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Abstract Objective Intraoral photographic images are instrumental in the early screening and clinical diagnosis of oral diseases. In addition, people have been trying to apply artificial intelligence to these images. The purpose of this study is to investigate and evaluate a deep learning system designed to segment intraoral photographic images for the detection of dental caries, dental calculus, and gingivitis, and to assess the degree of dental calculus based on the overall features of the tooth surface and gingival margin. Material and methods This cross-sectional study collected 3,365 oral endoscopic images, randomly distributed in training datasets (2,019 images), validation dataset (673 images), and test dataset (673 images). The training set and verification set images are manually labeled. An oral endoscopic image segmentation method based on Mamba (Oral-Mamba) and an intelligent evaluation model of dental calculus degree were proposed, achieving the segmentation of two types of oral diseases, namely gingivitis and dental caries, as well as the segmentation of dental calculus regions, and the intelligent evaluation of the degree of dental calculus. Results Oral-Mamba demonstrated high accuracy in segmentation, with accuracy rates for gingivitis, dental caries, and dental calculus at 0.83, 0.83, and 0.81, respectively. In particular, these rates surpassed those of the U-Net model in IoU, accuracy, and recall metrics. Furthermore, Oral-Mamba runs 25% faster than U-Net.The accuracy of degree classification in the intelligent evaluation model of dental calculus degree is 85%. Conclusion The proposed deep learning system is expected to be used for the detection of two types of oral diseases and dental calculus, and the degree judgment of photographic images from an intraoral camera. This system offers a practical method to assist in the oral screening of dental caries, dental calculus, and gingivitis, providing benefits such as intuitive use, time efficiency, cost-effectiveness, and ease of deployment.

Abstract Background Accurate regulation of gene expression is crucial for normal development and function of cells. The prognostic significance and potential carcinogenic mechanisms of the related gene JARID2 in OSCC are not yet clear, but existing research has indicated a significant association between the two. Methods and materials The relationship between the expression of the JARID2 gene in tumor samples of OSCC patients and clinical pathological factors was analyzed using immunohistochemistry experiments and RT-qPCR analysis. Based on the clinical pathological data of patients, bioinformatics analysis was conducted using public databases to determine the function of JARID2 in OSCC. Knockdown OSCC cell lines were constructed, and the impact of JARID2 on the biological behavior of OSCC cell lines was assessed through CCK-8, wound healing assay, and transwell analysis. Results Immunohistochemistry experiments confirmed the correlation between JARID2 and the prognosis of OSCC patients, while RT-qPCR experiments demonstrated its expression levels in tissue and cells. CKK-8 experiments, wound healing assays, and Transwell experiments indicated that knocking down JARID2 had a negative impact on the proliferation, invasion, and migration of OSCC cells. Bioinformatics analysis results showed that the expression of JARID2 in OSCC is closely associated with patient gene co-expression, gene function enrichment, immune infiltration, and drug sensitivity. Conclusion Our study indicates that JARID2 is a novel prognostic biomarker and potential therapeutic target for OSCC.

Abstract The polycomb group (PcG) comprises a set of proteins that exert epigenetic regulatory effects and play crucial roles in diverse biological processes, ranging from pluripotency and development to carcinogenesis. Among these proteins, enhancer of zeste homolog 2 (EZH2) stands out as a catalytic component of polycomb repressive complex 2 (PRC2), which plays a role in regulating the expression of homologous (Hox) genes and initial stages of x chromosome inactivation. In numerous human cancers, including head and neck squamous cell carcinoma (HNSCC), EZH2 is frequently overexpressed or activated and has been identified as a negative prognostic factor. Notably, EZH2 emerges as a significant gene involved in regulating the STAT3/HOTAIR axis, influencing HNSCC proliferation, differentiation, and promoting metastasis by modulating related oncogenes in oral cancer. Currently, various small molecule compounds have been developed as inhibitors specifically targeting EZH2 and have gained approval for treating refractory tumors. In this review, we delve into the epigenetic regulation mediated by EZH2/PRC2 in HNSCC, with a specific focus on exploring the potential roles and mechanisms of EZH2, its crucial contribution to targeted drug therapy, and its association with cancer markers and epithelial–mesenchymal transition. Furthermore, we aim to unravel its potential as a therapeutic strategy for oral squamous cell carcinoma.

