Exploring the Efficacy of Catha Edulis Extract-Loaded Nanofibrous Scaffolds Seeded with Bone Marrow-Derived Stem Cells for Diabetic Wound Healing: A Preclinical Investigation
JOURNAL OF BIOACTIVE AND COMPATIBLE POLYMERS(2024)
Henan Univ Chinese Med
Abstract
Objectives: The aim of this study was to investigate the potential of incorporating catha edulis extract into polycaprolacton/gelatin scaffolds using electrospinning technique for the treatment of diabetic wounds in a rat model.Methods: The in vitro characterization of the scaffolds was performed using various assays, including anti-inflammatory assay, microstructure study, DPPH radical scavenging assay, cell viability assay, hemocompatibility assay, and bacterial penetration assays. The scaffolds were then seeded with bone marrow-derived stem cells and cultured before implantation into the rat model.Results: The results of the in vitro study showed that the produced scaffolds were nanofibrous, antioxidative, and non-toxic to skin cells. In vivo study demonstrated that the stem cell and catha edulis extract-loaded scaffolds had the highest rate of wound closure and histomorphometric parameters compared to other groups. Moreover, gene expression studies showed that the developed wound dressings increased the expression of VEGF gene and reduced the expression of glutathione peroxidase gene.Conclusion: These findings suggest that the catha edulis extract-loaded polycaprolacton/gelatin scaffolds could be a promising therapeutic option for diabetic wounds.
MoreTranslated text
Key words
Wound healing,stem cells,nanotechnology,nanofibers,diabetic wound
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined