Search Results (124,795)

The complexity and heterogeneity of tumors make monotherapy inadequate for effective tumor elimination, highlighting the urgent need for multifunctional synergistic therapeutic strategies. In this study, a near-infrared (NIR)-responsive multimodal therapeutic nanoplatform (FSINPs) was constructed by simple adsorption of indocyanine green (ICG) and Fe³⁺ onto silk fibroin nanoparticles. Molecular docking showed that ICG binds stably to silk fibroin mainly via hydrogen bonds. Density functional theory (DFT) calculations predicted that Fe³⁺ strongly coordinates with the sulfonate groups of ICG and quenches ICG fluorescence via intermolecular charge transfer. Under conditions mimicking the acidic and high-glutathione tumor microenvironment, the ICG-Fe³⁺ coordination is disrupted, leading to fluorescence recovery of FSINPs. Fe³⁺ catalyzes the Fenton reaction to generate hydroxyl radicals (·OH), thereby achieving chemodynamic therapy (CDT). Upon 808 nm laser irradiation, ICG acts as a dual photosensitizer capable of both photothermal therapy (PTT) and photodynamic therapy (PDT), generating local hyperthermia with a photothermal conversion efficiency as high as 48.7% and producing singlet oxygen (¹O₂). The photothermal effect facilitates ·OH production, and CDT enhances photodynamic efficacy. The synergistic action of the three therapeutic modalities results in potent light-activated cytotoxicity toward tumor cells. In vivo experiments demonstrated that FSINPs enable tumor microenvironment-responsive fluorescence imaging for over 48 h and achieve complete tumor eradication in a 4T1 tumor model without obvious systemic toxicity. This study provides a new strategy for constructing activatable imaging and highly synergistic PTT/CDT/PDT-integrated silk fibroin-based nanomedicines and offers computational chemistry references for their rational design and development. Full article

Background/Objectives: Ulcerative colitis (UC) is a chronic, relapsing inflammatory bowel disease (IBD) lacking therapies that directly promote mucosal healing, an important clinical endpoint and therapeutic goal for achieving remission and improved long-term outcomes. Epithelial restitution is a key component of effective mucosal healing. EPICERTIN, a novel biotherapeutic candidate, has previously demonstrated epithelial repair activity in dextran sulfate sodium (DSS)-induced colitis models and enhanced epithelial cell viability in human IBD colon explants. To further substantiate its therapeutic efficacy in a UC-relevant context, we evaluated EPICERTIN in acute oxazolone (OXA)-induced colitis in BALB/c mice, a model reflecting the adaptive immune-driven inflammation and histopathologic characteristics of human UC. Methods: Orally administered EPICERTIN, at escalating doses (0.3 µg, 3 µg, and 30 µg), was assessed for therapeutic efficacy in male and female mice through body weight, Disease Activity Index (DAI) scores, and histopathology. Wound healing and immune impacts were examined using qRT-PCR, Imaging Mass Cytometry (IMC), and Cytometry by Time of Flight (CyTOF) in male mice. Results: EPICERTIN effectively mitigated acute OXA-induced colitis. The 3 µg dose provided the greatest benefit, improving body weight recovery and reducing DAI and histopathological damage scores. Treatment reduced Il1b while increasing Cdh1 expression, accompanied by higher levels of epithelial markers (pan-cytokeratin (PanCK), E-cadherin, epithelial cell adhesion marker (EpCAM)) and decreased fibrotic markers (collagen type I, alpha-smooth muscle actin (α-SMA), fibronectin). Moreover, EPICERTIN reduced inflammatory lymphoid and myeloid cells while increasing γδ T cells in the colon lamina propria. Conclusions: Collectively, these results demonstrate that EPICERTIN promotes epithelial restitution, suppresses inflammation, and supports mucosal healing, substantiating its therapeutic potential for UC. Full article