Background: Osteoarthritis (OA) is a common chronic joint disease. This study aimed to investigate possible OA diagnostic biomarkers and to verify their significance in clinical samples. Methods: We exploited three datasets from the Gene Expression Omnibus (GEO) database, serving as the training set. We first determined differentially expressed genes and screened candidate diagnostic biomarkers by applying three machine learning algorithms (Random Forest, Least Absolute Shrinkage and Selection Operator logistic regression, Support Vector Machine-Recursive Feature Elimination). Another GEO dataset was used as the validation set. The test set consisted of RNA-sequenced peripheral blood samples collected from patients and healthy donors. Blood samples and chondrocytes were collected for quantitative real-time PCR to confirm expression levels. Receiver operating characteristic curves were generated for individual and combined biomarkers. Results: In total, 251 DEGs were screened, where B3GALNT1, SCRG1 and ZNF423 were screened by all three algorithms. The area under the curve (AUC) of various biomarkers in our test set did not reach as high as that in public datasets. GRB10 exhibited highest AUC of 0.947 in the training set but 0.691 in our test set, while the favorable combined model comprising B3GALNT1, GRB10, KLF9 and SCRG1 demonstrated an AUC of 0.986 in the training set, 1.000 in the validation set and 0.836 in our test set. Conclusion: We identified a combined model for early diagnosis of OA that includes B3GALNT1, GRB10, KLF9 and SCRG1. This finding offers new avenues for further exploration of mechanisms underlying OA.

Highlights Novel enzyme-induced proximity labeling technology in nanoparticles (nano-EPL). Nano-EPL enables dynamic molecular mapping of the intracellular delivery of nanoparticles in macrophages. Nano-EPL enables the elucidation of a comprehensive phagosome-centered timeline during the vesicular transportation of nanoparticles, revealing distinct organelle engagement and its differential impact on drug delivery efficiency.

End-to-end disparity estimation algorithms based on cost volume deployed in edge-end neural network accelerators have the problem of structural adaptation and need to ensure accuracy under the condition of adaptation operator. Therefore, this paper proposes a novel disparity calculation algorithm that uses low-rank approximation to approximately replace 3D convolution and transposed 3D convolution, WReLU to reduce data compression caused by the activation function, and unimodal cost volume filtering and a confidence estimation network to regularize cost volume. It alleviates the problem of disparity-matching cost distribution being far away from the true distribution and greatly reduces the computational complexity and number of parameters of the algorithm while improving accuracy. Experimental results show that compared with a typical disparity estimation network, the absolute error of the proposed algorithm is reduced by 38.3%, the three-pixel error is reduced to 1.41%, and the number of parameters is reduced by 67.3%. The calculation accuracy is better than that of other algorithms, it is easier to deploy, and it has strong structural adaptability and better practicability.

Oral squamous cell carcinoma (OSCC) affects a large number of individuals worldwide. Despite advancements in surgery, radiation, and chemotherapy, satisfactory outcomes have not been achieved. In recent years, the success of drugs targeting programmed cell death 1 (PD-1) and programmed cell death ligand 1 (PD-L1) has led to breakthroughs in cancer treatment, but systematic summaries on their effectiveness against OSCC are lacking. This article reviews the latest research on the PD-1/PD-L1 pathway and the potential of combination therapy based on this pathway in OSCC. Further, it explores the mechanisms involved in the interaction of this pathway with exosomes and protein-protein interactions, and concludes with potential future OSCC therapeutic strategies.

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