N,N-Dimethylglycine (DMG) is a glycine derivative, and its sodium salt (DMG-Na) has been demonstrated to possess various biological activities, including immunomodulation, free radical scavenging, and antioxidation, collectively contributing to the stability of tissue and cellular functions. However, its direct effects and underlying mechanisms in wound healing remain unclear. In this study, a full-thickness excisional wound model was established on the dorsal skin of mice, and wounds were treated locally with DMG-Na. Wound healing progression was assessed by calculating wound closure rates. Histopathological analysis was conducted using hematoxylin-eosin (HE) staining, and keratinocyte proliferation, migration, and differentiation were evaluated using CCK-8 assays, scratch wound assays, and quantitative reverse transcription PCR (qRT-PCR). Inflammation-related cytokine expression in keratinocytes was analyzed via ELISA and qRT-PCR. Results revealed that DMG-Na treatment significantly accelerated wound healing in mice and improved overall wound closure quality. The wound healing rates on days 3, 6, and 9 were 49.18%, 68.87%, and 90.55%, respectively, with statistically significant differences compared to the control group (P<0.05). DMG-Na treatment downregulated the mRNA levels of keratinocyte differentiation markers while enhancing cell proliferation and migration (P<0.05). Furthermore, DMG-Na decreased the secretion of LPS-induced keratinocyte inflammatory cytokines, including IL-1β, IL-6, IL-8, TNF-α, and CXCL10 (P<0.05). These findings indicate that DMG-Na regulates inflammatory responses and promotes keratinocyte proliferation and migration, thereby facilitating the healing of skin wounds.
The macrotrabecular-massive (MTM) subtype of hepatocellular carcinoma (HCC) is a histological variant with higher malignant potential. Non-invasive preoperative identification of MTM-HCC is crucial for precise treatment. Current radiomics-based diagnostic models often integrate multi-phase features by simple feature concatenation, which may inadequately explore the latent complementary information between phases. This study proposes a feature fusion-based radiomics model using multi-phase contrast-enhanced computed tomography (mpCECT) images. Features were extracted from the arterial phase (AP), portal venous phase (PVP), and delayed phase (DP) CT images of 121 HCC patients. The fusion model was constructed and compared against the traditional concatenation model. Five-fold cross-validation demonstrated that the feature fusion model combining AP and PVP features achieved the best classification performance, with an area under the receiver operating characteristic curve (AUC) of 0.839. Furthermore, for any combination of two phases, the feature fusion model consistently outperformed the traditional feature concatenation approach. In conclusion, the proposed feature fusion model effectively enhances the discrimination capability compared to traditional models, providing a new tool for clinical practice.