Search Results (12,421)

Background/Objectives: Sleeve gastrectomy (SG) is the most commonly performed bariatric surgery worldwide. It results in significant weight loss and improves metabolic disorders such as hypertension. Weight loss is thought to be the main factor contributing to blood pressure (BP) reduction after SG. Small-intestinal hormones may also mediate the antihypertensive effects of SG. We aimed to investigate the mechanisms underlying the antihypertensive effects of SG through small-intestinal hormones independently of weight loss. Methods: This study involved male Sprague–Dawley rats that underwent a sham operation or SG, followed by a dietary intervention involving a standard diet, a high-fat and high-salt diet, or pair-feeding with SG. Results: Three weeks postoperatively, SG significantly reduced systolic blood pressure (SBP) and increased urinary sodium excretion. RNA sequencing of the small intestine revealed upregulation of the gene encoding prouroguanylin (proUGN). proUGN is a small-intestinal hormone that inhibits renal sodium reabsorption by converting sodium/hydrogen ion exchanger type 3 (NHE3) in the proximal tubules into the inactive phosphorylated form at Ser552 (pS552-NHE3). Furthermore, SG significantly increased proUGN levels in the ileum and plasma, as well as the levels of pS552-NHE3 in the renal cortex. The administration of exogenous uroguanylin, which is converted from proUGN, resulted in increased renal pS552-NHE3, increased urinary sodium excretion, and decreased SBP without body weight reduction. These effects were similar to those observed with SG. Conclusions: SG increases proUGN secretion from the small intestine, leading to increased blood concentration. This inhibits NHE3 activity in the proximal tubules, promotes natriuresis and reduces BP. Full article

Low-light image enhancement (LLIE) remains a challenging task due to the scarcity of paired training data and the complex signal-dependent noise inherent in low-light scenes. To address these issues, this paper proposes a fully unsupervised framework named Wavelet-Guided Zero-Reference Diffusion (WZD) for natural low-light image restoration. WZD leverages an ImageNet-pre-trained diffusion prior and a multi-scale representation of the Discrete Wavelet Transform (DWT) to restore natural illumination from a single dark image. Specifically, the input low-light image is first processed by a Practical Exposure Corrector (PEC) to provide an initial robust luminance baseline. It is then converted from the RGB to the YCbCr color space. The Y channels of the input image and the current diffusion estimate are decomposed into four orthogonal sub-bands—LL, LH, HL, and HH—and fused via learnable, step-wise weights while preserving structural integrity. An exposure control loss and a detail consistency loss are jointly employed to suppress over/under-exposure and preserve high-frequency details. Unlike recent approaches that rely on complex supervised training or lack physical guidance, our method integrates wavelet guidance with a zero-reference learning framework, incorporates the PEC module as a physical prior, and achieves significant improvements in detail preservation and noise suppression without requiring paired training data. Comprehensive experiments on the LOL-v1, LOL-v2, and LSRW datasets demonstrate that WZD achieves a superior or competitive performance, surpassing all referenced unsupervised methods. Ablation studies confirm the critical roles of the PEC prior, YCbCr conversion, wavelet-guided fusion, and the joint loss function. WZD also enhances the performance of downstream tasks, verifying its practical value. Full article

Fe-Mn-Al-C lightweight steel is an alternative to traditional low-alloy structural steels. It is lightweight and can be used to reduce the weight of structures without increasing their density. However, in the marine environment, traditional low-alloy structural steels can be damaged by chloride ions, which shortens their service life. We do not yet understand how aluminum, an important alloying element in lightweight steel, affects the process of corrosion. In this study, we examined Fe-Mn-Al-C lightweight steels with different amounts of aluminum. We used full-immersion simulated marine corrosion tests and multi-dimensional characterization techniques, such as microstructure observation and electrochemical measurements, to explore the relationship between aluminum content and the steel’s corrosion rate, corrosion product structure, and corrosion resistance. The results showed that, compared with CS, the weight loss and rate of corrosion of steels that contain aluminum were a lot lower. While the corrosion rate of CS is approximately 0.068 g·h⁻¹·m⁻², that of 7Al steel is reduced to 0.050 g·h⁻¹·m⁻². The stable phases α-FeOOH and FeAl₂O₄ are formed in the corrosion products when Al is added. As the Al content increases, so does the relative content of these phases. Furthermore, FeAl₂O₄ acts as a nucleation site that refines corrosion product grains, reduces pores and cracks, and significantly improves the compactness of corrosion products. It also forms a dense inner rust layer that blocks the penetration of corrosive ions such as Cl⁻. This study confirmed that aluminum improves the corrosion resistance of steel synergistically by regulating the structure of the corrosion products, optimizing the phase composition, and improving the electrochemical properties. The optimal aluminum content for lightweight steel in marine environments is 7%, within a range of 5–9%. Full article

