Search Results (13,604)

Background/Aim: Long non-coding RNAs (lncRNAs) such as NEAT1, HULC, and MALAT1, which are expressed in adipose tissue, are known to play a role in regulating adiposity. However, how the plasma expression of these lncRNAs changes in obese patients following rapid adipose tissue loss after sleeve gastrectomy remains unclear. This study aimed to investigate the relationship between plasma NEAT1, HULC, and MALAT1 expression levels and short-term weight loss after sleeve gastrectomy. Materials and Methods: Plasma samples prospectively collected from patient groups were used for total RNA extraction to measure the expression levels of NEAT1, HULC, and MALAT1 both before sleeve gastrectomy and 30 days after the procedure. Additionally, patients were followed for changes in body mass index (BMI) and HbA1C levels over a 12-month period. Associations between lncRNA expression levels and clinical parameters were evaluated. Results: Before sleeve gastrectomy, the expression levels of NEAT1 and HULC were significantly higher in obese patients compared to non-obese individuals (p < 0.0001). Sleeve gastrectomy was associated with decreased expression levels of NEAT1 (p = 0.004) and HULC (p = 0.0027). NEAT1 and HULC expression levels showed significant associations with changes in HbA1C and BMI, respectively (p < 0.05). Conclusions: NEAT1 and HULC expression levels were associated with short-term metabolic and anthropometric changes following sleeve gastrectomy. These findings are exploratory and hypothesis-generating, and further studies with larger cohorts and longer follow-up are needed to determine their potential clinical relevance. Full article

For power-grid applications such as transmission corridor inspection, substation asset inspection, and post-disaster emergency repair, reliable UAV self-localization under GNSS-degraded or GNSS-denied conditions is critical to ensuring operational safety and accurate defect geotagging. Due to substantial discrepancies in viewpoint, scale, and geometric structure between oblique UAV images and nadir satellite images, conventional RGB-based cross-view retrieval methods often suffer from unstable alignment and insufficient geometric modeling, particularly in scenarios with repetitive textures and partial overlap. To address these challenges, we propose a cross-view visual geo-localization model that integrates RGBD multimodal inputs with multi-scale attention enhancement. Specifically, MiDaS is used to estimate relative depth from UAV imagery, which is concatenated with RGB to form a four-channel input, while satellite images are padded with an additional zero channel to maintain dimensional consistency. A shared-weight ViTAdapter is adopted to learn joint semantic–geometric representations, and a lightweight Efficient Multi-scale Attention (EMA) module is adopted on spatial feature maps to strengthen multi-scale spatial consistency. In addition, an IoU-weighted InfoNCE loss is employed to accommodate partial matching during training, thereby improving the robustness of feature alignment. Experiments on the GTA-UAV dataset under the cross-area protocol show stable performance across both retrieval and localization metrics. Specifically, Recall@1, Recall@5, and Recall@10 reach 18.12%, 38.83%, and 49.47%, respectively; AP is 28.01 and SDM@3 is 0.53; meanwhile, the top-1 geodesic distance error Dis@1 is 1052.73 m. These results indicate that explicit geometric priors combined with multi-scale spatial enhancement can effectively improve cross-view feature alignment, leading to enhanced robustness and accuracy for localization in challenging power inspection scenarios. Full article

Acinetobacter baumannii, a prominent opportunistic pathogen in healthcare settings, causes severe infections and poses significant challenges for clinical treatment. This study investigates the synergistic effects of α-pinene combined with meropenem (MEM) on A. baumannii biofilm formation and lung injury in mice, aiming to develop new strategies to combat persistent infections and antibiotic resistance. α-pinene combined with MEM exhibited strong synergistic antibacterial activity against carbapenem-resistant A. baumannii (CRAB 5E9). The combination significantly inhibited biofilm formation, extracellular polymer production, surface motility, and quorum sensing. The expression of key genes such as ompA, bfmR, bap, csuAB, abaI, and abaR was reduced by up to 61%. In vivo, the treatment alleviated weight loss, decreased the bacterial load in lung tissue, and reduced lung inflammation. Furthermore, it significantly suppressed proteins involved in the inflammatory response and the MAPK pathway, including TLR4, NF-κB, NLRP3, TRAF6, ERK2, p38 MAPK, JNK, and TNF-α. The combination of α-pinene and MEM synergistically inhibits A. baumannii biofilm formation and alleviates the inflammatory response in a mouse model, offering a potential therapeutic approach for combating A. baumannii infections. Full article

