BackgroundOral squamous cell carcinoma (OSCC) is the fourth leading cause of male cancer death in Taiwan. Exposure to environmental carcinogens is the primary risk factor for developing OSCC. CD44, a well-known tumor marker, plays a crucial role in tumor cell differentiation, invasion, and metastasis. This study investigated CD44 single-nucleotide polymorphisms (SNPs) with environmental risk factors to determine OSCC susceptibility and clinicopathological characteristics.Methodology/Principal FindingsReal-time polymerase chain reaction (PCR) was used to analyze 6 SNPs of CD44 in 599 patients with oral cancer and 561 cancer-free controls. We determined that the CD44 rs187115 polymorphism carriers with the genotype AG, GG, or AG+GG were associated with oral cancer susceptibility. Among 731 smokers, CD44 polymorphisms carriers with the betel-nut chewing habit had a 10.30–37.63-fold greater risk of having oral cancer compared to CD44 wild-type (WT) carriers without the betel-nut chewing habit. Among 552 betel-nut chewers, CD44 polymorphisms carriers who smoked had a 4.23–16.11-fold greater risk of having oral cancer compared to those who carried the WT but did not smoke. Finally, we also observed that the stage III and IV oral cancer patients had higher frequencies of CD44 rs187115 polymorphisms with the variant genotype (AG+GG) compared with the wild-type (WT) carriers.ConclusionOur results suggest that gene–environment interactions between the CD44 polymorphisms and betel quid chewing and tobacco smoking increase the susceptibility to oral cancer development. Patients with CD44 rs187115 variant genotypes (AG+GG) were correlated with a higher risk of oral cancer development, and these patients may possess greater chemoresistance to advanced- to late-stage oral cancer than WT carriers do. The CD44 rs187115 polymorphism has potential predictive significance in oral carcinogenesis and also may be applied as factors to predict the clinical stage in OSCC patients.

Laser acupuncture may be an alternative treatment modality for TMD because it is non-invasive, results in partial or total relief of pain, and has no side effects.

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While prognosis and risk of progression are crucial in developing precise therapeutic strategy in neovascular age-related macular degeneration (nAMD), limited predictive tools are available. We proposed a novel deep convolutional neural network that enables feature extraction through image and non-image data integration to seize imperative information and achieve highly accurate outcome prediction. The Heterogeneous Data Fusion Net (HDF-Net) was designed to predict visual acuity (VA) outcome (improvement ≥ 2 line or not) at 12th months after anti-VEGF treatment. A set of pre-treatment optical coherence tomography (OCT) image and non-image demographic features were employed as input data and the corresponding 12th-month post-treatment VA as the target data to train, validate, and test the HDF-Net. This newly designed HDF-Net demonstrated an AUC of 0.989 (95% CI 0.970–0.999), accuracy of 0.936 [95% confidence interval (CI) 0.889–0.964], sensitivity of 0.933 (95% CI 0.841–0.974), and specificity of 0.938 (95% CI 0.877–0.969). By simulating the clinical decision process with mixed pre-treatment information from raw OCT images and numeric data, HDF-Net demonstrated promising performance in predicting individualized treatment outcome. The results highlight the potential of deep learning to simultaneously process a broad range of clinical data to weigh and leverage the complete information of the patient. This novel approach is an important step toward real-world personalized therapeutic strategy for typical nAMD.

Oral submucosal fibrosis (OSF) is a precancerous condition in the oral cavity and areca nut consumption has been regarded as one of the etiologic factors implicated in the development of OSF via persistent activation of buccal mucosal fibroblasts (BMFs). It has been previously reported that an epithelial to mesenchymal transition (EMT) factor, ZEB1, mediated the areca nut-associated myofibroblast transdifferentiation. In the current study, we aimed to elucidate how areca nut affected non-coding RNAs and the subsequent myofibroblast activation via ZEB1. We found that long non-coding RNA LINC00084 was elicited in the BMFs treated with arecoline, a major alkaloid of areca nut, and silencing LINC00084 prevented the arecoline-induced activities (such as collagen gel contraction, migration, and wound healing capacities). The upregulation of LINC00084 was also observed in the OSF tissues and fibrotic BMFs (fBMFs), and positively correlated with several fibrosis factors. Moreover, we showed knockdown of LINC00084 markedly suppressed the myofibroblast features in fBMFs, including myofibroblast phenotypes and marker expression. The results from the luciferase reporter assay confirmed that LINC00084 acted as a sponge of miR-204 and miR-204 inhibited ZEB1 by directly interacting with it. Altogether, these findings suggested that the constant irritation of arecoline may result in upregulation of LINC00084 in BMFs, which increased the ZEB1 expression by sequestering miR-204 to induce myofibroblast transdifferentiation.