Macrophages preferentially polarize to the anti-inflammatory M2 subtype in response to alterations in the wound microenvironment. SUMO-specific protease 3 (SENP3), a SUMO-specific protease, has been proven to regulate inflammation in macrophages by deSUMOylating substrate proteins, but its contribution to wound healing is poorly defined. Here, we report that SENP3 deletion promotes M2 macrophage polarization and accelerates wound healing in macrophage-specific SENP3 knockout mice. Notably, it affects wound healing through the suppression of inflammation and promotion of angiogenesis and collagen remodeling. Mechanistically, we identified that SENP3 knockout facilitates M2 polarization through the Smad6/IκB/p65 signaling pathway. SENP3 knockout elevated the expression of Smad6 and IκB. Moreover, Smad6 silencing enhanced the expression of p-p65 and proinflammatory cytokines while inhibiting the level of IκB. Our study revealed the essential role of SENP3 in M2 polarization and wound healing, which offers a theoretical basis for further research and a therapeutic strategy for wound healing.

Type 17 immune responses are primarily mediated by Th17 cells and their effector cytokine interleukin-17 (IL-17), exerting a dual influence on wound healing. IL-17 plays a protective role during the initial stages of acute injury by facilitating rapid neutrophil recruitment, inducing antimicrobial peptide production and reinforcing pro-inflammatory signaling. However, sustained high signal of IL-17 results in a persistent inflammatory response that impairs keratinocyte proliferation and migration, angiogenesis, and nerve regeneration. This review elucidates the IL-17 signal effects and Th17 subset plasticity, which determines wound healing and skin barrier repair through their interactions with microbiota–immune, neuro–immune and metabolic reprogramming systems. Finally, we propose that the new therapeutic methods focus on IL-17 targets through precise spatiotemporal modulation and microenvironmental remodeling to create effective treatments for chronic non-healing wounds.

Objective: To provide surgical references for selecting appropriate parotidectomy incisions, reviewing modified approaches, incision designs, and associated complications. Methods: We have systematically searched 5 medical literature databases examining parotidectomy incision designs and postoperative complications from 2008 to 2021. Results: There are a total of 9 novel incision designs: 1) posterior auricular hairline incision (PAHI); 2) combined preauricular and retroauricular incision (CPRI); 3) V-shaped incision (VI); 4) N-shaped incision (NI); 5) postaural incision (PI); 6) preauricular crutch incision (PCI); and 7) endaural incision (EI). Simultaneously, there are a total of 8 postoperative complications: 1) infection; 2) salivary fistula; 3) facial nerve palsy/paresis; 4) ear lobule numbness; 5) Frey syndrome; 6) facial deformity; 7) hematoma; and 8) tumor reoccurrence. Conclusions: Over the last decade, a surge in modified parotidectomy incisions has been witnessed in clinical practice. This expansion is attributed to rapid technical advancements and a deeper understanding of anatomy and histopathology. These modified approaches contribute significantly to improving cosmetic outcomes, minimizing associated complications, and enhancing patient satisfaction.

Background: Hyperpigmentation is a skin disorder characterized by a localized darkening of the skin due to increased melanin production. When patients fail first line topical treatments, secondary treatments such as chemical peels and lasers are offered. However, these interventions are not devoid of risks and are associated with postinflammatory hyperpigmentation. In the quest for novel therapeutic potentials, this study aims to investigate computational methods in the identification of new targeted therapies in the treatment of hyperpigmentation. Methods: We used a comprehensive approach, which integrated text mining, interpreting gene lists through enrichment analysis and integration of diverse biological information (GeneCodis), protein-protein association networks and functional enrichment analyses (STRING), and plug-in network centrality parameters (Cytoscape) to pinpoint genes closely associated with hyperpigmentation. Subsequently, analysis of drug-gene interactions to identify potential drugs (Cortellis) was utilized to select drugs targeting these identified genes. Lastly, we used Deep Learning Based Drug Repurposing Toolkit (DeepPurpose) to conduct drug-target interaction predictions to ultimately identify candidate drugs with the most promising binding affinities. Results: Thirty-four hyperpigmentation-related genes were identified by text mining. Eight key genes were highlighted by utilizing GeneCodis, STRING, Cytoscape, gene enrichment, and protein-protein interaction analysis. Thirty-five drugs targeting hyperpigmentation-associated genes were identified by Cortellis, and 29 drugs, including 16 M2PK1 inhibitors, 11 KRAS inhibitors, and 2 BRAF inhibitors were recommended by DeepPurpose. Conclusions: The study highlights the promise of advanced computational methodology for identifying potential treatments for hyperpigmentation.

Background: Gynecomastia, caused by an estrogen–testosterone imbalance, affects males across various age groups. With unclear mechanisms and no approved drugs, the condition underscores the need for efficient, innovative treatment strategies. Methods: This study utilized deep learning-based computational methods to discover potential drug compounds for gynecomastia. To identify genes and pathways associated with gynecomastia, initial analyses included text mining, biological process exploration, pathway enrichment and protein–protein interaction (PPI) network construction. Subsequently, drug–target interactions (DTIs) were examined to identify potential therapeutic compounds. The DeepPurpose toolkit was employed to predict interactions between these candidate drugs and gene targets, prioritizing compounds based on their predicted binding affinities. Results: Text mining identified 177 genes associated with gynecomastia. Gene Ontology (GO) biological process and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses identified critical genes and pathways, with notable involvement in signal transduction, cell proliferation and steroid hormone biosynthesis. PPI network analysis highlighted 10 crucial genes, such as IGF1, TGFB1 and AR. DTI analysis and DeepPurpose predictions identified 12 potential drugs, including conteltinib, yifenidone and vosilasarm, with high predicted binding affinities to the target genes. Conclusions: The study successfully identified potential drug compounds for gynecomastia using a deep learning-based approach. The findings highlight the effectiveness of combining text mining and artificial intelligence in drug discovery. This innovative method provides a new avenue for developing specific treatments for gynecomastia and underscores the need for further experimental validation and optimization of prediction models to support novel drug development.