Functionally graded multi-material thermoplastic architectures provide a promising route for tailoring viscoelastic energy dissipation through controlled phase contrast and interfacial interactions. However, rational selection of optimal material compositions remains challenging due to competing requirements among stiffness, damping efficiency, thermal stability, and processability. The absence of a quantitative decision framework often limits systematic design of architected polymer systems. This study proposes an Analytic Hierarchy Process (AHP)-based multi-criteria decision model to identify the optimal rigid–elastic thermoplastic composition for enhanced damping performance. Nine performance criteria were considered, including storage modulus, loss factor, damping bandwidth, interfacial adhesion strength, elongation at break, impact resistance, glass transition temperature, thermal stability, and printability. Fourteen alternative material configurations combining different rigid phases, elastomeric interlayers, filler contents, and layer thickness ratios were evaluated. Pairwise comparison matrices were constructed based on experimentally measured thermomechanical data and literature-reported values, and consistency ratios were maintained below 0.1 to ensure decision reliability. Numerical results indicate that a graded PLA/soft-TPU/PLA architecture with optimized layer thickness ratio achieved the highest global priority weight (0.431), outperforming the baseline PLA/TPU system by approximately ~25–30% in overall performance index. Sensitivity analysis confirmed ranking robustness across variations in damping and stiffness weighting factors. The proposed framework establishes a systematic methodology for polymer material selection and multi-material architectural optimization, enabling data-driven design of thermoplastic systems with tunable viscoelastic performance. Full article

Juglone (5-hydroxy-1,4-naphthalenedione), a natural compound derived from walnut husks, was investigated as a sustainable dual-function antibacterial agent and corrosion inhibitor for Q235 steel in sulfate-reducing bacteria (SRB)-containing seawater. Juglone was synthesized via an improved one-pot method, and its performance was evaluated through antibacterial assays, weight loss measurements, surface characterization (SEM, XPS, XRD), and electrochemical techniques (EIS, PDP). Juglone exhibited potent antibacterial activity against Desulfovibrio sp., with a minimum inhibitory concentration (MIC) of 40 mg/L. At 20 mg/L (0.5 MIC) and 40 mg/L (1 MIC), it effectively suppressed bacterial growth and metabolism, mitigating corrosion. At 80 mg/L (2 MIC), a dual-action mechanism was observed: strong antibacterial effect combined with chemical reaction with H₂S, a corrosive SRB metabolite, forming a protective thiol-containing film on the steel surface. This reduced the corrosion current density from 3.16 × 10⁻⁵ A/cm² to 7.94 × 10⁻⁷ A/cm², achieving an inhibition efficiency of 97.5%. Juglone represents a promising green alternative to conventional toxic antibacterial agents, aligning with circular economy principles. Full article

This study evaluates the landslide susceptibility of Zonguldak Province, Türkiye, by integrating the Analytical Hierarchy Process (AHP), artificial intelligence (AI) algorithms, and SBAS-InSAR deformation data. Eight environmental and geological parameters—elevation, slope, aspect, lithology, hydrogeology, land use, and distances to rivers and roads—were weighted using AHP and analyzed through 25 AI models. Among them, the Ensemble Bagged Trees (EBT) algorithm achieved the highest predictive accuracy (84%), demonstrating strong adaptability to complex geological datasets. The resulting susceptibility maps were validated using both traditional landslide inventories and InSAR-derived deformation maps, achieving an overall agreement of 83.05%. This dual-validation approach allows for the identification of unrecorded or active slope movements not captured in existing inventories. The combined use of AHP and AI significantly improves model reliability by incorporating both expert judgment and data-driven learning. The study introduces a novel hybrid framework for landslide susceptibility mapping and provides a valuable reference for disaster risk management and spatial planning in regions with complex topography. This study also contributes to sustainability by supporting risk-informed land-use planning, reducing potential economic losses, and enhancing environmental resilience in landslide-prone regions. The proposed framework aligns with sustainable development goals by integrating geospatial technologies and data-driven approaches for long-term hazard mitigation. Full article

