Background The biological role of miR-203 and the underlying mechanisms on the proliferation of epidermal stem cells (ESCs) have not yet been reported during the progression of chronic wound healing in diabetes mellitus. Our previous studies have observed that the expression of miR-203 showed a marked upregulation and ESC proliferation capacity was impaired in diabetes mellitus skin wounds in rats. Methods Wound models were established in normal rats and rats with type 2 diabetes. Expression level of miR-203 and the alteration of ESCs’ number and function were detected. ESCs were isolated from the back skin of fetal rats to assess the effects of glucose in vitro. An antagomir to miR-203 was used to assess its effect on ESCs. Using microarray analysis, we further identified potential target genes and signaling pathways of miR-203. Results We found that high glucose significantly upregulated the expression of miR-203 and subsequently reduced the number of ESCs and impaired their proliferation capacity. Meanwhile, over-expression of miR-203 reduced the ESCs’ numbers and impaired the proliferation capacity via downregulation of the Notch and Wnt signaling pathways. Conversely, inhibition of miR-203 enhanced the proliferation capacity. Additionally, silencing miR-203 in skin of rats with type 2 diabetes accelerated wound healing and improved healing quality via the upregulation of the Notch and Wnt signaling pathways. Finally, over-expression of miR-203 downregulated genes ROCK2, MAPK8, MAPK9, and PRKCA. Conclusion Our findings demonstrated that induced expression of miR-203 by high glucose in type 2 diabetic rats decreased the number of ESCs and impaired ESC proliferation capacity via downregulating genes related to Notch and Wnt signaling pathways, resulting in a delayed wound healing.

Burn infection delays wound healing and increases the burn patient mortality. Consequently, a new dressing with antibacterial and anti‐inflammatory dual properties is urgently required for wound healing. In this study, we propose a combination of methacrylate gelatin (GelMA) hydrogel system with silver nanoparticles embed in γ‐cyclodextrin metal–organic frameworks (Ag@MOF) and hyaluronic acid‐epigallocatechin gallate (HA‐E) for the burn wound infection treatment. Ag@MOF is used as an antibacterial agent and epigallocatechin gallate (EGCG) has exhibited biological properties of anti‐inflammation and antibacterial. The GelMA/HA‐E/Ag@MOF hydrogel enjoys suitable physical properties and sustained release of Ag + . Meanwhile, the hydrogel has excellent biocompatibility and could promote macrophage polarization from M1 to M2. In vivo wound healing evaluations further demonstrate that the GelMA/HA‐E/Ag@MOF hydrogel reduces the number of the bacterium efficiently, accelerates wound healing, promotes early angiogenesis, and regulates immune reaction. A further evaluation indicates that the noncanonical Wnt signal pathway is significantly activated in the GelMA/HA‐E/Ag@MOF hydrogel treated group. In conclusion, the GelMA/HA‐E/Ag@MOF hydrogel could serve as a promising multifunctional dressing for the burn wound healing.

BackgroundStem cells are a group of cells that can self-renew and have multiple differentiation capabilities. Shinya Yamanaka first discovered a method to convert somatic cells into pluripotent stem cells in 2006. Stem cell therapy can be summarized into three aspects (regenerative treatment, therapy targeted at stem cells, and establishment of disease models). Disease models are mainly established by induced pluripotent stem cells, and the research of stem cell precision medicine has been promising in recent years. Based on the construction of 3D, patient-specific disease models from pluripotent induced stem cells, proper research on disease development and treatment prognosis can be realized. Bibliometric analysis is an efficient way to quickly understand global trends and hotspots in this field.MethodsA literature search of stem cell precision medicine research from 2018 to 2022 was carried out using the Web of Science Core Collection.VOSviewer, R-bibliometrix, and CiteSpace software programs were employed to perform the bibliometric analysis.ResultsA total of 552 publications were retrieved from 2018 to 2022. Annual publication outputs trended upward and reached a peak of 172 in 2021. The United States contributed the most publications (160, 29.0%) to the field, followed by China (63, 11.4%) and Italy (60, 10.9%). International academic collaborations were active. CANCERS was considered the most productive journal with 18 documents. NATURE was the most co-cited journal with 1860 times citations. The most cited document was entitled “Induced Pluripotent Stem Cells for Cardiovascular Disease Modeling and Precision Medicine: A Scientific Statement From the American Heart Association” with 9 times local citations. “ precision medicine” (n = 89, 12.64%), “personalized medicine” (n = 72, 10.23%), “stem cells” (n = 43, 4.40%), and “induced pluripotent stem cells” (n = 41, 5.82%), “cancer stem cells” (n = 31, 4%), “organoids” (n = 26, 3.69%) were the top 6 frequent keywords.ConclusionThe present study performs a comprehensive investigation concerning stem cell precision medicine (2018–2022) for the first time. This research field is developing, and a deeper exploration of 3D patient-specific organoid disease models is worth more research in the future.

