship between NC and metabolic indicators. Receiver operating characteristic (ROC) curve analysis was used to establish sex-specific NC cut-off values for predicting abnormal VFA. Results: The cohort comprised 687 (60.3%) males and 452 (39.7%) females. NC demonstrated strong positive correlations with VFA (p < 0.001), as did body mass index (BMI), waist–hip ratio (WHR), and SFA. In males, NC was further positively correlated with key metabolic biomarkers including fasting insulin, Insulin Resistance Index, triglycerides, and creatinine. ROC analysis identified NC > 39.5 cm for males and >35.5 cm for females as the optimal cut-off points for detecting abnormal visceral adiposity, highlighting its diagnostic utility. Conclusions: NC serves as a highly accessible and effective biomarker for visceral adiposity and associated metabolic dysfunction in patients with T2D. The established sex-specific cut-off values provide a simple, non-invasive tool for risk stratification in clinical and public health settings, enabling early intervention and improved management of metabolic disease. Full article

Objectives: This study aims to explore the therapeutic effect and mechanism of stir-roasted deer velvet antler with ghee (ZLR) on Non-Alcoholic Fatty Liver Disease (NAFLD). Methods: This study used proteomics to analyze the protein composition of roasted deer antler velvet. It established a high-fat diet (HFD)-induced NAFLD rat model and evaluated the therapeutic effects of different dosage groups, including liver injury, oxidative stress, glucose metabolism, steatosis, and insulin homeostasis (via fasting glucose tolerance). Transcriptomics explored the mechanism. Gene expression and Western blot detected lipid metabolism-related gene expression. In vivo experiments validated that ZLR-containing serum alleviates NAFLD and reduces reactive oxygen species levels. Results: The results indicated that ZLR could significantly reduce the body weight, liver weight and degree of hepatic steatosis in HFD rats, improve glycolipid metabolism and insulin sensitivity, and alleviate oxidative stress damage. The mechanism involves activating the adenosine monophosphate-activated protein kinase/peroxisome proliferator-activated receptor (AMPK/PPAR) signaling pathway, regulating the expression of lipid metabolism-related genes, promoting fatty acid oxidation, and reducing fat deposition. The results of in vitro experiments show that ZLR-containing serum can effectively reduce lipid droplet production in liver cells and effectively alleviate oxidative stress damage in liver cells. Conclusions: The traditional Chinese medicine processed product ZLR can regulate lipid metabolism in the body and alleviate the degree of NAFLD by activating the AMPK and PPAR signaling pathways. It provides new ideas for the clinical treatment of NAFLD Full article

Operational expenditure in wind farms is heavily influenced by unplanned maintenance, much of which stems from undetected rotor system faults. Although many fault-detection methods have been proposed, most remain confined to laboratory test. Blade-root bending-moment measurements are among the few techniques applied in the field, yet their reliability is limited by strong sensitivity to varying operational and environmental conditions. This study presents a data-driven rotor health-monitoring framework that enhances the diagnostic value of blade bending-moments. Assuming that the wind speed profile remains approximately stationary over short intervals (e.g., 20 s), a machine-learning model is trained on bending-moment data from healthy blades to predict the incident wind-speed profile under a wide range of conditions. During operation, real-time bending-moment signals from each blade are independently processed by the trained model. A healthy rotor yields consistent wind-speed profile predictions across all three blades, whereas deviations for an individual blade indicate rotor asymmetry. In this study, the methodology is verified using high-fidelity OpenFAST simulations with controlled blade pitch misalignment as a representative fault case, providing simulation-based verification of the proposed framework. Results demonstrate that the proposed inverse-modeling and cross-blade consistency framework enables sensitive and robust detection and localization of pitch-related rotor faults. While only pitch misalignment is explicitly investigated here, the approach is inherently applicable to other rotor asymmetry mechanisms such as mass imbalance or aerodynamic degradation, supporting reliable condition monitoring and earlier maintenance interventions. Using OpenFAST simulations, the proposed framework reconstructs height-resolved wind profiles with RMSE below 0.15 m/s (R² > 0.997) under healthy conditions, and achieves up to 100% detection accuracy for moderate-to-severe pitch misalignment faults. Full article