The Low-Strain Pile Integrity Test (LSPIT), standardized in ASTM D5882, is widely used as a rapid and economical non-destructive technique for assessing pile continuity in deep foundation systems. However, interpretation of LSPIT reflectograms remains strongly dependent on expert judgment and is influenced by soil–pile interaction effects such as damping and radiation losses, which can alter waveform morphology and confound automated defect screening. This study proposes a soil-aware deep learning framework that combines image-based reflectogram features with categorical geotechnical context describing the dominant soil regime at the measurement site. Reflectogram images are processed with a pretrained ConvNeXt-Large backbone, while soil information derived from Unified Soil Classification System (USCS) logs is represented as a categorical auxiliary input and mapped to a learnable embedding. The resulting multimodal design conditions waveform interpretation based on site context rather than relying on signal morphology alone. The framework is examined on an assembled benchmark of 510 expert-labeled reflectograms (404 intact and 106 defective), including a nine-site subset of 182 field records with explicit soil annotations. On the assembled benchmark, the model yields 99.41% accuracy and a weighted F1-score of 0.9941; on the nine-site subset, the observed accuracy is 99.45% with zero missed defective cases. Balanced accuracy, specificity, missed-detection rate, false-alarm rate, and confidence intervals are additionally reported to better align the evaluation with engineering screening practice. The study also states the current limits of the evidence base, including partial soil annotation, dominant-soil simplification, restricted soil coverage, and the absence of leave-site-out and interpretability-focused validation. Overall, the results support soil-aware multimodal learning as a promising proof-of-concept direction for more context-aware automated LSPIT interpretation, while also identifying the validation steps still required for broad field deployment. Full article

WiFi fingerprinting remains attractive for indoor localization because it reuses existing wireless infrastructure, yet RSSI fingerprints are high-dimensional, sparse, and often ambiguous across adjacent floors and building regions. This study develops a hybrid blended localization framework that combines multi-task learning with feature-space weighted k-nearest-neighbor refinement. A shared neural encoder predicts building labels, floor labels, and normalized coordinates from 520-dimensional WiFi fingerprints, and the learned embedding space is then used for semantically constrained WKNN correction. The final model is trained with AdamW, a learning rate of $8\times {10}^{-4}$, batch size 512, and a joint loss over building classification, floor classification, and coordinate regression, without a learning-rate scheduler. Experiments on a public WiFi fingerprint dataset show that the hybrid model achieves the strongest overall localization robustness among the evaluated non-ensemble methods. On the official validation split, it obtains a mean localization error of 9.01, a median error of 6.25, and an RMSE of 12.95 in the dataset coordinate units. On the internal semantic validation split, it reaches 94.81% floor classification accuracy and 97.62% building classification accuracy. Floor-wise and building–floor analyses further show that the largest errors are concentrated in a small number of difficult semantic regions, especially the highest floor and sparsely constrained partitions. Full article

This study introduces a new two-parameter exponential (TPEX) model for modeling skewed phenomena and risk analysis, motivated by the need for flexible yet tractable models capturing asymmetric behavior in actuarial, financial, and reliability data. An extensive simulation study evaluated seven estimation procedures: maximum likelihood estimation (MLE), ordinary least squares (OrLS), weighted least squares (WLSQ), Cramér–von Mises (CVM), Anderson–Darling estimation (ADE), Kolmogorov estimation (KE), L-moments, and Bayesian estimation, comparing bias, efficiency, and stability across sample sizes and parameter settings. Four real-data applications were conducted: two comparing estimation methods on relief and survival datasets and two assessing competitive performance against exponential-type models. Key risk indicators (KRIs), including the Value at Risk (VaR), Tail Value at Risk (TVaR), Tail Variance (TV), Tail Mean–Variance (TMV), and expected loss (EL), were computed using UK motor non-comprehensive claims and US house price data, illustrating the model’s relevance for insurance reserving and market risk assessment. Full article