Tool wear detection is essential for machining quality control and predictive maintenance, but conventional inspection is often manual, time-consuming, and operator-dependent. Existing learning-based visual methods still face challenges in jointly achieving reliable wear-type recognition, accurate wear-region localization, and quantitative wear-width measurement under shop-floor imaging conditions. To address these issues, this study proposes MaskLenNet, a query-based instance segmentation and length prediction network for solid carbide end-milling tool diagnosis. MaskLenNet combines a Swin Transformer backbone, query-based instance-mask prediction, wear-oriented attention, and a key-point head that directly estimates the maximum wear-land width (VB). Evaluation uses 234 images from 54 physical tools under a tool-disjoint split, so different rotations of one tool cannot occur in both training and evaluation sets. On the held-out test set, MaskLenNet achieves 96.52% matched-instance classification accuracy, 95.75% foreground instance mIoU, and a VB mean absolute error of 0.010214 mm. Relative to BEiT-Base, the gains are 3.04 and 3.60 percentage points in accuracy and mIoU, respectively. These results demonstrate promising performance within the evaluated acquisition system; they do not establish equivalence to microscopy or generalization to other machines, optics, workpiece materials, or sites. Full article

To investigate the effects of 5-hydroxymethylfurfural (5-HMF) on ruminal fermentation characteristics and bacterial community structure, 5-HMF was supplemented at the levels of 0, 7500, and 30,000 mg/kg DM under two dietary concentrate-to-roughage ratios (20:80, T1; 80:20, T2) in in vitro rumen incubation, mainly focusing on gas production, fermentation parameters, nutrient digestibility and bacterial community. The results showed that 24 h gas production (GP₂₄), 72 h gas production (GP₇₂), the gas production rate (c) and dry matter digestibility (DMD) in T2 were higher than those in T1 (p < 0.01). With increasing levels of 5-HMF supplementation, GP₇₂ and asymptotic gas production (B) increased linearly and quadratically (p < 0.05). T2 also had a higher butyrate concentration and a lower acetate/propionate (A/P) ratio than T1 (p < 0.05). With the increase in 5-HMF supplementation level, ruminal ammonia nitrogen (NH₃-N) concentration showed a quadratic response. In contrast, the concentrations of total volatile fatty acids (TVFAs), acetate and isobutyrate decreased linearly. Propionate concentration showed linear and quadratic decreasing trends, while the A/P ratio exhibited linear and quadratic increasing trends (p < 0.05). The supplementation of 5-HMF at 30,000 mg/kg DM reduced propionate and isobutyrate concentrations under both dietary conditions (p < 0.05). No differences were observed in the alpha diversity indices of the bacterial community among different 5-HMF levels or substrates (p > 0.05). PCoA and PERMANOVA revealed that community structure differed between the 7500 mg/kg DM group and the 0 mg/kg DM group in the T1 diet (p = 0.011), whereas no difference was detected among T2 treatments. LEfSe revealed that 6 and 13 differential bacterial taxa were identified in T1 and T2 at the 5-HMF level of 7500 mg/kg DM, respectively. Correlation analysis revealed that, in T1, GP₇₂ was positively correlated with Pseudomonadota, and the A/P ratio was positively correlated with fibrolytic taxa including Rikenellaceae_RC9_gut_group and Christensenellaceae_R-7_group. In T2, c was positively correlated with Fibrobacterota and Fibrobacter, while NH₃-N was positively correlated with Desulfovibrio, indicating diet-specific microbial associations with rumen fermentation (p < 0.05). In conclusion, the high-concentrate diet exhibited stronger rumen fermentation activity. 5-HMF exerted diet-specific effects: In the low-concentrate diet, 5-HMF at 7500 mg/kg DM enriched fibrolytic microbes and shifted fermentation toward an acetate-type pattern. In the high-concentrate diet, it modulated hydrogen-metabolizing bacteria while selectively enriching Pseudomonadota capable of participating in carbohydrate metabolism. The 30,000 mg/kg DM level inhibited rumen fermentation. The findings provide a theoretical reference for the application of 5-HMF in ruminant feeds. However, further in vivo trials are required to validate its effects, and future studies with refined dose gradients based on realistic 5-HMF concentrations in feedstuffs are needed to comprehensively evaluate its efficacy and safety. Full article

