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The effects of platelet rich plasma and zinc oxide nanoparticle on skin wound healing in dogs
Abstract Wound healing is a complicated process, so it’s critical to identify efficient ways to hasten recovery. Platelet-rich plasma (PRP) and zinc oxide nanoparticles (ZnO NPs) have demonstrated potential in improving cutaneous wound healing in a variety of species. But little is known about their combined effects, especially in dogs. Therefore, this study determined how topical infiltration of PRP and ZnO NPs ointment, both separately and in combination, affect the healing of dogs’ cutaneous wounds. Thirty-six full skin wounds were induced in the chest of six adult mongrel dogs. These wounds were randomly divided into six equal groups (6 wounds each) according to treatment protocol: group 1 served as a control and the wounds were dressed daily with normal saline only, group 2: the wounds were dressed daily with lanolin only, group 3: the wounds were infiltrated once with PRP, group 4: the wounds were treated with PRP single infiltration combined with lanolin ointment daily dressing, group 5: the wounds were dressed daily with ZnO NPs ointment, and group 6: the wounds were infiltrated once with PRP and daily dressed with ZnO NPs ointment. Wound healing progress was monitored; epithelialization, wound contraction, and overall healing were assessed. Total antioxidant capacity (TAC), malondialdehyde (MDA) and the concentration of platelets derived growth factor beta (PDGFβ) were measured on wound fluid. Gene expression of matrix extracellular phosphoglycoprotien (MEPE), transforming growth factor beta (TGF-β) and tumor necrosis factor alpha (TNF-α) were also evaluated on skin biopsies at day 0, 5, 10 and 20. Histopathology, immunohistochemistry and staining of collagen bundles were performed on skin biopsies at 5, 10 and 20 days of wound induction. All data were statistically analyzed. There was a significant interaction between the group and time across all parameters ( P < 0.001). The PRP–ZnO NPs group consistently has a great effect on wound size reduction, contraction, healing, epithelialization, and antioxidant activity, along with higher MEPE and PDGFβ expression and arranged parallel collagen bundles, indicating enhanced regeneration. While PRP alone showed the strongest TGF-β increase and anti-inflammatory effect (lowest TNF-α). PRP–ZnO NPs provided the best overall balance between regeneration and inflammation control. All treatments surpassed the control and lanolin groups, which showed minimal improvement. PRP–Lanolin and ZnO NPs offered moderate benefits but were less effective than PRP–ZnO NPs or PRP. ZnO NPs and PRP work together to improve skin wound healing in dogs; PRP promotes regenerative signaling, while ZnO NPs reduce oxidative stress and microbial load.
Bond–electromagnetic origin of superconducting pairing across materials
A 19-layer convolutional neural network for accurate COVID-19 detection in chest X-ray images: comparative analysis with pretrained networks
Near real-time b -value analysis for volcano traffic light alert systems and eruption forecasting
Speed-feature-based engine stop-position control for hybrid electric vehicles
Multi-scale mechanistic evaluation of reactivity balance in metakaolin-nano-CaCO3 systems for high-performance stabilization of problematic clays
Presence and associations of retinal pigment epithelium and outer retinal atrophy: the Beijing eye study
Predicting diffusion-FLAIR mismatch from B1000 and ADC without FLAIR: A deep learning-based approach
Abstract Diffusion-FLAIR mismatch (DFM), defined as the discrepancy between diffusion-weighted imaging (DWI) and FLAIR sequences, is a key imaging biomarker used to identify patients with unclear symptom onset or those likely to benefit from recanalization therapy. However, FLAIR imaging is often limited in acute clinical settings due to time constraints, patient instability, or scanner availability. Therefore, alternative approaches to assess DFM without relying on FLAIR are needed. This study aimed to develop a deep learning model that predicts DFM using only B1000 and apparent diffusion coefficient (ADC) images, and to evaluate its performance. This study was conducted using multicenter stroke registry-based cohorts. After excluding cases not suitable for analysis, 2,369 cases were included in the derivation cohort for model development, and 679 cases from two independent stroke centers were included as external validation cohorts. Model performance was assessed based on two binary classification schemes. The Broad classification included all non-match cases, comprising both clearly defined mismatches and ambiguous cases with subtle or focal FLAIR changes, while the Focused classification excluded such ambiguous cases, including only clearly defined match and mismatch instances. In the Focused classification setting, the AI model achieved an AUROC of 0.92 (95% CI: 0.88–0.95) on the external validation dataset, significantly outperforming the average AUROC of human experts, which was 0.82 (95% CI: 0.77–0.87) ( p < 0.001, AUROC difference 0.10). Overall, the Focused classification yielded higher performance than the Broad classification, and this trend was consistent for both the AI model and human readers. We developed a deep learning-based classifier capable of predicting DFM using only B1000 and ADC images, without requiring FLAIR. The model outperformed human experts and demonstrates potential as a complementary diagnostic tool in acute stroke settings where FLAIR imaging is unavailable, supporting its future integration into decision-support systems.