Baicalein, a natural flavonoid derived from traditional medicinal herbs, has demonstrated anticancer activity in various malignancies, but its role in endometrial cancer remains largely unexplored. In this study, we investigated the therapeutic potential of baicalein, alone and in combination with metformin, in human endometrial cancer cells. Given that the mTOR signaling pathway is frequently dysregulated in endometrial cancer due to PTEN loss, we examined how baicalein affects this pathway. Our results demonstrated that baicalein significantly inhibited cell proliferation in a dose-dependent manner, which was associated with increased DDIT4 expression, activation of AMPK, and decreased phosphorylation of mTOR downstream targets S6K1 and S6. In vivo, baicalein treatment led to a reduction in tumor volume in HEC-1A xenograft female nude mice without affecting body weight. While metformin also reduced cell viability, baicalein achieved comparable effects at lower concentrations. The combination of baicalein and metformin produced a synergistic anti-tumor effect and more effectively inhibited the AMPK/PI3K/mTOR signaling pathway than either agent alone. These findings suggest that baicalein may represent a promising, non-toxic therapeutic option for endometrial cancer, particularly when used in combination with metformin. Further investigation is warranted to assess the clinical relevance of this strategy. Full article

Semaglutide, a glucagon-like peptide-1 receptor agonist (GLP-1RA), has demonstrated substantial efficacy in managing type 2 diabetes mellitus (T2DM). It provides glycemic control, promotes weight loss, and offers cardiovascular protection. Evidence also supports its role in diabetic kidney disease (DKD), a leading global cause of end-stage renal disease. DKD arises from a multifactorial interaction involving hyperglycemia, hypertension, and inflammation, which leads to cumulative nephron loss. Beyond glycemic control, semaglutide’s mechanisms of action target metabolic and hemodynamic pathways that contribute to renal damage. This review evaluates the preclinical and clinical evidence of semaglutide’s role in preventing DKD, focusing on its renal effects and the mechanistic basis for renoprotection. We also position semaglutide within the broader DKD therapeutic landscape by reviewing clinical trial findings, translational studies, real-world evidence, and its effectiveness compared to other drug classes. The expanded actions of semaglutide make it a promising agent in patients with T2DM and DKD and encourage further mechanistic research and long-term evaluation. Full article

Student classroom behavior recognition is a core research direction in intelligent education systems. Real-time analysis of students’ learning states and behavioral features through classroom monitoring provides quantitative support for teaching evaluation, classroom management, and personalized instruction, offering significant value for data-driven educational decision-making. To address the issues of low detection accuracy and severe occlusion in classroom behavior detection, this article proposes an improved YOLOv11n-based algorithm named YOLOv11-GLIDE. The model introduces a Channel Prior Convolutional Attention (CPCA) mechanism to integrate global and local feature information, enhancing feature extraction and detection performance. A scale-based dynamic loss (SD Loss) is designed to adaptively adjust the loss weights according to object scale, improving regression stability and detection accuracy. In addition, Sparse Depthwise Convolution (SPD-Conv) replaces traditional down-sampling to reduce fine-grained feature loss and computational cost. Experimental results on the SCB-Dataset3 demonstrate that YOLOv11-GLIDE achieves an excellent balance between accuracy and lightweight design. Compared with the baseline YOLOv11n, mAP@0.5 and mAP@0.5-0.95 increase by 2.5% and 7.6%, while Parameters and GFLOPS are reduced by 9.4% and 11.1%, respectively. The detection speed reaches 127.9 FPS, meeting the practical requirements of embedded classroom monitoring systems for accurate and efficient student behavior recognition. Full article

