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Bio inspired assessment of titanium-organic framework and exosome-constructed p-Synephrine carriage: pursuing the PI3K/mTOR pathway in a simulated periodontitis
Abstract Periodontitis, a chronic inflammatory disease, is driven by bacterial infection and oxidative stress, leading to tissue destruction and potential tooth loss. This study investigates the anti-inflammatory and antioxidant potential of p-Synephrine and enhanced delivery through NH 2− MIL-125 and exosomes derived from dental pulp stem cells (DPSCs). Primary Normal Human Gingival Keratinocytes (PCS) and Gingival Fibroblasts (HGF) were divided into eight groups, including controls, induction with LPS, and treatments with NH 2− MIL-125, exosomes, free p-Synephrine, p-Synephrine-loaded NH 2− MIL-125 (P-SYN-NH 2− MIL-125), p-Synephrine-loaded exosomes (P-SYN-Exo), and dexamethasone as a reference drug. Pro-inflammatory cytokines (IL-4, IL-6, TNF-α) and pathway markers (PI3K and mTOR) were quantified using ELISA kits, while antioxidant enzyme activities (GPx, SOD, and TAC) were assessed using colorimetric assays. Results showed that p-Synephrine loaded into NH 2− MIL-125 reduced inflammation markers and enhanced antioxidant defenses by increasingof GPx, SOD, and TAC concentrations. Among all treatments, p-Synephrine-loaded exosomes (P-SYN-Exo) demonstrated the most significant results, showing the highest increase in antioxidant markers GPx, SOD, and TAC, alongside a pronounced reduction in pro-inflammatory cytokines IL-4, IL-6, and TNF-α. Furthermore, p-Syn-Exo exhibited the most marked decrease in signaling pathway markers PI3K and mTOR. NH 2− MIL-125 and exosomes amplified these effects through controlled release and improved bioavailability, demonstrating superior reductions in TNF-α, IL-4, and IL-6 and increased antioxidative stress markers. These findings highlight p-Synephrine, particularly when delivered via NH 2− MIL-125 and exosomes, as a promising adjunctive treatment for periodontal inflammation and oxidative stress.
Outside Front Cover: A Synergistic Inhibitor Development Strategy Against Human UDP‐Galactose‐4‐Epimerase (Angew. Chem. Int. Ed. 22/2026)
Bearing capacity of inclined-loaded footings above dual tunnels in rock masses
FDA_YOLOv8: refined small object detection in unmanned aerial vehicle imagery
Abstract Small object detection (SOD) is essential for security monitoring in unmanned aerial vehicle (UAV) imagery. However, the inherently low effective resolution, weak semantic representation, and cluttered background of small objects pose significant challenges. Although deep learning methods have been widely applied to extract multi-scale features from UAV images, their performance remains limited by the small size of objects and complex scene variations. In addition, the constrained computational resources of UAV platforms make achieving both accuracy and efficiency in SOD even more challenging. In this study, we propose an efficient small-object detection method, called FDA_YOLOv8, which is developed based on the YOLOv8s baseline and is designed to accurately detect small objects in UAV images under low computational cost. First, a four-head detection architecture is designed by introducing an additional lightweight detection head to enhance the sensitivity of smaller objects. Second, the dynamic head (Dyhead) framework is integrated to improve the representation capability of the detection head. Third, a FasterNet block is embedded into the C2f module to form the C2f_FA architecture, which improves spatial feature extraction while reducing model complexity and computational cost. Furthermore, an efficient multi-scale attention (EMA) mechanism is incorporated into the C2f_FA module, yielding the C2f_FE structure for better feature discrimination. Experiments on the VisDrone2019 dataset show that FDA_YOLOv8 achieves an mAP of 45.8%, outperforming YOLOv8s by 5.2%, confirming its effectiveness in refined small object detection for UAV imagery.