Depression and general self-efficacy among first-generation college students in China: a longitudinal analysis
FAM111B may promote the progression of lung squamous cell carcinoma through PI3K signaling pathway
Machine-learned dimethyl sulphide (DMS) for the North Atlantic (2002–2024) to support movement studies
Abstract Dimethyl sulphide (DMS) serves as a key olfactory cue for seabird navigation, yet existing DMS products operate at coarse spatiotemporal resolutions (≥ 25 km, monthly) mismatched to the scales of individual movement decisions. We ask (I) whether machine learning can produce biologically relevant, high-resolution DMS estimates across the North Atlantic, and (ii) whether such estimates can be readily integrated with animal tracking data to support ecological interpretation of animal trajectories. Using North Atlantic in-situ DMS observations (2002–2024) and five satellite-data-based environmental predictors (chlorophyll, mixed layer depth, nitrate, sea-surface temperature, and photosynthetically available radiation), we developed a machine-learning-based ensemble model for DMS prediction that achieved strong accuracy (test R 2 = 0.88; RMSE = 0.859 µmol m −3 ), exceeding previously reported performance for basin-scale DMS mapping. To identify the key drivers of the model predictions, we conducted SHAP (SHapley Additive exPlanations) analysis, which revealed that mixed layer depth, nitrate concentration, and chlorophyll were the dominant controlling factors, aligning with established understanding of DMS biogeochemistry. We then produced a spatially continuous, daily 4 km DMS dataset for the North Atlantic domain (0–60° N, 80° W–15° E), revealing seasonal cycles, persistent hotspots, and fine-scale gradients not captured by coarse climatologies. Finally, we develop AniDMS, an open-source Python package that automates trajectory annotation with gridded DMS and associated covariates, demonstrated with a Manx shearwater case study. Together, the dataset and the tool enable scalable, hypothesis-driven tests of olfactory navigation of seabirds and provide a transferable framework for integrating high-resolution environmental context into movement ecology.
Intrathecal amylin reverses morphine tolerance through site-specific DNA methylation of Pdyn and Bdnf promoters in rats
Vulnerability of fishery resources to climate change in the Tropical Eastern Pacific Ecosystem off Peru
Dynamic staff scheduling optimization algorithm for hotel management
Saliva-based monitoring of chronic kidney disease: comparative evaluation of three collection methods for creatinine, urea, calcium, and PTH
Effects of personality steering on cooperative behavior in large language model agents
Abstract Large language models (LLMs) are increasingly used as autonomous agents in strategic and social interactions. Although recent studies suggest that assigning personality traits to LLMs can influence their behavior, how personality steering affects cooperation under controlled conditions remains unclear. In this study, we examine the effects of personality steering on cooperative behavior in LLM agents using repeated Prisoner’s Dilemma games. Based on the Big Five framework, we first measure basic personality scores of three models, GPT-3.5-turbo, GPT-4o, and GPT-5, using the Big Five Inventory. We then compare behavior under baseline and personality-informed conditions, and further analyze the effects of independently manipulating each personality dimension to extreme values. Our results show that agreeableness is the dominant factor promoting cooperation across all models, while other personality traits have limited impact. Explicit personality information increases cooperation but can also raise vulnerability to exploitation, particularly in earlier-generation models. In contrast, later-generation models exhibit more selective cooperation. These findings indicate that personality steering acts as a behavioral bias rather than a deterministic control mechanism.