Search Results (26,089)

Background/Objectives: Atopic dermatitis (AD) is associated with gut dysbiosis linked to early-life antibiotic use and Staphylococcus aureus colonization. Gum Arabic (GA), a prebiotic, may modulate this dysbiosis and influence AD-related microbial balance. This study evaluated whether GA could support AD-associated probiotics-Lactobacillus casei, Bifidobacterium bifidum, and Bifidobacterium infantis-under amoxicillin- or azithromycin-containing conditions, examined the response of S. aureus under the same screening conditions, and developed GA-phospholipid-based semisolid carriers for topical application. Methods: Probiotic strains were cultured with 1–5% GA in the presence and absence of antibiotics, and viable cell counts were assessed. Sixteen topical formulations containing propylene glycol or isopropyl myristate in a hydrogenated phosphatidylcholine base were prepared and screened for rheological properties and galactose release using in vitro release testing (IVRT) and HPLC-UV. Results: GA at 1–2% concentrations promoted probiotic growth in antibiotic-free conditions. GA preserved B. infantis viability under azithromycin exposure in this in vitro screening model. For S. aureus, numerical CFU differences were observed between antibiotic-only and GA-containing conditions; however, the present screening design was not intended to determine antibiotic interaction outcomes. Formulations F14 (2% GA + 7% IPM) and F15 (3% GA + 7% IPM) exhibited optimal spreadability. IVRT showed that 6 h cumulative galactose release varied by formulation (F6 > F10 > F14 > F15). Conclusions: GA demonstrated dose-dependent prebiotic activity and preserved B. infantis viability under azithromycin exposure in this in vitro screening model. For S. aureus, the observed CFU differences between antibiotic-only and GA-containing conditions should be considered exploratory only and do not allow for conclusions regarding interference with antibiotic efficacy. Optimized GA-HPC systems with suitable rheological and release characteristics represent promising candidates for further preclinical investigation. Full article

Background/Objectives: The frequent use of antibiotics has led to increasing drug resistance in Aeromonas hydrophila; therefore, there is an urgent need to develop novel antimicrobial agents to prevent and control bacterial diseases in aquaculture. Allicin E (ALE) is derived from garlic (Allium sativum L.), a plant extensively used in traditional medicine for treating infections. This study aimed to evaluate the potential of ALE against A. hydrophila, a major aquaculture pathogen, by investigating its antibacterial efficacy, mechanisms of action, and in vivo protective effects. Methods: The minimum inhibitory and bactericidal concentrations (MIC/MBC) were determined by broth microdilution. Antibacterial mechanisms were investigated through ROS detection, electron microscopy, fluorescent staining, and content leakage measurement. In vivo efficacy was evaluated in Carassius auratus gibelio by monitoring survival rates and bacterial loads, analyzing immune and antioxidant biomarkers, and histopathological analysis after A. hydrophila challenge. Results: ALE exhibited potent antibacterial activity (MIC = MBC = 8 μg/mL), achieving complete bacterial elimination within 1 h and showing a low resistance propensity. Mechanistically, ALE induced ROS accumulation, causing oxidative damage that disrupted membrane integrity and facilitated the leakage of cellular contents. In vivo, ALE significantly enhanced fish survival, reduced bacterial loads, modulated inflammatory cytokines, boosted antioxidant enzyme activities (SOD and CAT), and alleviated tissue damage. Conclusions: ALE possesses potent in vitro antibacterial activity and exerts an inhibitory effect on bacteria-induced inflammatory responses, effectively combating A. hydrophila through a multi-target mechanism and enhancing host resistance. Full article