Development and evaluation of a propolis, tea tree oil, and jojoba oil nanoemulgel with enhanced antioxidant, anti-inflammatory, and wound-healing activities
Abstract The wound healing’s acceleration depends on developing an innovative therapeutic strategy that reduces inflammation, regulates oxidative stress, promotes fibroblast proliferation, and collagen deposition. This study aimed to prepare and evaluate a green nanoemulsion-based formulation containing propolis, tea tree, and jojoba oils to integrate their biological activities for enhanced wound healing efficacy. Nanoemulsion formulations were prepared via a low-energy emulsification approach and characterized by zeta potential, hydrodynamic size (DLS), polydispersity index (PDI), and transmission electron microscopy (TEM). The in vitro antioxidant and anti-inflammatory activities were assessed. In addition, cytotoxicity was assessed using the MTT colorimetric assay against the HSF-1 normal skin cell line. Additionally, NO and IL-1β levels were measured on NHDF-Ad cells. A propolis nanoemulgel (PNEG) was produced using Carbopol 940 as a gelling agent. The in vivo wound healing efficacy of PNEG was tested by evaluating wound closure rate, malondialdehyde, superoxide dismutase, tumor necrosis factor-alpha, and histopathological alterations in healed tissues. The obtained results demonstrated that, the optimized formula displayed a zeta potential of − 28.6 ± 1.91 mV, a DLS of 187.8 ± 3.29 nm, and a 50% cytotoxic concentration (CC 50 ) of 24.82 ± 0.16% and showed a significant antioxidant and anti-inflammatory activities. Furthermore, NO and IL-1β levels were significantly decreased by 35% and 40%, respectively, demonstrating potent anti-inflammatory activity. Additionally, the therapeutic activity was associated with inhibition of IL-1β /NO-driven inflammatory signaling, suppression of NF-κB , activation of the NRF2/HO-1 antioxidant axis, and modulation of BAX/Bcl-2 apoptotic pathways. The in vivo results indicated that PNEG accelerates wound closure significantly compared with both positive and negative control groups. SOD level increased significantly ( p < 0.05), whereas MDA and TNF-α levels decreased compared with the control groups ( p < 0.05). Furthermore, histopathological examination illustrated infiltration reduction of inflammatory cells, increased collagen deposition, and fast epithelialization. The developed green formula, resulting from both the low-energy, environmentally friendly preparation method and the natural origin of its constituents, was demonstrated to be an antioxidant and inflammatory and to accelerate wound healing, suggesting it could be used as a natural formula for topical wound healing application.
Inside Front Cover: Multicyclic D‐Stereospecific Hydrolase Dimer With High Sustained Activity (Angew. Chem. Int. Ed. 22/2026)
Comparative analysis and influential factors of embodied carbon emissions across low-rise, multi-story, and high-rise residential buildings in China
Abstract Embodied carbon emission reduction is crucial for promoting energy conservation and carbon reduction in residential buildings as operational carbon emissions decline. While previous studies have examined the influence of various building characteristics on embodied carbon emissions, the underlying factors responsible for the observed variations in emissions under different characteristics remain insufficiently explored. Using a dataset of 538 residential buildings in China, this study statistically analyzed the embodied carbon intensities of low-rise, multi-story, and high-rise buildings, with average values of 399.2, 442.9, and 450.1 kgCO 2e /m 2 , respectively. Correlation analysis and significance tests were then used to examine differences in embodied carbon emissions across different characteristic categories. The results revealed that structural form and seismic fortification intensity primarily affected the embodied carbon intensity of structural materials, whereas delivery type was mainly associated with that of decorative materials. The proposed influence coefficients further indicated that concrete was the dominant factor to differences in embodied carbon intensity associated with structural forms and seismic fortification intensity in low-rise and high-rise buildings, whereas steel made a greater contribution in multi-story buildings. Moreover, the analysis of potential carbon reduction measures indicated that low-rise and multi-story buildings with frame structures exhibited the highest reduction potential, with reductions of 13.9% and 15.2%, respectively, achievable through material consumption optimization and transport distance reduction. However, in high-rise buildings, shear wall structures exhibited the highest reduction potential (12.5%). This study provides practical insights into optimizing residential building design from a low-carbon perspective.
