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The role of Luteolin, Naringenin, and Scutellarin in breast cancer by inhibition of HDAC4/HDAC8
Molecular profiling of gene-edited cells reveals shared drug-resistance mechanisms
Optimized cluster based routing protocol for IoT enabled healthcare data networks
A picture of health: gene-expression maps of the human liver from living donors
The radiological impacts of radioactivity in the Crown Mines gold tailings
Deep learning for early detection of cerebral small vessel disease using self-supervised graph embeddings and retinal image analysis
Abstract The primary driver or cause of cognitive decline and stroke is Cerebral Small Vessel Disease (CSVD), which currently requires neuroimaging tests, which are expensive to obtain and inaccessible in standard clinical settings. Low-cost retinal imaging techniques offer non-invasive assessments that mirror the condition of the brain’s small blood vessels (cerebral microvasculature). State-of-the-art diagnostic methods currently have no accessible, non-invasive, or cost-effective solution to identify CSVD at its earliest stages through visual assessment of retinal biomarkers. This study presents the Retino-Neuro Vision Transformer (RNV-T) framework as a proposed method to detect CSVD utilizing multimodality retinal imaging. The model system comprises five fundamental phases, beginning with Local Vascular Extraction (LVE), followed by Global Transformer-based Encoding (GTE), then proceeding to Graph-Based Relational Learning through Graph-based Convolutional Attention Network (G-CAN) before implementing Local-Global Attention Fusion (LGAF) as well as Optimized training procedures to obtain precise micro-vascular abnormality detection. The diagnostic performance of this model reaches 98.8% accuracy and shows 97.4% sensitivity along with 98.1% specificity, surpassing previous detection approaches. This diagnostic system represents a major leap forward in neuro-ophthalmic care because it enables early prediction of CSVD while expanding medical accessibility through retinal scans that are easy to conduct.
Comparative metabolomic assessment of bioconverted traditional and black wheat via the in vitro piglet digestion model
Association of donor heavy alcohol use with graft failure after deceased-donor liver transplantation stratified by donor sex and macrosteatosis in the OPTN/UNOS registry
Abstract A subset of deceased liver donors have a history of heavy alcohol intake. We evaluated whether donor heavy alcohol history is associated with graft failure after liver transplantation (LT) and whether associations differ by donor sex and macrosteatosis. We conducted a retrospective cohort study using the OPTN/UNOS registry, including 29,170 adult LT recipients (2000–2023). The exposure was heavy alcohol intake (> 2 drinks/day). The primary outcome was graft failure. Associations were estimated using Cox proportional hazards models adjusted for donor, recipient, and transplant covariates. The proportion of donors with heavy alcohol history increased over time (2000–2007, 19.0%; 2008–2015, 20.3%; 2016–2023, 22.3%). In multivariable analyses, donor heavy alcohol history was not associated with graft failure overall (HR = 0.96 [0.90–1.03], P = 0.249). Donor macrosteatosis was independently associated with higher risk (HR = 1.07 [1.01–1.13], P = 0.020). Associations differed by donor sex: among female donors, heavy alcohol history was associated with lower graft failure risk (HR = 0.87 [0.77–0.98], P = 0.010), whereas no association was observed among male donors ( P = 0.802). The lower risk seen with female donors was not present when donor livers had macrosteatosis ( P = 0.271). In conclusion, donor heavy alcohol history was not associated with increased graft failure risk overall. Notably, grafts from female donors with a heavy alcohol history were associated with lower failure risk, an apparent advantage that was not observed in the presence of macrosteatosis. No association was observed among male donors. These findings suggest sex- and liver-quality–dependent heterogeneity in the relationship between donor alcohol history and post-transplant outcomes.
A hybrid pelican-GWO optimized fractional order PID controller for enhanced performance of hybrid active power filters
Abstract Hybrid Active Power Filter (HAPF) performance is strongly affected by the nonlinear behavior and tight coupling of control parameters, which makes traditional optimization techniques prone to unstable tuning and unreliable performance when applied to fractional-order controllers. This paper proposes an advanced control framework for HAPFs based on a novel hybrid meta-heuristic optimization approach. The method combines the adaptive search capability of the Pelican Optimization Algorithm (POA) with the social intelligence of the Grey Wolf Optimizer (GWO) to achieve a more balanced and reliable tuning process than standalone methods to efficiently tune all five parameters of a Fractional Order PID (FOPID) controller. The objective is to improve dynamic stability and harmonic attenuation under diverse operating conditions. Simulations carried out in the MATLAB/Simulink (R2018a) environment demonstrate that the proposed hybrid POA-GWO approach outperforms conventional PID controllers and FOPID controllers optimized using single algorithms. Key improvements include significant reduction in total harmonic distortion (THD) where THD of source current reduces from 28.95% to 4.34%, also the proposed hybrid FOPID controller demonstrates faster convergence and achieves a lower objective function value compared to individual optimization algorithms and conventional controllers, The results also demonstrate enhanced durability under balanced and unbalanced loading conditions. The results confirm the effectiveness of the proposed controller as a practical solution for real-time power quality enhancement in emerging smart grid applications.