Background/Objectives: Fibroblast activation protein (FAP) has emerged as a promising target for oncologic molecular imaging due to its high expression in cancer-associated fibroblasts and low presence in healthy tissues. Among available FAP ligands, [⁶⁸Ga]Ga-FAPi-46 has shown rapid tumor accumulation, low background uptake, and broad tumor applicability. This study reports the successful translation of [⁶⁸Ga]Ga-FAPi-46 from preclinical development to routine clinical radiopharmacy practice, detailing automated synthesis, quality control performance, radiochemical stability, and the first clinical imaging results. Methods: Automated radiolabeling of FAPi-46 with gallium-68 was performed using a synthesis module. Quality control included radiochemical purity assessments by iTLC, SPE, and RP-HPLC (pH, appearance, endotoxin levels, and membrane integrity testing). Radiochemical stability was evaluated in saline (up to 6 h) and human serum (up to 90 min). In vitro characterization included the partition coefficient and serum protein binding determination. A clinical evaluation was conducted in a woman with newly diagnosed lung adenocarcinoma who underwent both [¹⁸F]FDG PET/CT and [⁶⁸Ga]Ga-FAPi-46 PET/CT. Results: Automated synthesis of [⁶⁸Ga]Ga-FAPi-46 achieved a high radiochemical yield (87.9 ± 1.3%) and radiochemical purity greater than 98%. All batches met release specifications for sterility, apyrogenicity, and physicochemical parameters. The radiotracer demonstrated high stability in saline and human serum, with radiochemical purity consistently above 95% at all evaluated time points. The compound showed a hydrophilic profile (LogP = −3.32 ± 0.14) and 40–60% serum protein binding. Clinically, [⁶⁸Ga]Ga-FAPi-46 PET/CT provided superior lesion delineation compared to [¹⁸F]FDG, revealing additional mediastinal, supraclavicular, and brain metastases. Conclusions: [⁶⁸Ga]Ga-FAPi-46 can be reliably synthesized using automated procedures under routine radiopharmacy conditions, meeting regulatory quality standards and demonstrating excellent stability. Its enhanced lesion detectability compared with [¹⁸F]FDG in the first patient case supports its potential value for oncological staging and clinical implementation. Full article

During the interpretation of Borehole Radar (BHR) B-scan profiles, the accurate determination of the azimuth of geological targets in three-dimensional space is a critical issue for achieving precise anomaly localization and spatial structure inversion. However, existing directional BHR anomaly localization methods exhibit limited intelligence, insufficient adaptability to multi-site data, and weak generalization capability, rendering them inadequate for engineering applications under complex geological conditions. To address these challenges, a robust deep learning model, termed BSS-Pose-BHR, is developed based on YOLOv11n-pose for keypoint detection in directional BHR profiles. The model incorporates three key optimizations: Bi-Level Routing Attention (BRA) replaces Multi-Head Self-Attention (MHSA) in the backbone to improve computational efficiency; Conv_SAMWS enhances keypoint-related feature weighting in the backbone and neck; and Spatial and Channel Reconstruction Convolution (SCConv) is integrated into the detection head to reduce redundancy and strengthen local feature extraction, thereby improving suitability for keypoint detection tasks. In addition, a three-dimensional electromagnetic model of limestone containing a certain density of clay particles is established to construct a simulation dataset. On the simulated test set, compared with current mainstream deep learning approaches and conventional directional borehole radar anomaly localization algorithms, BSS-Pose-BHR achieves superior performance, with an mAP50(B) of 0.9686, an mAP50–95(B) of 0.7712, an mAP50(P) of 0.9951, and an mAP50–95(P) of 0.9952. Ablation experiments demonstrate that each proposed module contributes significantly to performance improvement. Compared with the baseline, BSS-Pose-BHR improves mAP50(B) by 5.39% and mAP50(P) by 0.86%, while increasing model weight by only 1.05 MB, thereby achieving a reasonable trade-off between detection accuracy and complexity. Furthermore, indoor physical model experiments validate the effectiveness of the method on measured data. Robustness experiments under different Peak Signal-to-Noise Ratio (PSNR) conditions and varying missing-trace rates indicate that BSS-Pose-BHR maintains high detection accuracy under moderate noise and data loss, demonstrating strong engineering applicability and practical value. Full article