Recent years have witnessed significant growth in the development of propyl gallate (PG) sensors. PG can be monitored by various approaches, such as electrochemical and fluorescence methods. The electrochemical approaches have several advantages, such as low cost, a benign fabrication process, and high sensitivity and selectivity. Similarly, the fluorescence method has its own advantages, including low cost, high sensitivity, and fast response. Both methods are promising approaches for the monitoring of PG compared to chromatographic methods. In this mini-review article, we review the progress in the preparation of materials for the determination of PG using electrochemical and fluorescence methods. The fabrication of electrodes and the working principle for PG detection are illustrated. The challenges and future perspectives for PG detection are discussed. Full article

In this study, the response surface method (RSM) was used to determine the best reaction conditions for the gas-phase hydrogenation of carbon dioxide on a commercial nickel-based catalyst. The RSM was applied in our previous study to find the optimal conditions for the same process carried out in laboratory-scale tubular reactors. The main benefits observed were fast detection of optimal conditions and the high precision of the optimum detected (which was experimentally confirmed). These advantages were due to the small number of experiments conducted and the simplicity of the models employed; only linear and quadratic models were developed. The successful result encouraged us to carry out experiments in a larger-scale reactor—an intermediate between a laboratory plant and a pilot plant. This approach helped us to fix some problems resulting from the larger scale of the process conducted. Despite the difficulties described in the main part of this article, we can recommend using the RSM as a tool for supporting experimentation and substantially speeding up the analysis of results and their introduction into practice. At the process scale considered, maximum carbon dioxide conversion was obtained at a temperature of 354 °C and a ratio of molar fluxes of H₂ to CO₂ equal to 3.9. It should be emphasized that this result was confirmed experimentally. Full article

Differences in metabolic traits between traditional and modern pig breeds may influence their physiological responses to stress hormones. This study evaluated the in vivo metabolic effects of an acute adrenaline challenge in Iberian (obese, slow-growing) and Landrace (lean, fast-growing) pigs (Sus scrofa domesticus). Four Iberian and five Landrace barrows (≈50 kg body weight; BW) fitted with a carotid catheter received an injection of adrenaline (3 µg/kg BW), and serial blood samples were collected for 105 min. Adrenaline transiently increased plasma glucose (p < 0.001) and lactate (p < 0.001) concentrations, both peaking at 5 min post-injection. Iberian pigs showed higher plasma lactate (1.26 vs. 1.03 mM; p = 0.002), triglycerides (0.34 vs. 0.27 mM; p < 0.001), and non-esterified fatty acids (NEFA; 0.38 vs. 0.29 mM; p = 0.021), but lower glucose (4.80 vs. 5.03 mM; p = 0.010) than Landrace pigs, while cholesterol remained unaffected (p > 0.10). No breed × time interaction was detected for any metabolite. The relative increase in glucose reached +47% in Iberian and +27% in Landrace pigs, whereas lactate rose +140% and +113%, respectively, indicating stronger glycolytic activation in Iberian pigs. Despite the limited sample size, the results provide physiologically relevant evidence supporting increased metabolic flexibility in Iberian pigs, characterized by a heightened sensitivity to adrenergic stimulation and associated with enhanced lipolytic and glycolytic responses; however, these conclusions should be interpreted within the specific experimental conditions under which the study was conducted. These findings demonstrate that Iberian pigs have higher metabolic sensitivity to adrenergic stimulation, with enhanced lipolytic and glycolytic activity. In conclusion, breed-dependent differences in stress-related metabolism suggest that Iberian pigs are furnished with increased metabolic flexibility to face short-term stress. Full article

