Role of mammary serine protease inhibitor on the inflammatory response in oral lichen planus

Research and Application of Deep Learning Models with Multi-Scale Feature Fusion for Lesion Segmentation in Oral Mucosal Diseases

Given the complexity of oral mucosal disease diagnosis and the limitations in the precision of traditional object detection methods, this study aims to develop a high-accuracy artificial intelligence-assisted diagnostic approach based on the SegFormer semantic segmentation model. This method is designed to automatically segment lesion areas in white-light images of oral mucosal diseases, providing objective and quantifiable evidence for clinical diagnosis. This study utilized a dataset of oral mucosal diseases provided by the Affiliated Stomatological Hospital of Zhejiang University School of Medicine, comprising 838 high-resolution images of three diseases: oral lichen planus, oral leukoplakia, and oral submucous fibrosis. These images were annotated at the pixel level by oral specialists using Labelme software (v5.5.0) to construct a semantic segmentation dataset. This study designed a SegFormer model based on the Transformer architecture, employed cross-validation to divide training and testing sets, and compared SegFormer models of different capacities with classical segmentation models such as UNet and DeepLabV3. Quantitative metrics including the Dice coefficient and mIoU were evaluated, and a qualitative visual analysis of the segmentation results was performed to comprehensively assess model performance. The SegFormer-B2 model achieved optimal performance on the test set, with a Dice coefficient of 0.710 and mIoU of 0.786, significantly outperforming other comparative algorithms. The visual results demonstrate that this model could accurately segment the lesion areas of three common oral mucosal diseases. The SegFormer model proposed in this study effectively achieves the precise automatic segmentation of three common oral mucosal diseases, providing a reliable auxiliary tool for clinical diagnosis. It shows promising prospects in improving the efficiency and accuracy of oral mucosal disease diagnosis and has potential clinical application value.

Genetic and therapeutic for oral lichen planus and diabetes mellitus: a comprehensive study

BACKGROUND

This study employed a bidirectional Mendelian Randomization (MR) approach to explore the causal relationships between Oral Lichen Planus (OLP), diabetes mellitus (DM), and glycemic control. It also aims to identify potential pharmacological and herbal treatments that effectively address both OLP and the metabolic dysfunctions associated with DM.

METHODS

This study employs a two-way MR approach to investigate the potential causal relationships between diabetes type and glycated hemoglobin (HbA1c) levels, and the risk of OLP. We analyzed differentially expressed genes from the OLP dataset in the Genomics Expression Omnibus (GEO) database, cross-referencing these with HbA1c-related genes for enrichment analysis. Additionally, the Drug-Gene Interaction Database (DGIdb) and Traditional Chinese Medicine Systems Pharmacology Database (TCMSP) were utilized to assess the effectiveness of specific drugs, herbs, and ingredients in treating OLP while managing blood glucose levels.

RESULTS

The MR analysis revealed a significant association between Type 1 Diabetes mellitus (T1DM) and an increased risk of OLP, unlike Type 2 Diabetes mellitus (T2DM). This finding indicates a unique immunological interaction in T1DM that may predispose individuals to OLP. The drug prediction analysis focused on core targets linked to OLP and HbA1c, evaluating the therapeutic potential of retinoic acid, prednisone, and thalidomide for treating OLP and regulating blood glucose levels. Additionally, herbal medicines such as Ecliptae herbaand Amygdalus communis vas, along with herbal ingredients like quercetin, luteolin, and 17-beta-estradiol, were identified for their anti-inflammatory properties and potential to mitigate metabolic dysfunction in diabetes.

CONCLUSION

The study highlighted a complex interplay between diabetes and OLP, underscoring the efficacy of integrated therapeutic strategies that target both conditions. The findings suggest that both pharmaceutical and herbal treatments can effectively manage the clinical manifestations of OLP and associated metabolic challenges. This holistic approach to treatment could significantly enhance patient outcomes by addressing the interconnected aspects of these chronic conditions.

Smoking behavior associated upregulation of SERPINB12 promotes proliferation and metastasis via activating WNT signaling in NSCLC

BACKGROUND

Non-small cell lung cancer (NSCLC) is the leading cause of morality among all malignant tumors. Smoking is one of the most important causes of NSCLC, which contributes not only to the initiation of NSCLC but also to its progression. The identification of specific biomarkers associated with smoking will promote diagnosis and treatment.

