Background: DCBLD1 is highly expressed in several kinds of cancer and plays a potential prognostic factor. However, the prognostic value and immune infiltration in head and neck squamous cell carcinoma remain unclear and need further research.Materials and Methods: DCBLD1 expression and clinical information were obtained from the Cancer Genome Atlas (TCGA) database. The mRNA level in cell lines (SCC25 and CAL27) and gingival fibroblasts were detected using quantitative PCR. Cox regression analysis was used to evaluate the prognostic values of DCBLD1 and clinical data in HNSCC. A nomogram was also established to predict the impact of DCBLD1 on prognosis based on Cox multivariate results. The methylation level of DCBLD1 in HNSC and its prognosis were analyzed in UALACN and MethSurv. Finally, the potential biological functions of DCBLD1 were investigated using gene set enrichment analysis (GSEA) and single-sample GSEA (ssGSEA).Results: The mRNA and protein expression levels of DCBLD1 were highly expressed in HNSCC tissue and cell lines. The Cox analyses demonstrate that highly expressed DCBLD1 is an independent prognosis marker (p < 0.05). ROC curve analysis showed the performance of DCBLD1 (area under the ROC curve: 0.948, sensitivity: 93.2%, specificity: 84.7%). The methylation was increased in HNSCC patients compared with normal subjects (p < 0.05) and was associated with poor prognosis at sites cg27642470 and cg21104965. Additionally, DCBLD1 expression is poorly associated with immune cell infiltration and immunological checkpoints PD-L1 and TIM-3.Conclusion: In head and neck squamous cell carcinoma, DCBLD1 is overexpressed, associated with poor patient prognosis. The detailed underlying mechanism merits further research.

Introduction: Head and neck cancer (HNC) is a complex disease, and multiple risk factors can lead to its progression. Observational studies indicated that herpes simplex virus (HSV) may be correlated with the risk of HNC. However, the causal effects and direction between them were still unclear.Methods: This study utilized a Mendelian randomization (MR) approach for causality assessment between HSV infection and Head and neck cancer based on the latest public health data and Genome-Wide Association Study (GWAS) data. The causal effects were estimated using IVW, weighted median, and MR-Egger. A reverse MR analysis was subsequently performed. Cochrans Q test, MR‐Egger intercept test, leave one out analysis, and the funnel plot were all used in sensitivity analyses.Results: Genetically predicted higher level of HSV-1 IgG was causally related to HNC (OR=1.0019, 95%CI=1.0003–1.0036, p=0.0186, IVW) and oral and oropharyngeal cancer (OR=1.0018, 95%CI=1.0004–1.0033, p=0.0105, IVW). The reverse MR analysis did not demonstrate a reverse causal relationship between HSV and HNC. However, HSV-2 infection was not causally related to HNC data and oropharyngeal cancer data. Sensitivity analysis was performed and revealed no heterogeneity and horizontal pleiotropy.Conclusion: Collectively, a significant association was noted between HSV infection and increased risk of HNC, providing valuable insights into the etiology of this malignancy. Further in-depth study is needed to validate these findings and elucidate the underpinning mechanisms.

Introduction: The coronavirus disease 2019 (COVID-19) has emerged as a main global public health challenge. Additionally, herpes simplex virus type-1 (HSV-1) and type 2 (HSV-2) are widespread viruses that can cause orolabial herpes and genital herpes. Several clinical case reports have declared a possible association between the two, however, the causal relationship between them has not been clarified.Methods: This study utilized a Mendelian randomization (MR) approach for causality assessment between COVID-19 infection and HSV infection based on the latest public health data and Genome-Wide Association Study (GWAS) data. Multiple causal estimation methods, such as IVW, weighted median, simple mode, and weighted mode, were employed to validate the causal relation between COVID-19 infection and HSV infection, with COVID-19 infection, COVID-19 hospitalization, and severe COVID-19 as exposures, and HSV1/2 infection as the outcome. A reverse MR analysis was subsequently performed.Results: MR analysis exhibited that COVID-19 infection was relevant to a reduced risk of HSV1 infection (p=7.603239e−152, OR=0.5690, 95%CI=0.5455−0.5935, IVW). Regarding the effect of COVID-19 infection on HSV2, MR analysis suggested that COVID-19 infection was correlated with an augmented risk of HSV2 infection (p=6.46735e−11, OR=1.1137, 95%CI=1.0782−1.1502, IVW). The reverse MR analysis did not demonstrate a reverse causal relationship between HSV and COVID-19. Discussion: Altogether, COVID-19 infection might cause a decreased risk of HSV1 infection and an elevated risk of HSV2 infection.