Decisions made by pilots and drivers suffering from depression can endanger the lives of hundreds of people, as demonstrated by the tragedies of Germanwings flight 9525 and Air India flight 171. Since the detection of depression is currently based largely on subjective self-reporting, there is an urgent need for fast, objective, and reliable detection methods. In our study, we present an artificial intelligence-based system that combines iris-based identification with the analysis of pupillometric and eye movement biomarkers, enabling the real-time detection of physiological signs of depression before driving or flying. The two-module model was evaluated based on data from 242 participants: the iris identification module operated with an Equal Error Rate of less than 0.5%, while the depression-detecting CNN-LSTM network achieved 89% accuracy and an AUC value of 0.94. Compared to the neutral state, depressed individuals responded to negative news with significantly greater pupil dilation (+27.9% vs. +18.4%), while showing a reduced or minimal response to positive stimuli (−1.3% vs. +6.2%). This was complemented by slower saccadic movement and longer fixation time, which is consistent with the cognitive distortions characteristic of depression. Our results indicate that pupillometric deviations relative to individual baselines can be reliably detected and used with high accuracy for depression screening. The presented system offers a preventive safety solution that could reduce the number of accidents caused by human error related to depression in road and air traffic in the future. Full article

This paper introduces a cybersecurity framework that combines a deception-based ransomware detection system, called the Intrusion and Ransomware Detection System for Cloud (IRDS4C), with a blockchain-enabled Cyber Threat Intelligence platform (CTIB). The framework aims to improve the detection, reporting, and sharing of ransomware threats in cloud environments. IRDS4C uses deception techniques such as honeypots, honeytokens, pretender network paths, and decoy applications to identify ransomware behavior within cloud systems. Tests on 53 Windows-based ransomware samples from seven families showed an ordinary detection time of about 12 s, often quicker than tralatitious methods like file hashing or entropy analysis. These detection results are currently limited to Windows-based ransomware environments, and do not yet cover Linux, containerized, or hypervisor-level ransomware. Detected threats are formatted using STIX/TAXII standards and firmly shared through CTIB. CTIB applies a hybrid blockchain consensus of Proof of Stake (PoS) and Proof of Work (PoW) to ensure data integrity and protection from tampering. Security analysis shows that an attacker would need to control over 71% of the network to compromise the system. CTIB also improves trust, accuracy, and participation in intelligence sharing, while smart contracts control access to erogenous data. In a local prototype deployment (Hardhat devnet + FastAPI/Uvicorn), CTIB achieved 74.93–125.92 CTI submissions/min, The number of attempts or requests in each test was 100 with median end-to-end latency 455.55–724.99 ms (p95: 577.68–1364.17 ms) across PoW difficulty profiles (difficulty_bits = 8–16). Full article

Point-of-care ultrasound (POCUS) is an essential component of emergency medicine, enabling rapid bedside assessment across a wide spectrum of acute conditions. Its effectiveness, however, remains constrained by operator dependency, variable image quality, and time-critical decision-making. Recent advances in artificial intelligence (AI) offer opportunities to augment POCUS by supporting image acquisition, interpretation, and quantitative analysis. This narrative review synthesizes current evidence on AI-enhanced POCUS applications in emergency care, encompassing trauma, non-traumatic emergencies, integrated workflows, resource-limited settings, and education and training. Across trauma settings, AI-assisted POCUS has demonstrated promising performance for automated detection of pneumothorax, hemothorax, and free intraperitoneal fluid, supporting standardized eFAST examinations and rapid triage. In non-traumatic emergencies, AI-enabled cardiovascular, pulmonary, and abdominal applications provide automated measurements and pattern recognition that can approach expert-level performance when image quality is adequate. Integrated AI–POCUS systems and educational tools further highlight the potential to expand ultrasound access, support non-expert users, and standardize training. Nevertheless, important limitations persist, including limited generalizability, dataset bias, device heterogeneity, and uncertain impact on clinical decision-making and patient outcomes. In conclusion, AI-enhanced POCUS is transitioning from proof-of-concept toward early clinical integration in emergency medicine. While current evidence supports its role as a decision-support tool that may enhance consistency and efficiency, widespread adoption will require prospective multicentre validation, development of representative POCUS-specific datasets, vendor-agnostic solutions, and alignment with clinical, ethical, and regulatory frameworks. Full article