METHODS

Data mining was used to identify the smoking associated gene SERPINB12. CCK8 assays, colony formation assays, a mouse xenograft model and transwell assays were performed to measure the biological functions of SERPINB12 in NSCLC. GSEA, luciferase reporter assays and immunofluorescence were conducted to explore the potential molecular mechanisms of SERPINB12 in NSCLC.

RESULTS

In this study, by data mining the TCGA database, we found that SERPINB12 was greatly upregulated in NSCLC patients with cigarette consumption behavior, while the expression level was positively correlated with disease grade and poor prognosis. SERPINB12 is a kind of serpin peptidase inhibitor, but its function in malignant tumors remains largely unknown. Functionally, knockdown of SERPINB12 observably inhibited the proliferation and metastasis of NSCLC cells in vitro and in vivo. Moreover, downregulation of SERPINB12 attenuated Wnt signaling by inhibiting the nuclear translocation of β-catenin, which explained the molecular mechanism underlying tumor progression.

CONCLUSIONS

In conclusion, SERPINB12 functions as a tumorigenesis factor, which could be a promising biomarker for NSCLC patients with smoking behavior, as well as a therapeutic target.

The mechanism on Prevotella melaninogenica promoting the inflammatory progression of oral lichen planus

Oral lichen planus (OLP) is a common chronic inflammatory disease occurring in the oral mucosa. Bacteria is a key driver of mucosal immune response and can induce changes in gene expression and function of epithelial keratinocytes. IL-36γ can induce the expression of antimicrobial peptides, cytokines and chemokines, and is widely involved in many chronic inflammatory diseases. Our aim is to explore the role of IL-36γ in pathological process of OLP when Prevotella melaninogenica (P. melaninogenica) invades oral mucosa. The expression of IL-36γ in OLP lesions and mice was detected by immunohistochemistry. Recombinant human IL-36Gamma (rhIL-36γ) was used to treat oral keratinocytes and the expression levels of inflammatory cytokines were detected by qRT-PCR and ELISA. The expression of IL-36γ and TRPV1 was detected by western blotting following co-culturing P. melaninogenica with oral keratinocytes. The mRNA expression of IL-36γ was detected by qRT-PCR. From our results, IL-36γ was upregulated in OLP lesions. Exogenous rhIL-36γ promoted the expression of pro-inflammatory cytokines and antibacterial peptides in oral keratinocytes. The expression of IL-36γ was significantly increased following the stimulation of P. melaninogenica in oral keratinocytes and mice. TRPV1 activation was induced by P. melaninogenica and its activation enhanced the expression of IL-36γ. IL-36Ra could reduce the inflammation in OLP in vitro. In summary, overexpression of IL-36γ in OLP lesions could promote its pathogenesis by inducing inflammation. P. melaninogenica invasion of oral keratinocytes could induce the expression of IL-36γ by the activation of TRPV1, thereby regulating the interaction between bacteria and oral epithelial cells.

Anti-inflammatory effect of paeoniflorin combined with baicalin in oral inflammatory diseases

OBJECTIVE

There are challenges in the treatment of chronic inflammatory diseases of oral mucosa. Both paeoniflorin (PF) and baicalin (BAI) exert anti-inflammatory effects, but the mechanism underlying their combined effects is still unclear. Here, we explored the anti-inflammatory function of the PF-BAI combination in the oral inflammatory response.

MATERIALS AND METHODS

The CCK-8 assay was used to determine the proliferative capacity of HOKs with PF and BAI. Enzyme-linked immunosorbent (ELISA), Western blotting, reverse transcription polymerase chain reaction, and confocal immunofluorescence were performed to study the anti-inflammatory effects of PF-BAI in LPS-stimulated human oral keratinocytes (HOKs). Immunohistochemistry and ELISA were performed to detect the levels of NF-κB p65, IKKα and IL-6, TNF-α in OLP and healthy tissues.

RESULTS

Compared to PF or BAI alone, the combination of PF-BAI at 5 µg/ml downregulated secretion of inflammatory cytokines more effectively (p < .05). Combined PF-BAI decreased NF-κB p65 and IκBα protein phosphorylation, leading to reduce nuclear translocation of NF-κB p65. Higher expression of TNF-α, IL-6, NF-κB p65, and IKKα were observed in OLP than in HC tissues (p < .01).

CONCLUSION

The optimal combination concentration of PF and BAI at 5 µg/ml may have a positive effect on the treatment of oral inflammatory diseases, providing a novel therapeutic approach.