Strictly causal dense arm-motion recognition from surface electromyography (sEMG) requires past context without future samples. We evaluated single-scale exponential moving average (EMA) latent memory as a lightweight causal temporal mechanism under a controlled, matched design. Causal CNN, causal CNN–Transformer (CT), and causal CNN with EMA memory (CEMA) were tested on a 30-subject, eight-channel, seven-class dataset in five subject-independent folds. At 300, 500, and 800 ms, five deterministic seeds were averaged within-fold before inference. CEMA accuracy exceeded CNN accuracy by 9.105, 9.313, and 9.657 percentage points, respectively (all Holm-adjusted paired-t p < 0.001). CEMA–CT differences were +0.209, −0.240, and −0.715 points, respectively; confidence intervals included zero, and directions varied across seeds. With parameters frozen, resetting memory at the current-window start reduced CEMA accuracy by 10.928–17.009 points. CEMA–CT B100 recall differences were −4.97, −12.84, and −16.54 points, respectively, whereas absolute-delay differences were 6.79, 7.71, and 6.94 ms, respectively, all below 8 ms. At 300 ms, CEMA used 6968 parameters and 0.751 M MACs, 48.0% fewer MACs than CT; CT required 2.31× CEMA’s full-buffer host-wall time. CEMA offers a favorable average accuracy–efficiency trade-off, although CT remains more responsive at transitions. Timing reflects full-buffer host inference only, not incremental, embedded, or end-to-end deployment. Full article

Background: Bronchial asthma is a chronic inflammatory airway disease characterized by acute bronchoconstriction and type 2-driven inflammation. This study investigated whether inhaled amitriptyline, a functional inhibitor of acid sphingomyelinase, exerts both bronchodilatory and immunomodulatory effects in experimental murine models of allergic airway inflammation (AAI) and human cellular systems. Methods: Acute AAI was induced in mice using ovalbumin (OVA) and house dust mite (HDM) protocols, respectively. Inhaled amitriptyline (3.3 mg/mL) was administered for either 20 days (short-term) or 36 days (long-term). Lung function was assessed using FlexiVent^®, and inflammatory markers including IgE, eosinophils, and type 2 cytokines were measured in bronchoalveolar lavage fluid and lung tissue. Complementary experiments were included using passively sensitized PCLSs and human type 2-differentiated CD4⁺ T cells. Results: Inhaled amitriptyline improved lung mechanics in both the OVA and HDM models, reducing total respiratory resistance and elastance. In the OVA model, eosinophil and T cell counts in BALF were decreased, whereas immunomodulatory effects were less pronounced in the short-term HDM model. In human T_H2 cells, no significant changes in cytokine production or gene expression were observed. Ex vivo, amitriptyline dose-dependently inhibited allergen-induced bronchoconstriction in PCLSs. Conclusions: Inhaled amitriptyline improves lung function across murine models of AAI, supporting its potential in exhibiting model-dependent immunomodulatory effects, and directly attenuates allergen-induced bronchoconstriction, supporting its potential as a bronchodilator with context-dependent immunomodulatory properties. Full article

Background/Objectives: Adolescents from multicultural families in South Korea, most with foreign-born mothers and Korean fathers, may be vulnerable to peer victimization due to cultural, linguistic, or appearance-related differences. Grounded in minority stress theory, this study examined whether acculturative stress mediated the prospective association between peer victimization and depressive symptoms and whether family support, perceived across T1 (Year 1 of middle school) and T2 (Year 2 of middle school), moderated these pathways among adolescents from multicultural families. Methods: The analytic sample comprised 1218 adolescents (50.8% girls) drawn from the Multicultural Adolescents Panel Study, a national longitudinal dataset from South Korea. Mediation and moderated mediation analyses were conducted using the PROCESS macro. Results: Peer victimization at T1 was associated with higher depressive symptoms at T2 through acculturative stress assessed concurrently at T2, warranting caution in interpreting the indirect association as causal. Family support moderated the peer victimization–acculturative stress and acculturative stress–depressive symptoms paths, but not the direct peer victimization–depressive symptoms path. Specifically, family support weakened the association between peer victimization and acculturative stress, whereas it strengthened the association between acculturative stress and depressive symptoms. No overall moderated mediation pattern was evident because the two path-specific moderation effects operated in opposing directions and offset each other. Conclusions: These findings suggest that family support may operate differently across stress pathways. This study underscores the importance of culturally responsive school–family collaboration to reduce peer victimization, address acculturative stress and depressive symptoms, and equip families to support adolescents’ constructive coping. Full article