Recent evidence links lens epithelial cell (LEC) dysfunction and cellular senescence—an irreversible cell cycle arrest with a pro-inflammatory secretory phenotype—to age-related cataract (ARC) progression. This systematic review synthesizes current knowledge on LEC senescence, its molecular features, and laboratory methods for senescence assessment in the ARC. Following PRISMA guidelines, a comprehensive search of PubMed, Scopus and Cochrane databases retrieved 3417 records from inception to 9 February 2025, with 14 studies ultimately included (821 patients and multiple in vitro LEC models). The following multiple senescence expression pathways were identified: SA-β-gal activity, p53/p21 and p16INK4A pathway activation, mitochondrial dysfunction, oxidative stress, and secretion of senescence-associated secretory phenotype (SASP) factors. Notably, cortical cataract demonstrated direct association with local senescent cell accumulation, while nuclear cataract reflected cumulative oxidative damage from impaired LEC-mediated antioxidant defense. Senescence markers correlated positively with cataract severity across multiple studies. Several potential therapeutic targets emerged, including metformin (AMPK activation/autophagic restoration), circMRE11A silencing, NLRP3 inflammasome inhibition, and modulation of FYCO1/PAK1 and MMP2 pathways. This review establishes LEC senescence as a central process in ARC pathogenesis and highlights promising senotherapeutic approaches. Future research should prioritize human surgical samples, develop standardized senescence detection panels (SA-β-gal + p21/p16 + SASP factors), and conduct longitudinal studies to establish causal relationships between senescence accumulation and cataract progression. Full article

Background/Objectives: The preschool period is critical for neurodevelopment, yet evidence investigating fortified formula’s effect and potential microbiota–gut–brain axis mechanisms in this age group is limited. To evaluate fortified formula milk’s effect on neurodevelopment and explore potential microbiota–gut–brain axis mechanisms in preschool children. Methods: In this 9-month cluster-randomized, double-blind, controlled trial, 120 healthy children aged 3–6 years from four kindergarten classes were stratified by grade and randomly allocated (1:1) to receive either multi-nutrient fortified formula (intervention, n = 60) or standard control milk (n = 60). Neurocognitive function was assessed using the Wechsler Preschool and Primary Scale of Intelligence, Fourth Edition (WPPSI-IV). Safety was evaluated through anthropometry and blood biochemistry. Gut microbiota (16S rRNA sequencing) and fecal metabolomes (untargeted LC-MS) were analyzed at baseline and 9 months. Results: The intention-to-treat (ITT) analysis showed no significant difference in Full Scale Intelligence Quotient (adjusted mean difference: 1.05 points; 95% CI: −1.42, 3.52; p = 0.400). However, the intervention group significantly improved the Processing Speed Index (adjusted mean difference: 5.91 points; 95% CI: 1.88, 9.93; p = 0.004), increased gut microbial alpha diversity (Shannon index) and Bifidobacterium abundance. Metabolomic analysis revealed elevated fecal 2-hydroxybutyric acid (2-HB), a marker of propanoate metabolism. Increases in both Bifidobacterium and 2-HB levels showed a positive association with PSI improvement (both p < 0.05). All children maintained normal growth and safety parameters. Conclusions: Fortified formula milk improved processing speed in preschoolers, a benefit associated with gut ecosystem modulation characterized by Bifidobacterium enrichment and upregulated microbial propanoate metabolism. These results offer preliminary evidence for the role of the microbiota–gut–brain axis in nutritional cognitive programming during early childhood. (Clinical Trial Registry: ChiCTR2400084211). Full article

Virtual try-on is a key enabling technology for online fashion retail and digital garment visualization. It aims to realistically render a target garment on a person while preserving geometric alignment and fine texture details. Appearance flow-based approaches provide explicit deformation modeling but often suffer from texture squeezing and boundary artifacts in challenging scenarios, such as long sleeves and tucked-in garments, especially under high-resolution settings. In this work, we propose StageAttn-VTON (Stage-wise Attentive Virtual Try-On), an appearance flow-based framework that improves structural coherence and visual fidelity through stage-wise deformation modeling. Specifically, garment warping is decomposed into three stages—coarse alignment, local refinement, and non-target region removal—which mitigates the coupling between competing objectives, such as smooth texture preservation and accurate structural alignment. Furthermore, we introduce a self-attention module in the image synthesis stage to enhance global dependency modeling and capture long-range garment–body interactions. Experiments on VITON-HD and the upper-body subset of DressCode demonstrate that StageAttn-VTON achieves consistently strong performance against representative warping-based and diffusion-based baselines. In addition, qualitative comparisons show that the proposed method better alleviates deformation artifacts in challenging regions such as sleeves and waist areas. Full article