To enhance the anti-corrosion performance of storage tanks, Ni–SiC composites were successfully fabricated on the surface of Q345 steel substrate via the ultrasonic electrodeposition technique. The influence of ultrasonic power on the surface morphology, element content, phase structure, and anti-corrosion performance of Ni–SiC composites were explored utilizing a scanning electron microscope (SEM), X-ray diffraction (XRD), energy dispersive spectroscopy (EDS), and an electrochemical workstation, respectively. SEM images showed that the Ni–SiC composites obtained at 120 W had a flat, dense surface morphology, with a uniform distribution of SiC nanoparticles (NPs) and a refined size of nickel grains. Meanwhile, the Si content (7.3 wt.%) of Ni–SiC composites prepared at 120 W was obviously higher than those obtained at 0 W (4.8 wt.%) and 60 W (6.1 wt.%). The thicknesses and adhesion force of Ni–SiC composites manufactured at 120 W were the largest of 103.5 μm and 51.2 N, respectively. XRD patterns presented that the diffraction peaks intensity and width of Ni–SiC composites manufactured at 120 W were lower and broader than that of Ni–SiC composites manufactured at 0 W and 60 W. A corrosion test illustrated that the Ni–SiC composites prepared at 120 W had the lowest corrosion current of 3.5 × 10⁻³ mA/cm², the lowest corrosive weight loss (4.2 mg) and corrosion rate (0.06 mg/h), while the corrosion potential was the highest of −0.41 V, which demonstrated the best anti-corrosion performance. In addition, the co-deposition mechanism of SiC NPs and Ni²⁺ ions was also analyzed. Full article

This study aimed to describe a new broiler meat quality class—red, soft, and exudative (RSE) meat—and to propose novel classification criteria. Two-step cluster analysis assigned 132 broilers into five meat quality classes using ultimate pH, drip loss, and L* values: pale, soft, and exudative (PSE); pale, firm, and nonexudative (PFN); RSE; red, firm, and nonexudative (RFN); and dark, firm, and dry (DFD) meat. PSE meat showed the lowest plasma superoxide dismutase activity, highest malondialdehyde activity, greater live and carcass weights, higher breast and leg yields, the lowest initial and ultimate pH, highest initial temperature, the lightest colour (the highest L* and b* values, and the lowest a* value), and the greatest drip, thawing, and cooking losses. RFN meat had the highest superoxide dismutase activity, lowest malondialdehyde activity, and remained within the optimal range for ultimate pH, drip loss, and L* value, generally occupying a midpoint between PSE and DFD meat. RSE meat shared the poor water-holding capacity of PSE but differed by showing a colour similar to RFN and an optimal ultimate pH. PFN meat had firmness comparable to RFN, with appropriate water-holding capacity and optimal ultimate pH, but an undesirably pale colour resembling PSE. DFD meat displayed the highest initial and ultimate pH, lowest drip, thawing, and cooking losses, darkest colour (the lowest L* value), and lowest protein content. This study provides the first evidence of RSE meat in broilers and proposes a classification system based on ultimate pH, drip loss, and L* values to distinguish five quality classes. Further studies are required to validate these findings and develop preventive strategies. Full article

Automatic speech recognition (ASR) technology faces the dual challenges of model complexity and noise robustness when deployed on terminal devices (e.g., mobile devices, embedded systems). To meet the demand for lightweight and high-performance models in terminal devices, we propose a lightweight end-to-end speech recognition model, OS-Denseformer (Omni-Scale-Denseformer). The core of this model lies in its lightweight design and noise adaptability: multi-scale acoustic features are efficiently extracted through a multi-sampling structure to enhance noise robustness; the proposed OS-Conv module improves local feature extraction capability while significantly reducing the number of parameters, enhancing computational efficiency, and lowering model complexity; the proposed normalization function, ExpNorm, normalizes the model output, facilitating more accurate parameter optimization during model training. Finally, we employ distinct loss functions across different training stages, using Minimum Bayes Risk (MBR) joint optimization to determine the optimal weighting scheme that directly minimizes the character error rate (CER). Experimental results on public datasets such as AISHELL-1 demonstrate that, under a high-noise environment of −15 dB, the CER of the OS-Denseformer model is reduced by 9.95%, 7.97%, and 4.85% compared to the benchmark models Squeezeformer, Conformer, and Zipformer, respectively. Additionally, the model parameter count is reduced by 53.35%, 10.27%, and 27.66%, while the giga floating-point operations per second (GFLOPs) are decreased by 67.51%, 66.51%, and 13.82%, respectively. Deployment on resource-constrained mobile devices demonstrates that, compared to Conformer, OS-Denseformer reduced memory usage by 10.79% and decreased inference latency by 61.62%. Full article