During the fabrication of prestressed concrete beams, the quality and positional accuracy of the laid corrugated ducts (or prestressing ducts) directly influence the load-bearing capacity and durability of the beams. However, traditional manual inspection is inefficient, highly subjective, and difficult to achieve full coverage. To address this problem, this paper proposes an automated detection method that integrates improved YOLOv11-based pose estimation, robust curve fitting, and image stitching techniques. The method automatically identifies duct positions and evaluates laying quality. By incorporating the SE channel attention mechanism and the SPPFCSPC multi-scale pooling module, the SC-YOLOv11 model is developed, which significantly enhances the detection accuracy of slender corrugated pipe key points in environments with dense rebar occlusion. The RANSAC algorithm is employed to fit curves to the predicted key points, effectively suppressing the influence of outliers. Furthermore, the SIFT algorithm is used for precise stitching of drone-captured segmented images, which are then transformed into a unified front orthographic coordinate system of the entire box girder via perspective transformation, enabling accurate reconstruction of the corrected 2D layout of corrugated ducts across the full beam. Ablation experiments using 5-fold cross-validation demonstrate that SC-YOLOv11 improves mAP₅₀ and mAP_50–95 by 2.6% and 1.2%, respectively, with statistical significance (paired t-test, p < 0.01). The model achieves a per-image inference time of 6.37 ms, with 4.34 M parameters and 8.1 GFLOPs, meeting real-time requirements. In a 30 m prefabricated box girder field application, the measured section trajectory fitting curves of the corrugated ducts were compared with the design alignment, successfully identifying two abnormal locations where the laying deviation exceeded the allowable threshold. Cross-validation with on-site inspector records shows that over 92% of the measurement points agree within ±10 mm. This method achieves a fully automated analysis chain from key point detection and curve fitting to deviation quantification, providing an efficient, non-contact, and traceable intelligent tool for quality control of bridge prestressed systems. Full article

Volatility regime identification underpins risk management and portfolio allocation in quantitative finance, yet standard mixture models fail in heavy-tailed environments: components are consumed by outliers rather than genuine persistent regimes. We propose the Bayesian Spike-and-Slab Finite Mixture with Adaptive Tail Regularisation (BSS-FM-ATR), which resolves this at the component level via a spike-and-slab prior on the degrees-of-freedom parameter ${\nu }_k$. A latent binary indicator assigns each component to a slab state (data-driven tail adaptation for genuine regimes) or a spike state (inert heavy-tail absorber for artefacts). Applied to 19 JSE blue-chip securities over December 2019 to December 2025—spanning the COVID-19 crash and Eskom load-shedding episodes—BSS-FM-ATR achieves the highest silhouette score (0.3809 on the full dataset), regime persistence (0.9391), and interpretability (0.800) across nine standard baselines, including Gaussian HMM, MS-AR, MS-GARCH(1,1), and Bayesian Changepoint detection, plus three outlier-component comparators (Contaminated–Normal Mixture, TCLUST, and robust Bayesian mixture). A complete rerun after removing the contaminated observations confirms that ARI ($\Delta =-0.0283$) and persistence ($\Delta =+0.0105$) remain stable; the silhouette reduction (from 0.3809 to 0.3773) is a positive finding: it confirms that the spike-state component R3 was correctly identified as a compact, well-separated artefact cluster whose removal reveals the genuine regime structure. This dual validation establishes BSS-FM-ATR’s role as both a regime identifier and a data quality filter. The method isolates a Yahoo Finance data-contamination artefact ($n=21$, January–February 2025) through a principled spike-and-slab mechanism, providing an explicit posterior probability $p({\gamma }_3=0\mid \mathbf{X})>0.99$ of artefact status: a structural identifier that contaminated-normal and robust Bayesian alternatives cannot supply, establishing its value as both a regime identifier and a data quality filter for financial monitoring systems. Full article

