This study is aimed at exploring the value of magnetic resonance diffusion-weighted imaging (DWI) combined with perfusion-weighted imaging (PWI) for diagnosing melanoma under a three-dimensional (3D) hybrid segmentation algorithm. 40 patients with melanoma were collected as research objects and subjected to magnetic resonance imaging (MRI) examination. A segmentation model was constructed and the original images were input. The noise contained in the images was preprocessed and normalized, and the mixed level set segmentation was performed after linear fusion of the images. Imaging findings were analyzed to find that the combined diagnosis of DWI and PWI with a 3D hybrid segmentation algorithm had the advantage of being clear and accurate. 10 primary cases were detected, which occurred in the cerebral meninges; 30 cases of metastases occurred inside the skull, mostly adjacent to the surface of the brain. The typical T1-weighted imaging (T1WI) and T2-weighted imaging (T2WI) of melanoma showed high signal and low signal, respectively, and the enhanced scan showed obvious enhancement. Atypical melanoma was manifested variously in MRI; a few had cystic necrosis, and an enhanced scan of the solid area revealed significant enhancement. Patients with multiple metastatic melanomas mainly showed low signal on DWI, and patients with primary or single metastatic melanoma mainly showed high signal or mixed high signal. Patients with perfusion imaging showed high perfusion on PWI. The 3D hybrid segmentation algorithm helped to improve the accuracy of DWI combined with PWI in the diagnosis of melanoma. This work provided a certain reference for the clinical diagnosis of melanoma.

Purpose: Burn is one of the most common injuries in clinical practice. The use of transcription factors (TFs) has been reported to reverse the epigenetic rewiring process and has great promise for skin regeneration. To better identify key TFs for skin reprogramming, we proposed a predictive system that conjoint analyzed gene expression data and regulatory network information. Methods: Firstly, the gene expression data in skin tissues were downloaded and the LIMMA package was used to identify differential-expressed genes (DEGs). Then three ways, including identification of TFs from the DEGs, enrichment analysis of TFs by a Fisher’s test, the direct and network-based influence degree analysis of TFs, were used to identify the key TFs related to skin regeneration. Finally, to obtain most comprehensive combination of TFs, the coverage extent of all the TFs were analyzed by Venn diagrams. Results: The top 30 TFs combinations with higher coverage were acquired. Especially, TFAP2A, ZEB1, and NFKB1 exerted greater regulatory influence on other DEGs in the local network and presented relatively higher degrees in the protein–protein interaction (PPI) networks. Conclusion: These TFs identification could give a deeper understanding of the molecular mechanism of cell trans-differentiation, and provide a reference for the skin regeneration and burn treatment.