This study aimed to evaluate the pathogenic potential of the 2024 Vietnam PRRSV genotype 2 (NA01/2024, isolate). Fifteen 6-week-old piglets were intramuscularly inoculated with 1 × 10^5.5 TCID₅₀/mL of PRRSV, while fifteen control piglets remained uninfected. Blood and nasal swabs were collected every 3 days until 21 days post-infection (dpi). Necropsies were performed on piglets at 6, 15, and 21 dpi. Infected piglets exhibited fever, blue ear, weight loss, respiratory distress, diarrhea, and leucopenia between 3 and 12 dpi. PRRSV was detected in serum and nasal secretions up to 21 dpi, peaking between 6 and 9 dpi. Seroconversion began at 6 dpi, with the highest antibody titers at 21 dpi. Virus load was highest in lung tissues at all intervals, while the spleen and lymph nodes showed higher viral load at later stages. Routine blood tests indicated a slight decrease in red blood cells, hemoglobin, and reticulocytes, along with a notable increase in monocytes in infected piglets. Cytokine levels (IFN-γ, IL-6, and IL-10) in serum and lung tissues were significantly higher in infected piglets compared to controls. Additionally, the PRRSV infection triggered innate immune responses, including cytokines, growth factors, and chemokines in whole blood and tissues such as IFN-α, IFN-β, TNF-α, IL-1β, etc. These findings highlight the pathogenicity of the Vietnam PRRSV NA01/2024 isolate and its impact on the immune response, providing insights into PRRSV infection mechanisms and strategies for prevention and control. Full article

Postharvest losses in onion (Allium cepa L.) bulbs constitute a major economic challenge globally, primarily driven by fungal pathogens and premature sprouting during long-term storage. Addressing these issues with effective preharvest strategies is critical for market stability and supply chain integrity. This study evaluated the effects of two preharvest fungicide strategies, i.e., T1 (dimethomorph + pyraclostrobin) and T2 (metalaxyl + mancozeb + copper oxychloride), on the crop yield, postharvest quality, and sprouting behavior of dried onion bulbs. Both treatments significantly reduced the incidence of foliar disease in the field and improved the crop yield of commercial bulbs compared to the control in two consecutive seasons. T1 achieved the highest yield (~76 and 88 t ha⁻¹ in ‘Mata Hari’ and ’Recas’ onions). During storage at 20 °C for 84 days, in the ‘Mata Hari’ cultivar, the T1 bulbs exhibited the lowest weight loss and respiration rate, the lowest sprouting incidence (1%), and superior firmness retention and higher total soluble solids. In contrast, control bulbs exhibited accelerated weight loss and tissue degradation, with up to 95% sprouting. Pyruvic acid content, an indicator of pungency, was highest in T1 bulbs and increased significantly in sprouted controls, likely due to internal enzymatic activation and tissue senescence. The fungicides indirectly delayed dormancy release by delaying sprouting and internal stem axis formation. Overall, T1 was the most effective strategy for preserving onion quality during storage without using synthetic sprout inhibitors. These findings support the integration of specific fungicide programs into preharvest management to improve onion storability, reduce postharvest losses, and maintain commercial value in intermediate-dormancy dried onion cultivars, such as ‘Mata Hari’. Full article

Carbon fibers derived from carbonized and activated polyacrylonitrile (CFPAN) were sequentially brominated and subsequently functionalized with selected primary and secondary amines to engineer a directional electromagnetic (EM) response. Besides bromine incorporation, bromination introduced oxygen-containing surface groups (e.g., carboxyl, lactone), enabling nucleophilic substitution by amines. Surface characterization (SEM-EDS, FTIR ATR) confirmed successful amine grafting, while thermal analysis (TGA, TPD MS) revealed increased weight loss in the 150–450 °C range due to the decomposition of covalently bonded nitrogen- and oxygen-containing moieties, evidencing strong surface functionalization. Microwave characterization in the X-band (8.2–12.4 GHz) demonstrated that functionalization strongly influences the EM response of CFPAN fibers. The measured reflection coefficient varied from −1.0 to −2.5 dB for sulfonylethylenediamine (SuEn)-functionalized fibers and from −2.0 to −4.0 dB for ethylenediamine (En)-treated ones, depending on frequency and fiber orientation. The frequency-averaged absorption coefficient of pure CFPAN amounted to 32–41%, with absorption maxima and minima corresponding to orientations differing by 90°. SuEn modification decreased absorption to 21–35%, while En functionalization enhanced it to 32–51%. Pure CFPAN exhibited the lowest absorption anisotropy (factor 1.28), whereas piperazine- and En-modified samples showed the highest anisotropy (1.57 and 1.59, respectively). Across all compositions, the attenuation constant remained within 1.5–4.5 mm⁻¹. The observed anisotropic behavior is governed primarily by orientation-dependent variations in characteristic impedance and, to a lesser extent, by anisotropic attenuation constants. Such tunable anisotropy is particularly advantageous for EM shielding textiles, where fiber alignment can be tailored to enhance interaction with polarized fields. Among the tested amines, En-functionalized CFPAN exhibited the highest nitrogen content (up to 10.1 at%) and the most significant enhancement in microwave absorption, positioning it as a promising candidate for advanced orientation-sensitive shielding applications. Full article

