Mesenchymal stem cell derived exosomes (MSC-Exos) demonstrate beneficial effects on wound healing via anti-inflammatory and angiogenic properties. Chitosan (CS) exhibits excellent biocompatibility and accelerates cellular migration, adhesion, and proliferation. The ions released from bioactive glass (BG) and titanium dioxide (TiO₂) nanoparticles exhibit sustained angiogenic and antibacterial potency. In this study, CMCS-CEBT hydrogel was synthesized from exosomes encapsulated carboxymethyl chitosan (CMCS), chitosan nanoparticles (CS-NPs), BG, and TiO₂ nanoparticles for a preliminary evaluation of its impacts on the treatment of full-thickness skin defects, diabetic wounds, and burn skin injury due to burns. In vitro analysis indicated that the hydrogel exhibits excellent cell compatibility, stimulates endothelial cell adhesion and proliferation, and presents anti-inflammatory, angiogenic, and antibacterial activities. In vivo, the composite hydrogel dressing accelerated a wound healing acceleration effect, stimulated angiogenesis, and increased collagen deposition and the expression of anti-inflammatory factors. This innovative composite hydrogel dressing as a potential clinical therapy, utilizing bioactive materials, holds promise as a potential clinical therapy that aims to facilitate the regeneration of acute and chronically damaged skin tissue.

The discrepant concordance between biopsy and radical prostatectomy (RP) specimen are well reported. To validate the clinical usefulness of neutrophil-lymphocyte ratio (NLR) in discriminating real GS ≥ 7 PCa from biopsy-based GS ≤ 6 PCa in comparison with serum total prostate-specific antigen (tPSA) and value of their combination. One hundred one patients who underwent physical examinations incidentally found elevated tPSA and subsequently received biopsy with a conclusion of GS ≤ 6 and RP with an interval of 4-6 weeks after biopsy were enrolled. NLR and tPSA were obtained within 15 days prior to biopsy. Logistic regression model was applied appropriately; McNemar tests and AUC model were performed to evaluate differences among tPSA, NLR and their combination and corresponding diagnostic power respectively. The pathological results from RP specimen comprised 61 patients with GS ≤ 6 and 100 patients with GS ≥ 7. Higher tPSA and NLR were significantly associated with patients with actual GS ≥ 7 (All P < 0.05) concurrently. Multivariate logistic regression indicated that tPSA (OR = 1.088, 95% C.I. = 1.029-1.151, P = 0.003) and NLR (OR = 1.807, 95% C.I. = 1.021-3.200, P = 0.042) could be independent predictors for GS groupings. Under cutoff value of 14.09 ng/ml for tPSA and 2.25 for NLR, the sensitivity, specificity and accuracy were 60.0%, 80.3% and 67.7% for tPSA, 42%, 88.5% and 59.6% for NLR, and 71.0%, 75.4% and 72.7% for combination of tPSA and NLR (tPSA + NLR) respectively. The sensitivity of tPSA + NLR was significantly higher in comparison with tPSA (P = 0.001) and NLR (P < 0.001). Except for sensitivity, no significant difference was found between tPSA and NLR in specificity (P = 0.227) and accuracy (P = 0.132). tPSA got the largest AUC with 0.732 (p < 0.001, 95% C.I.: 0.651-0.813). Serum tPSA and NLR were significantly elevated among GS ≥ 7 PCa concurrently. The combination of tPSA and NLR might have additional benefit to biopsy on discriminating real GS ≥ 7 Pca from biopsy-based GS ≤ 6 PCa. More stratification models and prospectively multicenter studies are necessary.

Background: Acute kidney injury (AKI) is a common and severe disease, which poses a global health burden with high morbidity and mortality. In recent years, ferroptosis has been recognized as being deeply related to Acute kidney injury. Our aim is to develop a diagnostic signature for Acute kidney injury based on ferroptosis-related genes (FRGs) through integrated bioinformatics analysis and machine learning.Methods: Our previously uploaded mouse Acute kidney injury dataset GSE192883 and another dataset, GSE153625, were downloaded to identify commonly expressed differentially expressed genes (coDEGs) through bioinformatic analysis. The FRGs were then overlapped with the coDEGs to identify differentially expressed FRGs (deFRGs). Immune cell infiltration was used to investigate immune cell dysregulation in Acute kidney injury. Functional enrichment analysis and protein-protein interaction network analysis were applied to identify candidate hub genes for Acute kidney injury. Then, receiver operator characteristic curve analysis and machine learning analysis (Lasso) were used to screen for diagnostic markers in two human datasets. Finally, these potential biomarkers were validated by quantitative real-time PCR in an Acute kidney injury model and across multiple datasets.Results: A total of 885 coDEGs and 33 deFRGs were commonly identified as differentially expressed in both GSE192883 and GSE153625 datasets. In cluster 1 of the coDEGs PPI network, we found a group of 20 genes clustered together with deFRGs, resulting in a total of 48 upregulated hub genes being identified. After ROC analysis, we discovered that 25 hub genes had an area under the curve (AUC) greater than 0.7; Lcn2, Plin2, and Atf3 all had AUCs over than this threshold in both human datasets GSE217427 and GSE139061. Through Lasso analysis, four hub genes (Lcn2, Atf3, Pir, and Mcm3) were screened for building a nomogram and evaluating diagnostic value. Finally, the expression of these four genes was validated in Acute kidney injury datasets and laboratory investigations, revealing that they may serve as ideal ferroptosis markers for Acute kidney injury.Conclusion: Four hub genes (Lcn2, Atf3, Pir, and Mcm3) were identified. After verification, the signature’s versatility was confirmed and a nomogram model based on these four genes effectively distinguished Acute kidney injury samples. Our findings provide critical insight into the progression of Acute kidney injury and can guide individualized diagnosis and treatment.