Underwater optical images often exhibit severe color distortion, weak texture, and uneven illumination due to light absorption and scattering in water. These issues result in unstable feature detection and inaccurate image registration. To address these challenges, this paper proposes an underwater image stitching method that integrates ORB (Oriented FAST and Rotated BRIEF) feature extraction with a fixed-ratio constraint matching strategy. First, lightweight color and contrast enhancement techniques are employed to restore color balance and improve local texture visibility. Then, ORB descriptors are extracted and matched via a KNN (K-Nearest Neighbors) nearest-neighbor search, and Lowe’s ratio test is applied to eliminate false matches caused by weak texture similarity. Finally, the geometric transformation between image frames is estimated by incorporating robust optimization, ensuring stable homography computation. Experimental results on real underwater datasets show that the proposed method significantly improves stitching continuity and structural consistency, achieving 40–120% improvements in SSIM (Structural Similarity Index) and PSNR (peak signal-to-noise ratio) over conventional Harris–ORB + KNN, SIFT (scale-invariant feature transform) + BF (brute force), SIFT + KNN, and AKAZE (accelerated KAZE) + BF methods while maintaining processing times within one second. These results indicate that the proposed method is well-suited for real-time underwater environment perception and panoramic mapping on low-cost, micro-sized underwater robotic platforms. Full article

Accurate measurement of wall shear stress (WSS) is essential for both fundamental and applied fluid dynamics, where it governs boundary-layer behavior, drag generation, and the performance of flow-control systems. Yet, existing WSS sensing methods remain limited by low spatial resolution, complex instrumentation, or the need for user-dependent calibration. This work introduces a method based on artificial intelligence (AI) and Oil-Film Interferometry, referred to as AI-OFI, that transforms a classical optical technique into an automated and sensor-like platform for local WSS detection. The method combines the non-intrusive precision of Oil-Film Interferometry with modern deep-learning tools to achieve fast and fully autonomous data interpretation. Interference patterns generated by a thinning oil film are first segmented in real time using a YOLO-based object detection network and subsequently analyzed through a modified VGG16 regression model to estimate the local film thickness and the corresponding WSS. A smart interrogation-window selection algorithm, based on 2D Fourier analysis, ensures robust fringe detection under varying illumination and oil distribution conditions. The AI-OFI system was validated in the high-Reynolds-number Long Pipe Facility at the Centre for International Cooperation in Long Pipe Experiments (CICLoPE), showing excellent agreement with reference pressure-drop measurements and conventional OFI, with an average deviation below 5%. The proposed framework enables reliable, real-time, and operator-independent wall shear stress sensing, representing a significant step toward next-generation optical sensors for aerodynamic and industrial flow applications. Full article

Hybrid supercapacitors possess high power and energy density, while the cascaded H-bridge converter features rapid response capability. Integrating these two components leads to an energy storage system capable of swiftly responding to power demands, effectively mitigating voltage and frequency instability in weak-grid renewable energy stations. Based on this system, in this paper, a novel automatic frequency–voltage regulation strategy is proposed. First, a fast fault severity detection method is proposed. It evaluates the system’s fault condition by monitoring the voltage response and generates auxiliary signals to enable subsequent rapid compensation of voltage and frequency. Subsequently, fast automatic voltage and frequency regulation strategies are developed. These strategies leverage real-time fault assessment to deliver immediate power support to weak-grid renewable stations following a disturbance, thereby effectively stabilizing the terminal voltage magnitude and system frequency. The effectiveness of the proposed method is validated through simulations. A grid-connected model of a weak-grid renewable energy station is established in MATLAB (2023b)/Simulink. Tests under various fault scenarios with different short-circuit ratios and voltage sag depths demonstrate that the proposed strategy can rapidly stabilize both voltage and frequency after large disturbances. Full article