Frequency response analysis (FRA) is recognized as an effective method in power transformer winding fault diagnosis. However, the traditional numerical index methods focus on the overall features of FRA curves, making it difficult to capture subtle deformations in transformer windings. Similarly, existing digital image processing methods rely on a single feature or a simple feature combination without adaptive fusion. These methods ignore differences in the data distributions of features, leading to feature mismatch, the loss of sensitive fault information, and lower diagnostic accuracy. To solve this problem, a novel adaptive multiple-image-feature fusion method for transformer winding fault diagnosis is proposed. First, a multi-dimensional feature space combining image pixel matrix similarity, morphological features, and image texture features is built to decode the difference in fault of FRA images. Second, the multiple kernel learning (MKL) framework is used to dynamically adjust the fusion weights of different kernels to make features compatible and remove redundant information. Finally, comparative and ablation experiments show that the proposed method outperforms the traditional methods in identifying different types and levels of faults. The method achieves over 99% accuracy in fault type identification across SVM, KNN, and RF classifiers. For radial deformation (RD) severity prediction, the accuracy of the proposed model is 93.37% with SVM and 94.85% with KNN, outperforming the full-feature concatenation method. These results confirm the method’s robustness and diagnostic precision. Full article

The global rise in obesity and cardiometabolic disease represents a major public health concern and contributes significantly to cardiovascular morbidity and mortality. Contemporary Western dietary patterns and excess adiposity are strongly associated with atherosclerotic cardiovascular disease. Although pharmacologic therapies have expanded, lifestyle interventions remain the cornerstone of prevention and management. However, identifying sustainable and effective dietary approaches continues to be challenging given the wide range of available nutrition regimens. Intermittent fasting (IF) has emerged as a promising strategy for weight reduction and metabolic improvement. In this article, we review the physiological effects of IF, including metabolic switching, ketosis, and improvements in insulin sensitivity and inflammatory regulation. We also evaluate clinical evidence regarding the impact on cardiovascular risk, as well as its safety and tolerability. We examine the hormonal responses to IF based on sex. While early studies raised concerns regarding potential reproductive and endocrine disturbances, recent data suggest beneficial effects in both males and females. IF may modestly reduce testosterone in men without impairing muscle mass or strength and may improve metabolic and reproductive outcomes in women, particularly those with hyperandrogenic conditions such as polycystic ovarian syndrome, with favorable effects also observed in postmenopausal women, especially when combined with physical activity. Full article

Cross-border regions often exhibit complex vegetation-related land-cover patterns due to contrasting natural conditions and divergent development trajectories, causing multi-source land-cover products to suffer from disagreements in class assignment and boundary delineation, especially for cropland, forestland, and grassland. Because border zones are rarely mapping priorities, classification instability near national boundaries undermines transboundary comparisons. To address this, we propose a Conflict-aware Adaptive Distillation Fusion Network (CADF-Net) that fuses multi-source land-cover products to improve the discrimination and spatial consistency of key vegetation classes in cross-border regions. Taking the transnational China–Russia border (Sanjiang Plain and Primorskiy Kray) as a representative case, we integrate geo-environmental factors and introduce a pixel-level Conflict Index (CI) to explicitly steer the model toward discrepancy-prone areas. Building on this, we develop an Adaptive Distillation U-Net (AD-UNet) with uncertainty-adaptive distillation and employ a confidence-guided, dynamically weighted ensemble to generate the final fused land-cover product (CADF-LC). Quantitative assessments demonstrate that CADF-LC achieved an OA of 0.8600, a Kappa of 0.8133, and an mIoU of 0.7589, outperforming all input land-cover products. Compared with the strongest input product, Esri Land Cover, CADF-LC improved OA by 0.0150 and mIoU by 0.0222. Furthermore, it effectively mitigates the trade-off between detail loss and morphological fragmentation. Ultimately, CADF-Net enhances classification stability for key vegetation classes, offering a reliable foundation for transboundary ecological monitoring and land management. Full article