Background/Objectives: Ulcerative colitis (UC) involves inflammatory response, oxidative stress, changes in metabolites, and the gut microbiota. Jingangteng capsule (JGTC) has been utilized clinically for the treatment of inflammatory diseases for many years. However, the efficacy of JGTC in ameliorating UC remains unclear, and the underlying mechanisms have not yet been elucidated. This study aims to investigate the effect and mechanism of JGTC on UC. Methods: The chemical compositions of JGTC were examined using ultra-high-performance liquid chromatography with quadrupole time-of-fight mass spectrometry. The anti-UC effect of JGTC was evaluated by assessing the disease activity index (DAI), colon length, intestinal barrier recovery, and inflammatory factors in a dextran sulfate sodium (DSS)-induced colitis model. Mechanisms were investigated through fecal 16S rDNA sequencing, metabolomics analysis, enzyme-linked immunosorbent assay (ELISA), Western blotting, and network pharmacology analysis. Results: JGTC significantly reduced the DAI scores in UC mice, increased their body weight and colon length (p < 0.001), repairing damaged intestinal tissue. It decreased the levels of inflammatory cytokines TNF-α, IL-6, IL-1β, and LPS (p < 0.01, p < 0.001), alleviating intestinal inflammation. It also raised the expression of tight junction proteins ZO-1, Claudin-1, and Occludin (p < 0.05, p < 0.001), thereby enhancing intestinal barrier function. Fecal metabolomic analysis revealed that the favorable alterations in amino acid and lipid metabolites were more pronounced. Heat maps showed strong correlations between pharmacological indicators and gut microbiota, as well as between the main differential metabolites and gut microbial communities. UPLC-QTOF-MS detection yielded 33 components of JGTC, and network pharmacology analysis based on these components predicted pathways of action of JGTC in UC. Functional pathways closely associated with significantly differential metabolites and metabolic pathways were also investigated. The PI3K-AKT-mTOR pathway was one of them, which is consistent with the conclusions drawn from network pharmacology. JGTC significantly modulated key factors in this pathway, inhibiting the expression of PI3K, Akt, PDK1, and mTOR, while augmenting the expression of PTEN (p < 0.05, p < 0.01, p < 0.001). It also mitigated the levels of related oxidative stress factors MDA, MPO, and D-LA, and raised SOD levels (p < 0.01, p < 0.001). Conclusions: JGTC improved the excessive inflammatory response in UC by regulating intestinal flora and metabolic disorders, affecting the PI3K-AKT-mTOR signaling pathway, restoring intestinal tissue damage and intestinal barrier, and inhibiting inflammatory and oxidative stress factors. Full article