Between 2015 and 2022, we evaluated a novel broad-range (BR) 16S PCR rDNA PCR/Sanger sequencing assay to improve diagnosis of invasive infections in culture-negative specimens. Using dual-priming oligonucleotides (DPO), this assay analyzed ribosomal DNA from sterile fluids or tissues. A total of 762 specimens were analyzed from 661 patients: 61% had negative cultures and BR 16S PCR tests; 35% had negative cultures but positive BR 16S PCR tests; and only 4% had negative cultures with indeterminate BR 16S PCR results. After resolution of indeterminate BR 16S PCR results (i.e., 29 negative, 1 false-positive, and 1 positive) the assay showed a sensitivity of 98.26% (95% CI = 96.00–99.43%), specificity of 99.79% (95% CI: 99.82–99.99%), positive predictive value of 99.65% (95% CI: 97.56–99.95%), negative predictive value of 98.94% (95% CI: 97.51–99.55%), and accuracy of 99.21% (95% CI: 98.28–99.71%) for a disease prevalence of 38.10% (95% CI: 34.62–41.66%). Gram stain purulence predicted the BR 16S PCR result better (69.4%) than organisms (24.6%), but the latter had a higher PPV (78.5%). Increased peripheral WBC (86.1%) or CRP (71.8%) predicted positive BR 16S PCR results. Our DPO BR 16S PCR assay improved pathogen detection over culture and minimized contamination. Broad range 16S rDNA PCR/sequencing (BR 16S PCR) is an important diagnostic technique in cases with invasive infection due to fastidious or uncultivatable pathogens. However, appropriate case selection, the quality of clinical specimen, and the specific assay primers affect its performance. Our novel BR 16S PCR assay uses unique dual-priming oligonucleotides (DPO) primers and fast protocols for rapid, optimal detection of bacterial pathogens, while minimizing contamination. Fast BR 16S PCR assay reports occurred within 24–48 h. BR 16S PCR and culture analyzed a diverse range of clinical specimens from patients with invasive infections. BR 16S PCR demonstrated a high performance for accurately detecting pathogens, ruling out infections, and minimizing contamination. BR 16S PCR detection of a pathogen allowed the appropriate clinical management of one-third of patients in this cohort. BR 16S PCR is an essential tool for the clinical management of patients with invasive infection when primary cultures are negative or contaminated. Full article

Isocitrate dehydrogenase (IDH) variants can lead to the development and/or progression of various solid tumors and hematological malignancies. IDH testing can guide diagnosis, prognosis, and therapeutic choice and typically relies on NGS, IHC, or PCR-based assays. Here, we evaluated the analytical performance of the Idylla IDH1-2 mutation assay for IDH variant detection using 70 fixed samples from patients with solid tumors and 36 DNA extracts from patients with acute myeloid leukemias previously characterized by NGS +/− IHC. Idylla IDH1-2 mutation assay gave 98.1% of valid results with an overall agreement, sensitivity, and specificity of 97.1%, 96.2%, and 98.1%, respectively, compared to NGS. Using commercial DNA standards, the limit of detection of the assay was 1.6% and 0.5% for IDH1 R132H and IDH2 R172K variants, respectively. Based on these data, the Idylla IDH1-2 mutation assay represents a fast and reliable alternative to detect IDH hotspot variants in solid tumors and hematological malignancies using either fixed tissue sections or DNA extracts. Particular attention, however, is needed for the interpretation of cases with cycle of quantification values of the internal controls over 35, for which a variant with low allelic frequency could be missed due to low DNA quantity or quality. Full article

Weed detection algorithms based on deep learning are considered crucial for smart agriculture, with the YOLO series algorithms being widely adopted due to their efficiency. However, existing YOLO algorithms struggle to maintain high accuracy, while low parameter requirements and computational efficiency are achieved when weeds with occlusion or overlap are detected. To address this challenge, a target detection algorithm called SSS-YOLO based on YOLOv9t is proposed in this paper. First, the SCB (Spatial Channel Conv Block) module is introduced, in which large kernel convolution is employed to capture long-range dependencies, occluded weed regions are bypassed by being associated with unobstructed areas, and features of unobstructed regions are enhanced through inter-channel relationships. Second, the SPPF EGAS (Spatial Pyramid Pooling Fast Edge Gaussian Aggregation Super) module is proposed, where multi-scale max pooling is utilized to extract hierarchical contextual features, large receptive fields are leveraged to acquire background information around occluded objects, and features of weed regions obscured by crops are inferred. Finally, the EMSN (Efficient Multi-Scale Spatial-Feedforward Network) module is developed, through which semantic information of occluded regions is reconstructed by contextual reasoning and background vegetation interference is effectively suppressed while visible regional details are preserved. To validate the performance of this method, experiments are conducted on both our self-built dataset and the publicly available Cotton WeedDet12 dataset. The results demonstrate that compared to existing algorithms, significant performance improvements are achieved by the proposed method. Full article