Guinea pig (Cavia porcellus) meat is valued for its nutritional quality and ease of production. Marination is a key value-adding process, but it is limited by its long duration. Therefore, technologies that accelerate marination and improve tenderness are needed. The objective of this study was to evaluate the effect of ultrasound application time and temperature on the physicochemical parameters and yield of guinea pig meat during marination. The marination solution contained 1.9% NaCl, 1.9% acetic acid, and 0.51% oregano essential oil. Ultrasound treatment (200 W) was applied for 15–120 min, while static treatments were conducted at 20, 30, and 40 °C. Ultrasound-assisted marination of guinea pig meat improved NaCl uptake, reduced acetic acid content, and improved water-holding capacity. Moderate temperatures (40 °C) minimized weight loss, and short ultrasound times preserved color and texture. However, prolonged ultrasound exposure led to myofibrillar disruption and increased weight loss. Correlation analysis revealed that pH, influenced by NaCl and acetic acid, had a significant impact on moisture, texture, and water-holding capacity. Overall, the controlled application of ultrasound and temperature effectively optimized marination efficiency, enhanced functional properties, and preserved the quality of guinea pig meat. Full article

Medicine and vaccine supply chains in Nigeria are socio-technical systems exposed to persistent uncertainty and disruption. Existing studies rarely integrate systems thinking with uncertainty-aware decision tools to jointly prioritize challenges and policy responses. This study asks which policy mix most effectively strengthens these supply chains while balancing multiple, conflicting criteria and stakeholder judgments. We develop a two-stage Fermatean fuzzy framework that first weights 35 challenges using Fermatean Fuzzy Stepwise Weight Assessment Ratio Analysis (FF-SWARA) and then ranks four policy alternatives via Fermatean Fuzzy VIšeKriterijumska Optimizacija I Kompromisno Resenje (FF-VIKOR), based on expert elicitation and linguistic assessments. Results identify interruption of drug supplies, limited vaccine funding, cold-chain potency loss, human resource shortages, and product damage as the most critical challenges. FF-VIKOR prioritizes Effective Implementation of Existing Policies as the best alternative, followed by Improving Access to Medicines and Vaccines, indicating that governance quality and access-enabling infrastructure are complementary levers for resilience. To further enhance robustness, we embed the VIKOR outcomes into a policy-oriented game-theoretic analysis, where strategic weighting scenarios (e.g., cost-focused, infrastructure-driven, human-capital focused) interact with policy choices. The equilibrium results reveal that a mixed strategy combining Effective Implementation of Existing Policies and Strengthening Distribution and Storage Systems guarantees the best compromise performance across adversarial scenarios. The proposed framework operationalizes systems thinking for uncertainty-aware and strategically robust policy design and can be extended with real-time data integration, scenario planning, and regional replication to guide adaptive supply chain governance. Full article

The accurate detection of high-impedance faults (HIFs) in distribution systems is fundamentally dependent on the extraction of weak fault signatures. However, these features are often obscured by complex and high-level noise present in current transformer (CT) measurement data. To address this challenge, an energy-proportion-guided channel-wise attention stacked denoising autoencoder (EPGCA-SDAE) model is proposed. In this model, wavelet decomposition is employed to transform the signal into informative frequency band components. A channel attention mechanism is utilized to adaptively assign weights to each component, thereby enhancing model interpretability. Furthermore, a physics-informed prior, based on energy distribution, is introduced to guide the loss function and regulate the attention learning process. Extensive simulations using both synthetic and real-world $10\mathrm{kV}$ distribution network data are conducted. The superiority of the EPGCA-SDAE over traditional wavelet-based methods, stacked denoising autoencoders (SDAE), denoising convolutional neural network (DnCNN), and Transformer-based networks across various noise conditions is demonstrated. The lowest average mean squared error (MSE) is achieved by the proposed model (simulated: $50.60\times {10}^{-5}\mathrm{p}.\mathrm{u}.$; real: $76.45\times {10}^{-5}\mathrm{p}.\mathrm{u}.$), along with enhanced noise robustness, generalization capability, and physical interpretability. These results verify the method’s feasibility within the tested 10 kV distribution system, providing a reliable data recovery framework for fault diagnosis in noise-contaminated distribution network environments. Full article