Background/Objectives: Osteonecrosis of the femoral head (ONFH) is a progressive condition characterized by bone necrosis, impaired vascularization, and immune dysregulation, often resulting in femoral head collapse. Effective strategies to halt disease progression are limited. Extracellular vesicles (EVs), including exosomes and microvesicles, mediate intercellular communication and influence osteogenesis, angiogenesis, and immune responses. This review summarizes current evidence on EVs in ONFH and their translational potential. Methods: A structured narrative review of PubMed, Scopus, Web of Science, and Cochrane Central databases was conducted, including in vitro, preclinical, and clinical studies on EVs in ONFH. Data on EV sources, molecular cargo, signaling pathways, functional effects, and translational implications were qualitatively synthesized. No pooled statistical analysis was performed because the extracted data were heterogeneous. Bioinformatic analyses such as Gene Ontology, KEGG enrichment, and protein–protein interaction networks were also summarized. Results: In vitro, EVs from bone marrow mesenchymal stem cells, endothelial cells, and M2 macrophages modulate osteogenic differentiation, angiogenesis, and inflammation. Preclinical studies demonstrate that EV administration reduces femoral head necrosis, improves trabecular structure, and enhances neovascularization. Clinical studies have identified EV-associated molecules (SAA1, C4A, RPS8) linked to disease stage and the risk of femoral head collapse. Bioinformatic analyses connect EV cargo to pathways regulating bone formation, vascularization, immunity, and metabolism. Conclusions: EVs appear to play key roles in ONFH pathogenesis and may represent promising candidates for diagnostic and therapeutic applications. However, current clinical evidence remains limited and requires validation in larger studies. Nonetheless, heterogeneity and limited clinical data require standardized, longitudinal studies to validate their translational relevance. Full article

In recent years, the number of deaths caused by traffic accidents has continued to rise. According to investigations, approximately one-fifth of accidents are caused by drivers being distracted. With the rapid development of convolutional neural networks (CNNs) in the field of computer vision, many researchers have developed CNN-based network models to recognize distracted driving actions. However, many models have too many parameters, making them unsuitable for deployment in actual vehicles. To address this issue, we propose a multiscale driver distraction detection network called ICK-PANet, which combines attention, lightweight incremental convolution kernels, and lightweight pyramid convolution to quickly and accurately identify driver distraction actions. First, ICK-PANet uses lightweight incremental convolution kernels to capture global information and driving action details effectively. Then, it introduces lightweight pyramid convolution and attention modules to extract multistage features, thereby expanding the network’s receptive field to improve the recognition ability of key features. Finally, it fuses multistage features to predict the results. ICK-PANet was experimentally evaluated on two public datasets: the American University in Cairo Distracted Driver (AUC) dataset and the StateFarms dataset (SFD) provided by the Kaggle competition platform. The AUC and SFD accuracies are 95.66% and 99.84%, respectively, which are higher than those achieved by many other state-of-the-art methods. ICK-PANet requires only 0.4M parameters, making it one of the most lightweight models currently available. Full article

As a key task in remote sensing analysis, semantic segmentation of remote sensing images (RSI) underpins many practical applications. Despite its importance, obtaining dense pixel-wise annotations remains labor-intensive and time-consuming. Unsupervised domain adaptation (UDA) offers a promising solution by utilizing knowledge from labeled source domains for unlabeled target domains, yet its effectiveness is often compromised by significant distribution shifts arising from variations in imaging conditions. To address this challenge, we propose a depth-aware adaptation network (DAAN), a novel two-branch network that explicitly leverages complementary depth information from a digital surface model (DSM) to enhance cross-domain remote sensing segmentation. Unlike conventional UDA methods that primarily focus on semantic features, DAAN incorporates depth data to build a more generalized feature space. This network introduces three key components: an adaptive feature aggregator (AFA) for progressive semantic-depth feature fusion, a gated prediction selection unit (GPSU) that selectively integrates predictions to mitigate the impact of noisy depth measurements, and misalignment-focused residual refinement (MFRR) module that emphasizes poorly aligned target regions during training. Experiments on the ISPRS and GAMUS datasets demonstrate the effectiveness of the proposed method. In particular, DAAN achieves an mIoU of 50.53% and an F1 score of 65.75% for cross-domain segmentation on ISPRS to GAMUS, outperforming models without depth information by 9.17% and 8.99%, respectively. These results demonstrate the advantage of integrating auxiliary geometric information to improve model generalization on unlabeled remote sensing datasets, contributing to higher mapping accuracy, more reliable automated analysis, and enhanced decision-making support. Full article