Heavy metal concentrations and assessment of health risk attributed to the consumption of wheat
Correction to “Emergent Chemical Reactivity and Complexity of RNA Condensates”
Exploring multi-feedstock biodiesel–hydrogen synergies for enhanced diesel engine performance using hybrid AI techniques
Seepage analysis of tunnel lining under the synergistic effect of cave-fracture networks in water-rich karst formations
The mediating role of partner support in the relationship between reproductive health concerns and psychological distress among cancer survivors
Abstract Cancer survivorship often includes reproductive health concerns (e.g., fertility potential and treatment‐related health problems) that elevate psychological distress. Partner support is a key, modifiable resource linked to better adjustment, yet its pathway of influence is not fully understood. To test whether partner support mediates and/or moderates the association between reproductive health concerns and psychological distress among female cancer survivors. A cross-sectional study was conducted with 202 married female cancer survivors (ages 20–49) attending the oncology departments of Tanta University Hospital, Egypt. Validated measures included the Reproductive Concerns after Cancer Scale, Partner Support Scale, and Kessler Psychological Distress Scale. Higher reproductive concerns related to greater distress (r = 0.517, p < 0.01). Partner support correlated negatively with reproductive concerns (r = − 0.446, p < 0.01) and distress (r = − 0.471, p < 0.01). Adding partner support improved prediction of distress (Model 2, R 2 = 0.345); partner support was a significant negative predictor (B = − 0.364, p < 0.001). Mediation was supported: Reproductive Concerns after Cancer Scale → distress total effect B = 0.4212 ( p < 0.001); direct effect B = 0.3124 ( p < 0.001); indirect effect via partner support B = 0.1088, 95% CI [0.0617, 0.1613]. Moderation was not supported (interaction B≈0.000, p = 0.998). Conclusions: Reproductive concerns are associated with elevated psychological distress. Partner support partially mediates yet does not moderate this relationship, indicating a protective, indirect effect on distress. Implications for Practice: Integrate structured partner‐involved education and counselling into survivorship care to reduce distress linked to reproductive concerns (confirmed need). Screen routinely for reproductive concerns and perceived partner support and refer dyads to targeted psychosocial interventions. Training for nurses should prioritize communication about fertility/health concerns and techniques to engage partners effectively.
Productivity evaluation, multi-scale characterisation, and safety assessment of biochar derived from green and woody agricultural biomass in Qatar
Multi-level evaluation of zinc sulfate toxicity and its modulation by Helichrysum arenarium in Allium cepa
Knowledge and attitudes regarding cervical cancer among adult Kuwaiti women: A cross-sectional study
Proactive soft-failure prediction in optical transport networks via physics-inspired features and Infrastructure-as-Code orchestration
Abstract Optical transport networks rely on reactive fault management, which guarantees service disruption during the onset of soft failures. We present a framework for proactive soft-failure prediction that combines physics-inspired feature engineering, tree-ensemble machine learning, and Infrastructure-as-Code (IaC) orchestration. The framework is validated on (i) a multi-physics stochastic simulation spanning five degradation modes (Ornstein–Uhlenbeck, exponential, Weibull, step, oscillatory) and (ii) a publicly available real optical telemetry benchmark (Ghosh & Adhya, 2025) comprising 756 lightpaths $$\times$$ 4 failure classes $$\times$$ 900 samples (2.72 M records). A Random Forest regressor augmented with velocity, acceleration, and rolling-statistic features predicts time-to-failure with 17.9 s mean absolute error (MAE) on synthetic test data and 73.2 $$\,\pm \,$$ 0.03 s MAE (95% CI, $$n{=}10$$ seeds) on the real benchmark, outperforming heuristic baselines by 6 $$\times$$ and matching a tuned XGBoost ( $$72.7\pm 0.05$$ s) while surpassing LSTM and 1D-CNN sequence models trained under identical conditions. A trajectory-level train/test split eliminates temporal leakage. SHapley Additive exPlanations (SHAP) applied to four operational case studies (EDFA-aging, NLI-accelerating, stable-link, and false-alarm trajectories) show that alarm decisions are driven primarily by current OSNR, rolling-window statistics, and velocity, yielding interpretable diagnostics at the moment of alert. An end-to-end latency budget of the proposed IaC pipeline, measured stage-by-stage, totals 6.7 s mean wall-clock, dominated by Kubernetes reconciliation and Terraform apply; machine-learning inference contributes < 0.5%. The framework is scoped to gradual OSNR-degrading failures (EDFA pump aging, nonlinear-interference drift); extension to laser-current-visible ECL failures through multi-channel feature fusion is identified as future work.