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Background: Dermatofibrosarcoma protuberans (DFSP) is a rare mesenchymal tumor which is primarily treated with surgery. Targeted therapy is a promising approach to help reduce the high rate of recurrence. This study aims to identify the potential target genes and explore the candidate drugs acting on them effectively with computational methods. Methods: Identification of genes associated with DFSP was conducted using the text mining tool pubmed2ensembl. Further gene screening was carried out by conducting Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Protein-Protein Interaction (PPI) network was constructed by using the Search Tools for the Retrieval of Interacting (STRING) database and visualized in Cytoscape. The gene candidates were identified after a literature review. Drugs targeting these genes were selected in Pharmaprojects. The binding affinity scores of Drug-Target Interaction (DTI) were predicted by a deep learning algorithm DeepPurpose. Results: A total of 121 genes were found associated with DFSP by text mining. The top 3 statistically functional enriched pathways of GO and KEGG analysis included 36 genes, and 18 hub genes were further screened out by constructing a PPI networking and literature retrieval. 42 candidate drugs targeted at hub genes were found by Pharmaprojects under our restrictions. Finally, 10 drugs with top affinity scores were predicted by DeepPurpose, included 3 platelet-derived growth factor receptor beta kinase (PDGFRB) inhibitors, 2 platelet-derived growth factor receptor alpha kinase (PDGFRA) inhibitors, 2 Erb-B2 receptor tyrosine kinase 2 (ErbB-2) inhibitor, 1 tumor protein p53 (TP53) stimulant, 1 vascular endothelial growth factor receptor (VEGFR) antagonist and 1 prostaglandin-endoperoxide synthase 2 (PTGS2) inhibitor. Conclusion: Conclusion: Text mining and bioinformatics are useful methods for genes identification in drug discovery. DeepPurpose is an efficient and operative deep learning tool for predicting the DTI and selecting the drug candidates.

Background. Melanomas are skin malignant tumors that arise from melanocytes which are primarily treated with surgery, chemotherapy, targeted therapy, immunotherapy, radiation therapy, etc. Targeted therapy is a promising approach to treating advanced melanomas, but resistance always occurs. This study is aimed at identifying the potential target genes and candidate drugs for drug-resistant melanoma effectively with computational methods. Methods. Identification of genes associated with drug-resistant melanomas was conducted using the text mining tool pubmed2ensembl. Further gene screening was carried out by GO and KEGG pathway enrichment analyses. The PPI network was constructed using STRING database and Cytoscape. GEPIA was used to perform the survival analysis and conduct the Kaplan-Meier curve. Drugs targeted at these genes were selected in Pharmaprojects. The binding affinity scores of drug-target interactions were predicted by DeepPurpose. Results. A total of 433 genes were found associated with drug-resistant melanomas by text mining. The most statistically differential functional enriched pathways of GO and KEGG analyses contained 348 genes, and 27 hub genes were further screened out by MCODE in Cytoscape. Six genes were identified with statistical differences after survival analysis and literature review. 16 candidate drugs targeted at hub genes were found by Pharmaprojects under our restrictions. Finally, 11 ERBB2-targeted drugs with top affinity scores were predicted by DeepPurpose, including 10 ERBB2 kinase inhibitors and 1 antibody-drug conjugate. Conclusion. Text mining and bioinformatics are valuable methods for gene identification in drug discovery. DeepPurpose is an efficient and operative deep learning tool for predicting the DTI and selecting the candidate drugs.