Human adenovirus type 7 (HAdV-7) is a significant pathogen responsible for viral community-acquired pneumonia in children. To date, no specific antiviral agents have been approved for clinical use against HAdV infections. Traditional Chinese medicine (TCM) mixtures have shown promising potential in managing viral pneumonia. This study aimed to evaluate the antiviral activity of Yuanzhixingrenheji (YZ), a hospital-prepared TCM formulation from Beijing Children’s Hospital, against HAdV-7. Initial screening of four hospital formulations (Feiyanheji, Qingjieheji, Yindaizhikeheji, and Yuanzhixingrenheji) using a CCK-8 assay revealed that YZ exhibited the lowest cytotoxicity. In vitro, YZ pretreatment and post-infection treatment exhibited dose-dependent antiviral activity against HAdV-7 in A549 cells, significantly suppressing the DBP mRNA level and protein expression while reducing viral genome copies, HAdV-7-GFP fluorescence, hexon fluorescence, and DBP nuclear localization. In the hDSG2^+/+ C57BL/6 mouse model of HAdV-7 infection, YZ effectively mitigated infection-induced body weight loss and substantially reduced viral loads in lung tissue. Furthermore, a clinical retrospective analysis indicated that YZ treatment significantly decreased post-hospitalization serum C-reactive protein levels of pediatric patients with HAdV infection in various disease severities. Compared with conventional treatment, YZ treatment also significantly reduced peak temperature and shortened the duration of fever in children with HAdV infection, supporting its therapeutic potential. In summary, this study provides the first integrated evidence from in vitro, in vivo, and clinical retrospective investigations, demonstrating that the TCM mixture YZ has significant anti-HAdV-7 activity and clinical efficacy. Characterized by a favorable safety profile and low economic burden, YZ is a promising candidate for the treatment of pediatric adenovirus pneumonia. Full article

Background: In older people, syphilis diagnosis might be undervalued due to both clinical conditions and age-related changes that obscure symptom presentation and physician discomfort with sexual history-taking, creating a dual barrier to timely recognition. Methods: Case presentation with literature review. Results: An 80-year-old woman was referred to the Dermatology Department of Cagliari University by her oncologist, with a 2-month history of intermittent episodes of pruritus associated with papular–nodular skin lesion eruptions, accompanied with asthenia, night sweats, and unintentional weight loss, indicative of a paraneoplastic syndrome or an adverse drug reaction. Careful evaluation indicated the need to perform serological testing, which confirmed secondary syphilis (RPR 1:64 and TPHA 1:5120). Specific questioning regarding sexual behaviors pointed out oral and anal intercourse. The 83-year-old husband did not have active lesions at visit but reported a self-healing generalized skin rash, episodes of asthenia, arthralgia, and headache he had never suffered before. Blood tests showed positive RPR 1:64 and TPHA 1:5120. Targeted sexual history assessment disclosed patient’s engaging with commercial sex workers, clarifying the chain of transmission in this conjugal STI case. Treatment with Benzathine penicillin G 2.4 million units IM in a single dose resulted in complete recovery in both patients. Conclusions: The observation highlights the importance of maintaining a high index of suspicion for syphilis even at advanced age. Persistent stigma regarding elderly sexuality should be faced, and targeted interventions are necessary to improve the clinician’s ability to identify STIs in older adults, but also to reduce sexual stigma and taboo persistence in the general population. Full article

In the process of extracting and transporting waxy crude oil, pipeline blockages resulting from wax deposition significantly impede production efficiency and lead to substantial economic losses. Ethylene vinyl acetate copolymer (EVA) is a widely used chemical wax inhibitor; however, its performance is influenced by multiple factors, including its molecular structure, concentration, and the carbon number distribution of the wax system. A systematic elucidation of its mechanism of action and associated phase changes is therefore necessary. In this study, differential scanning calorimetry (DSC) was employed to systematically investigate the thermal behavior of a wax system with a broad carbon number distribution (C5–C50). The objectives were to analyze the influence of EVA concentration, vinyl acetate (VA) content, and molecular weight on the phase transition parameters, to elucidate the wax inhibition mechanism, and to construct a phase prediction model based on the Flory–Huggins theory. The results demonstrate that the wax appearance temperature (WAT), phase transition temperature, and phase transition enthalpy of the wax systems increase monotonically with carbon number. Furthermore, the addition of EVA was found to significantly reduce both the WAT and the amount of wax precipitated. The optimal structural parameters were identified as a VA content of 10%, a number average molecular weight of 20,000, and an optimal concentration of 800 ppm. The medium-carbon wax system (C16–C30) was found to be the most sensitive to the EVA response. The established phase model exhibited high predictive accuracy, with a mean relative error of less than 4%, a root mean square error (RMSE) of 0.32%, and a coefficient of determination (R²) of 0.987, thereby providing preliminary insights and a practical tool for optimizing EVA wax inhibitor formulations under simplified conditions and guiding their potential engineering applications. Full article