Nonlinear state estimation in electric power systems remains challenging under mixed-measurement conditions due to the coexistence of legacy SCADA and PMU data with markedly different reliability levels, the sensitivity of classical Gauss–Newton-type methods to heterogeneous noise and numerical conditioning, and the increasing complexity of large-scale grids. To address these issues, this paper proposes ST-ResGAT, a spatio-temporal residual graph attention framework for nonlinear state estimation under heterogeneous sensing conditions. The proposed method models the problem on an augmented heterogeneous factor graph, employs a reliability-aware heterogeneous graph attention mechanism with residual propagation to adaptively fuse measurements of different quality, and further refines the graph-based estimates through a lightweight LSTM post-processing module that exploits short-term temporal continuity. All datasets are generated using pandapower on the IEEE 30-bus, IEEE 118-bus, and IEEE 1354-bus benchmark systems to ensure full reproducibility of the experimental pipeline. Experimental results show that the proposed method consistently achieves lower estimation errors than WLS, DNN, GAT, and PINN baselines across all three systems, while also exhibiting more compact node-level error distributions and stronger spatial consistency. Multi-seed ablation studies further indicate that residual propagation, reliability-aware attention, and temporal refinement play complementary roles across different system scales. Robustness experiments additionally show that, under random measurement exclusion as well as bias, Gaussian, and mixed corrupted-measurement settings, ST-ResGAT exhibits smooth and progressive degradation, including on the newly added large-scale IEEE 1354-bus benchmark. These results suggest that the proposed framework is a promising direction for data-driven state estimation under controlled mixed-measurement benchmark conditions. Full article

Background/Objectives: Curcumin derivatives have attracted interest due to their redox-modulating properties and potential applications in aquatic organisms, yet their molecular interactions and environmental safety remain insufficiently characterized. This study aimed to evaluate the redox-related molecular behavior and ecotoxicological profile of curcumin derivatives, with emphasis on their interaction with glutathione S-transferase from L. vannamei. Methods: Molecular docking and molecular dynamics simulations were performed to assess binding stability and interaction patterns between the derivatives and LvGSTmu. In parallel, computational predictions were used to estimate environmental persistence, bioaccumulation (BCF/BAF), and acute and chronic aquatic toxicity across multiple trophic levels. Results: Docking and dynamics analyses indicated stable ligand–protein interactions, particularly for CURNO, which showed favorable binding behavior without destabilizing the protein structure. Ecotoxicological predictions suggested low bioaccumulation potential and limited persistence for most derivatives, with CURH and CURNO showing higher sediment persistence. Toxicity responses varied by organism and exposure time but did not differ significantly among derivatives relative to curcumin. Conclusions: The derivatives retained redox-related molecular features while presenting an overall acceptable predicted environmental profile. CURNO emerged as a promising candidate, although its environmental behavior supports the need for further monitoring and experimental validation. Full article

Millimeter-wave (mmWave) radar point clouds offer a privacy-preserving solution for Human Activity Recognition (HAR), but their inherent sparsity and noise limit single-modal performance. While multimodal fusion mitigates this issue, existing methods often suffer from severe negative transfer during visual degradation and incur high computational costs, unsuitable for edge devices. To address these challenges, we propose Tac-Mamba, a lightweight cross-modal state space model. First, we introduce a topology-guided distillation scheme that uses a Spatial Mamba teacher to extract structural priors from visual skeletons. These priors are then explicitly distilled into a Point Transformer v3 (PTv3) radar student with a modality dropout strategy. We also developed a Trust-Aware Cross-Modal Attention (TACMA) module to prevent negative transfer. It evaluates the reliability of visual features through a SiLU-activated cross-modal bilinear interaction, smoothly degrading to a pure radar-driven fallback projection when visual inputs are corrupted. Finally, a Lightweight Temporal Mamba Block (LTMB) with a Zero-Parameter Cross-Gating (ZPCG) mechanism captures long-range kinematic dependencies with linear complexity. Experiments on the public MM-Fi dataset under strict cross-environment protocols demonstrate that Tac-Mamba achieves competitive accuracies of 95.37% (multimodal) and 87.54% (radar-only) with only 0.86M parameters and 1.89 ms inference latency. These results highlight the model’s exceptional robustness to modality missingness and its feasibility for edge deployment. Full article