This article analyses the transition from the space-charge-limited (SCL) regime to the inverse sheath regime of a thermionic emissive probe, considering ionisation and collisions as presheath mechanisms. We show that, before the fully developed inverse sheath mode is reached, an intermediate regime appears, characterised by the formation of a virtual cathode structure with ion confinement and probe potentials above the plasma potential. The inverse sheath mode is reached when the minimum potential of the virtual cathode reaches the plasma potential. At this point, a monotonic sheath potential profile above the plasma potential is produced, together with a flat, non-accelerating presheath. The findings of this work can help optimise the performance of emissive probes in plasma diagnostics and control plasma–surface interactions in devices such as fusion reactors and electric propulsion systems, thereby mitigating sputtering and material erosion. Full article

Additive manufacturing has evolved beyond monomaterial fabrication, enabling the integration of dissimilar polymers within a single structure to achieve spatially tailored properties. In Fused Filament Fabrication (FFF), however, the discrete, layer-wise deposition and inherent material incompatibilities make the interfacial region a critical determinant of structural integrity. Rather than acting as a simple boundary, the interface governs stress transfer, damage initiation, and failure propagation, especially in biodegradable polymer systems where thermal and rheological mismatches are pronounced. This study investigates bi-component FFF structures manufactured from PLA and PLA/PHA using mechanically interlocked interface geometries (T-type and dovetail configurations). Mechanical performance was assessed through tensile, flexural, and Charpy impact testing, complemented by fracture analysis, surface topography evaluation, and X-Ray Computed Tomography (XCT) for internal defect characterization. The results establish correlations between interface design, defect distribution, and overall structural response. Full article

Accurate pavement crack segmentation is essential for structural health monitoring, yet existing methods often face a trade-off between segmentation accuracy and computational efficiency. To address this issue, a novel teacher–student framework, termed RTCS-T and RTCS-S, is proposed. The teacher network RTCS-T is constructed based on the Swin Transformer to capture long-range dependencies and multi-scale contextual information. To further enhance crack representation, a strip refinement module is introduced to model directional structural features, while a cascaded atrous spatial pyramid pooling module is employed to improve multi-scale feature aggregation. Based on the teacher network, a lightweight student model RTCS-S is developed by using depthwise separable convolutions to achieve efficient inference. In addition, a foreground-aware and boundary-aware knowledge distillation strategy is introduced to guide the transfer of structural and contextual information from the teacher to the student. Experiments on the Crack500, DeepCrack, and CFD datasets demonstrated competitive performance against representative segmentation models. On CFD, RTCS-S achieved an F1 Score of 0.7514 and an mIoU of 0.7962. Notably, RTCS-S required only 1.82 M parameters and 1.13 GFLOPs and achieved a model inference speed of 680 FPS on an RTX 4090 GPU. When deployed on an RDK X5 edge-computing platform, the complete pipeline achieved an end-to-end throughput of 34 FPS, with an average latency of approximately 29.4 ms and peak memory consumption of 1.8 GB. These results demonstrate that the proposed framework provides an efficient solution for automated pavement crack detection and shows strong potential for practical road inspection applications. Full article

