pH-Responsive Calcium Ions and Crocetin Releasing Hydrogel for Accelerating Skin Wound Healing.

The ideal and highly anticipated dressing for skin wounds should provide a moist environment, possess antibacterial properties, and ensure sustained drug release. In the present work, a hyaluronic acid-based hydrogel was formed by cross-linking crocetin and CaCO3@polyelectrolyte materials (CaCO3@PEM) microspheres with HA hydrogels via hydrogen bond and amido bonding (CaCO3@PEM@Cro@HA hydrogel, CPC@HA hydrogel). Moreover, the CPC@HA hydrogel had the capability of sustained, controlled release of calcium ions and crocetin via pH-sensitive and accelerated skin wound healing. The experiment results showed that the CPC@HA hydrogel exhibited porous network structures, stable physical properties, and had antibacterial properties and biocompatibility in vitro. In addition, the CPC@HA hydrogel covering on the skin wound could reduce inflammation and promote wound healing. The high expression of angiogenic cytokines (CD31) and epidermal terminal differentiation markers (Loricrin) of wound healing tissue suggested the CPC@HA hydrogel also had the function of promoting the remodeling of regenerated skin. Overall, CPC@HA hydrogel has promising potential for clinical applications in accelerating skin wound repair.

Development and Validation of a Machine Learning Prognostic Model of m5C Related immune Genes in Lung Adenocarcinoma.

BACKGROUND: The aim of this retrospective research was to develop an immune-related genes significantly associated with m5C methylation methylation (m5C-IRGs)-related signature associated with lung adenocarainoma (LUAD). METHODS: We introduced transcriptome data to screen out m5C-IRGs in The Cancer Genome Atlas (TCGA)-LUAD dataset. Subsequently, the m5C-IRGs associated with survival were certificated by Kaplan Meier (K-M) analysis. The univariate Cox, least absolute shrinkage and selection operator (LASSO) regression, and xgboost.surv tool were adopted to build a LUAD prognostic signature. We further conducted gene functional enrichment, immune microenvironment and immunotherapy analysis between 2 risk subgroups. Finally, we verified m5C-IRGs-related prognostic gene expression in transcription level. RESULTS: A total of 76 m5C-IRGs were identified in TCGA-LUAD dataset. Furthermore, 27 m5C-IRGs associated with survival were retained. Then, a m5C-IRGs prognostic signature was build based on the 3 prognostic genes (HLA-DMB, PPIA, and GPI). Independent prognostic analysis suggested that stage and RiskScore could be used as independent prognostic factors. We found that 4104 differentially expressed genes (DEGs) between the 2 risk subgroups were mainly concerned in immune receptor pathways. We found certain distinction in LUAD immune microenvironment between the 2 risk subgroups. Then, immunotherapy analysis and chemotherapeutic drug sensitivity results indicated that the m5C-IRGs-related gene signature might be applied as a therapy predictor. Finally, we found significant higher expression of PPIA and GPI in LUAD group compared to the normal group. CONCLUSIONS: The prognostic signature comprised of HLA-DMB, PPIA, and GPI based on m5C-IRGs was established, which might provide theoretical basis and reference value for the research of LUAD. PUBLIC DATASETS ANALYZED IN THE STUDY: TCGA-LUAD dataset was collected from the TCGA (https://portal.gdc.cancer.gov/) database, GSE31210 (validation set) was retrieved from GEO (https://www.ncbi.nlm.nih.gov/geo/) database.