Chain-mediated effects of multiple factors in physical activity on self-rated health among sedentary college students
Multi-strain bacterial combination mitigates pelvic irradiation-induced gut damage by preserving gut integrity, inhibiting inflammation and apoptosis
Abstract Pelvic irradiation effectively treats pelvic malignancies, but its side effects can be challenging, causing intestinal damage. Alterations in the gut environment can disrupt the microbiota balance, affecting key microbial communities essential for maintaining gut health. Because the gut microbiota helps maintain gut health, bacterial supplementation may reduce radiation-induced gut toxicity. This study explores the mechanism by which a multi-strain bacterial combination comprising Lactobacillus, Bifidobacterium , and Streptococcus mitigates pelvic irradiation-induced gut toxicity. Male Sprague–Dawley rats were orally administered a multi-strain bacterial combination throughout the study period or after radiation exposure. Changes in intestinal morphology, integrity, fibrosis, inflammation, and apoptosis were assessed. The prophylactic-therapeutic administration of the bacterial combination effectively preserved villus height, crypt depth, goblet cell count, and overall gut barrier integrity. Furthermore, prophylactic treatment significantly reduced radiation-induced fibrosis, inflammation, and the expression of apoptotic markers in both the jejunum and colon. In contrast, therapeutic bacterial combination treatment was less effective, suggesting that preventive administration is more beneficial in mitigating radiation-induced gastrointestinal damage. Thus, this study underscores the efficacy of pre-radiation bacterial supplementation in protecting the gut from radiation injury, with potential implications for improved patient quality of life.
Ten-year hip and knee arthroplasty implant survival in patients with inflammatory arthritis receiving methotrexate monotherapy compared with osteoarthritis: a registry-based data linkage study
Bathymetry and environmental features govern the microbial communities in mesopelagic sediments of the Lakshadweep Islands of India
Shifts in Context Affect Hippocampal Activity and the Sequence of Recall
Development of plastome-based HRM markers for the authentication of 12 major medicinal herbs in the Apiaceae family
ADAM15 promotes the progression and metastasis of hepatocellular carcinoma by activating the JNK/p38 pathway
DIPLI: deep image prior lucky imaging for blind astronomical image restoration
Abstract Modern image restoration and super-resolution methods utilize deep learning due to its superior performance compared to traditional algorithms. However, deep learning typically requires large labeled training datasets, which are rarely available in astrophotography. Deep Image Prior (DIP) bypasses this constraint by performing unsupervised optimization on a single image without training data; however, DIP often suffers from overfitting, artifact generation, and instability. This work proposes DIPLI - a framework designed specifically for resolved, high-contrast astronomical targets that shifts from single-frame to multi-frame processing using the Back Projection technique, combined with dense optical flow estimation via the TVNet model, and replaces deterministic predictions with Monte Carlo estimation obtained through Stochastic Gradient Langevin Dynamics (SGLD). A comprehensive evaluation compares the method against the original DIP, the transformer-based model RVRT, and the diffusion-based model DiffIR2VR-Zero on synthetic data with ground truth, while comparing qualitatively against Lucky Imaging on real astronomical data. On synthetic datasets, DIPLI achieves the best perceptual fidelity scores (LPIPS in 12/12 and DISTS in 10/12 scenarios), while the diffusion-based DiffIR2VR-Zero achieves the best pixel-level distortion scores (PSNR in 9/12 and SSIM in 8/12 scenarios), consistent with the well-known perceptual–distortion trade-off in image restoration (Blau and Michaeli In Proceedings of the IEEE Conference on Computer Visionand Pattern Recognition 6228–6237 2018). Compared to classical Lucky Imaging, the model requires far fewer input frames (7-13 versus thousands) and avoids the need for early stopping that limits standard DIP. Qualitative evaluation on real-world data of resolved solar-system objects, where ground truth is unavailable and domain shifts typically hinder generalization, suggests that the method appears to preserve fine detail while suppressing noise and artifacts.
Sliding sampling and successive variational mode decomposition CNN-BiLSTM-attention based fault detection and early warning method for DC microgrid
Abstract A fault detection method that integrates sliding sampling, successive variational mode decomposition (SVMD), and CNN-BiLSTM-Attention model is proposed to address the problem of insufficient sensitivity and discrimination ability in fault signal diagnosis of DC microgrids. Firstly, sliding sampling is used to capture transient fault information, avoiding the loss of information in traditional fixed windows; Secondly, by decomposing the fault signal through SVMD, the penalty factor is optimized with the maximum mutual information coefficient (MIC), and the effective modal components (IMF) are selected by combining the correlation coefficient, spectral entropy, and Teager Kaiser energy ratio to achieve noise reduction and signal reconstruction; Finally, a CNN-BiLSTM-Attention classification model is constructed, using CNN to extract local time-frequency features, BiLSTM to capture sequence context relationships, and adaptive weighting of key fault features through attention mechanism to suppress noise interference. The experimental results show that the proposed method has an average classification accuracy of 92.35% in islanding mode and 91.13% in grid connected mode, which is significantly better than the compared methods, especially with an accuracy rate of over 95% in single-phase grounding faults; The accuracy exceeds 91% in different scenarios (radial/mesh topology), verifying its robustness and adaptability.