Background: Although the morphological changes of sella turcica have been drawing increasing attention, the acquirement of linear parameters of sella turcica relies on manual measurement. Manual measurement is laborious, time-consuming, and may introduce subjective bias. This paper aims to develop and evaluate a deep learning-based model for automatic segmentation and measurement of sella turcica in cephalometric radiographs. Methods: 1129 images were used to develop a deep learning-based segmentation network for automatic sella turcica segmentation. Besides, 50 images were used to test the generalization ability of the model. The performance of the segmented network was evaluated by the dice coefficient. Images in the test datasets were segmented by the trained segmentation network, and the segmentation results were saved in binary images. Then the extremum points and corner points were detected by calling the function in the OpenCV library to obtain the coordinates of the four landmarks of the sella turcica. Finally, the length, diameter, and depth of the sella turcica can be obtained by calculating the distance between the two points and the distance from the point to the straight line. Meanwhile, images were measured manually using Digimizer. Intraclass correlation coefficients (ICCs) and Bland–Altman plots were used to analyze the consistency between automatic and manual measurements to evaluate the reliability of the proposed methodology. Results: The dice coefficient of the segmentation network is 92.84%. For the measurement of sella turcica, there is excellent agreement between the automatic measurement and the manual measurement. In Test1, the ICCs of length, diameter and depth are 0.954, 0.953, and 0.912, respectively. In Test2, ICCs of length, diameter and depth are 0.906, 0.921, and 0.915, respectively. In addition, Bland–Altman plots showed the excellent reliability of the automated measurement method, with the majority measurements differences falling within ± 1.96 SDs intervals around the mean difference and no bias was apparent. Conclusions: Our experimental results indicated that the proposed methodology could complete the automatic segmentation of the sella turcica efficiently, and reliably predict the length, diameter, and depth of the sella turcica. Moreover, the proposed method has generalization ability according to its excellent performance on Test2.

Objective: Despite numerous existing treatments for keloids, the responses in the clinic have been disappointing, due to either low efficacy or side effects. Numerous studies dealing with preclinical and clinical trials have been published about effective therapies for fibrotic diseases using mesenchymal stem cells; however, no research has yet been reported to scientifically investigate the effect of human dental pulp stem cells (HDPSCs) on the treatment of keloids. The objective is to provide an experimental basis for the application of stem cells in the treatment of keloids. Methods: Human normal fibroblasts (HNFs) and human keloid fibroblasts (HKFs) were cultured alone and in combination with HDPSCs using a transwell cell-contact-independent cell culture system. The effects of HDPSCs on HKFs were tested using a CCK-8 assay, live/dead staining assay, quantitative polymerase chain reaction, Western blot and immunofluorescence microscopy. Results: HDPSCs did not inhibit the proliferation nor the apoptosis of HKFs and HNFs. HDPSCs did, however, inhibit their migration. Furthermore, HDPSCs significantly decreased the expression of profibrotic genes (CTGF, TGF-β1 and TGF-β2) in HKFs and KNFs (p < 0.05), except for CTGF in HNFs. Moreover, HDPSCs suppressed the extracellular matrix (ECM) synthesis in HKFs, as indicated by the decreased expression of collagen I as well as the low levels of hydroxyproline in the cell culture supernatant (p < 0.05). Conclusions: The co-culture of HDPSCs inhibits the migration of HKFs and the expression of pro-fibrotic genes, while promoting the expression of anti-fibrotic genes. HDPSCs’ co-culture also inhibits the synthesis of the extracellular matrix by HKFs, whereas it does not affect the proliferation and apoptosis of HKFs. Therefore, it can be concluded that HDPSCs can themselves be used as a tool for restraining/hindering the initiation or progression of fibrotic tissue.