Background: Type 1 diabetes (T1D) is a complex autoimmune disease demonstrating substantial heterogeneity in age of onset, residual C-peptide levels, clinical outcomes, and therapeutic response. Although the autoimmune classification of T1D has traditionally relied on detection of autoantibodies indicating B-cell involvement, studies targeting total CD3⁺ T cells have underscored the importance of T-cell regulation. Th40 cells, a pathogenic subset of CD3⁺ T cells, first identified in NOD mice, become significantly increased during diabetogenesis. Human subjects with T1D exhibit variable but significantly elevated Th40 levels in peripheral blood. Methods: To target pathogenic effector Th40 cells, we developed OPT101, a 15-mer peptide, and found that it interacts with CD40 in association with an activated integrin, identifying a novel inflammatory receptor complex. We conducted a phase 1b, double-blind, first-in-human clinical trial to evaluate OPT101 and met the primary objectives of safety and tolerability. Results: OPT101 generated only Grade 1 and 2 adverse events. Across eight doses, administered over six weeks, no product-related immune suppression was observed. Secondary objectives included immunologic outcomes and potential efficacy. Subjects with higher Th40 levels had low or undetectable C-peptide, higher (>7.0%) HbA1c, and elevated inflammatory cytokines. Th40 levels were significantly higher in subjects diagnosed before age eighteen. OPT101 treatment significantly reduced Th40 percentages without cell ablation, increased Treg levels, and decreased inflammatory cytokines. Serum blood glucose levels and HbA1c were significantly reduced by visit 8 in treated subjects. In two subjects, 11 and 13 years post-diagnosis, with undetectable C-peptide at screening, C-peptide became detectable post-treatment. Conclusions: OPT101 proved safe and effective in human T1D subjects with only mild and a few moderate adverse events. In this short-term study, OPT101 improved beta cell functions thus warranting further exploration. Full article

Uncoordinated charging and discharging of large-scale electric vehicles (EVs) exacerbates grid peak–valley fluctuations, while deep discharging accelerates battery degradation. To address these challenges, this study proposes a coordinated charging–discharging optimization strategy integrating dynamic discharge restraint and a three-dimensional weighted comprehensive objective covering electricity price signals, grid operational constraints and battery health state. First, an EV travel behavior model is established to characterize spatiotemporal availability. Subsequently, a coupled battery aging model is developed by combining a power-law-based cycle aging formulation with a square-root calendar aging model, based on which an adaptive linkage mechanism between the depth-of-discharge upper bound and a net-revenue threshold is introduced. Building on these components, this model is constructed to jointly optimize three sub-objectives: charging station revenue maximization, battery lifetime cost minimization, and load fluctuation suppression, thereby mitigating grid peak–valley differences while reducing battery degradation and discharge costs. Multi-scenario simulations demonstrate that the proposed strategy, by coupling discharge restraint with spatiotemporal dynamic pricing, enables precise peak shaving of discharge power. For a fleet of 50 EVs, the charging station revenue reaches 1073.7 CNY, the grid peak–valley difference is reduced by 9.8%, and the battery degradation cost decreases by 23.1% compared with conventional strategies, corresponding to a carbon emission reduction of 386.4 tCO₂. When scaled to 100 EVs, the revenue increases by 101.9%, while the peak–valley difference is further reduced by 0.6%, demonstrating the effectiveness of the proposed strategy in enhancing economic performance, extending battery lifetime, and supporting grid stability. Full article

Preparing future educators for technology-enhanced learning environments has become increasingly important as artificial intelligence (AI) continues to influence teaching and learning. Guided by the Intelligent Technological Pedagogical Content Knowledge (i-TPACK) framework, this mixed-methods study examined preservice teachers’ perceptions of AI and the competencies they considered necessary for effective AI integration. Participants included 108 preservice teachers enrolled in a teacher preparation program at a public university in the southeastern United States. Data were collected using a survey containing Likert-scale and open-ended questions. Quantitative data were analyzed using descriptive statistics, exploratory factor analysis, and independent-samples t-tests, while qualitative responses were analyzed using thematic coding. The exploratory factor analysis identified a four-factor empirical structure that partially corresponded with the original theoretical domains, with varying levels of internal consistency. Preservice teachers generally viewed AI favorably and recognized its potential to support teaching and learning. Participants with internship experience reported significantly higher scores for perceived changes brought by AI and reasons for using AI than those without internship experience. Qualitative findings identified five competencies considered important for responsible AI integration: AI literacy, prompt engineering, critical evaluation, ethical AI use, and pedagogical balance. Participants also expressed concerns about academic dishonesty, misinformation, overreliance on AI, reduced critical thinking, and loss of human interaction. The findings highlight the importance of preparing preservice teachers to integrate AI in pedagogically meaningful and ethically responsible ways. Full article