G2 rotavirus was prevalent in a 1993 epidemic of acute gastroenteritis in Taiwan. In this study, the genetic relationship among G2 rotavirus strains was analysed. The VP7 genes were amplified and sequenced. Except for one strain isolated in 1981, the nucleotide sequences of the VP7 genes of most of the G2 rotaviruses were very similar (identity 97 %) and were closely related to that of a Japanese G2 reference strain, S2. The genetic relatedness of G2 rotaviruses was analysed further by RNA-RNA hybridization. The genomes of the major G2 strains of 1993 did not hybridize well with those of the G2 strains of previous seasons in RNA segments 1, 6 and 7. Partial nucleotide sequences of the VP1 gene were analysed and appeared to be similar among the major G2 strains from the same epidemic (identity 98 %), whereas the identity of the VP1 genes of the major G2 strains of the 1993 epidemic to those of previous seasons was only about 84 %. Since the numbers of mutations accumulated in the VP1 and VP7 genes over a period of 10 years were comparable, the significant change in the VP1 genes of the major strains of the 1993 epidemic suggests that these G2 rotaviruses had evolved by genetic reassortment.

Head and neck squamous cell carcinoma (HNSCC) is one of the leading causes of cancer deaths worldwide, especially in male. With poor prognosis, significant portions of patients with HNSCC die due to cancer recurrence and tumor metastasis after chemotherapy and targeted therapies. The HNSCC FaDu cell ectopic expression of Twist, a key transcriptional factor of epithelial–mesenchymal transition (EMT), which triggers EMT and results in the acquisition of a mesenchymal phenotype, was used as the cell model. Our results demonstrated that treatment with newly synthesized 2‐(3‐hydroxyphenyl)‐5‐methylnaphthyridin‐4‐one (CSC‐3436), a flavonoid derivative, elicited changes in its cell morphology, upregulated E‐cadherin messenger RNA and protein expression, downregulated N‐cadherin, vimentin, and CD133 (a marker associated with tumor‐initiating cells) in FaDu‐pCDH‐Twist cells. Moreover, CSC‐3436 exposure reduced B cell‐specific Moloney murine leukemia virus integration site 1 (Bmi1) expression regulated by Twist and further suppressed the direct co‐regulation of E‐cadherin by Twist and Bmi1. Interestingly, CSC‐3436 reduced EMT, cancer stemness, and migration/invasion abilities through the inhibition of the Twist/Bmi1‐Akt/β‐catenin pathway. Most importantly, our findings provided new evidence that CSC‐3436 played a crucial role in therapeutic targeting to Bmi1 and its molecular pathway in HNSCC, and it will be valuable in prognostic prediction and treatment.

Background/Aim: To investigate the impact of PDZ-binding kinase (PBK) on the clinical outcome of patients with oral squamous cell carcinoma (OSCC) who received radiotherapy. Patients and Methods: PBK immunoreactivity of cancer specimens obtained from 179 patients with primary OSCC was analyzed by immunohistochemistry. Results: High PBK expression in tumor cells tended to be associated with advanced N-stage. The 5-year survival rate was greater for patients with high total PBK expression than in those with low PBK expression. After adjustment, high PBK remained associated with a favorable outcome. In subgroups according to tumor stage, the prognostic role was significant in patients with stage III/IV rather than those with stage I/II disease. Conclusion: We suggest that PBK expression should be used as an independent prognostic marker for patients with OSCC treated with radiotherapy, especially for those with advancedstage disease.Oral squamous cell carcinomas (OSCCs) are the most common among all head and neck squamous cell cancers (1). In 2020, cancer of the lip and oral cavity accounted for more than 377,713 cases and 177,757 deaths worldwide (2). Individuals from developing countries where there is higher number of risk factors, such as smoking, betel nut chewing, and alcohol consumption, are at an increased risk for developing OSCC. In Taiwan, OSCC is the fourth common cancer type (approximate incidence rate of 29 per 100,000 population and mortality of 32 per 100,000 population) and the second cancer type with the fastest increasing incidence (3). The Surveillance, Epidemiology, and End Results Cancer Statistics Review reported that the 5-year relative survival rate patients with locally advanced oral cavity and oropharyngeal cancer is 54.7% whereas it is 82.5% for those with early-stage disease (4).Many efforts have been made to identify biomarkers or specific genes that might provide useful information for clinical patient management. The complex pathogenesis of oral cancer is driven by DNA-repair genes, tumor-suppressor genes, and well-recognized factors, such as alcohol, betel nut chewing, and viral infection (5, 6). PDZ-binding kinase (PBK), also known as lymphokine-activated killer T-celloriginated protein kinase, is a mitogen-activated protein kinase kinase-like serine/threonine kinase that is involved in cell-cycle regulation via a cyclin B1-dependent manner, and in mitotic progression (7-9). PBK is found in proliferative tissues, such as testis, fetal, and neuronal stem cells; studies have found PBK overexpression in various malignancies, such as leukemia, Burkitt's lymphoma, breast cancer, and lung cancer (10-13). PBK is up-regulated in tumors; however, reports on the clinical significance of PBK are lacking. Our previous research showed the unfavorable 2177 *These Authors contributed equally to this study.

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Objective Little information is available about deep learning methods used in ultrasound images of salivary gland tumors. We aimed to compare the accuracy of the ultrasound‐trained model to computed tomography or magnetic resonance imaging trained model. Materials and methods Six hundred and thirty‐eight patients were included in this retrospective study. There were 558 benign and 80 malignant salivary gland tumors. A total of 500 images (250 benign and 250 malignant) were acquired in the training and validation set, then 62 images (31 benign and 31 malignant) in the test set. Both machine learning and deep learning were used in our model. Results The test accuracy, sensitivity, and specificity of our final model were 93.5%, 100%, and 87%, respectively. There were no over fitting in our model as the validation accuracy was similar with the test accuracy. Conclusions The sensitivity and specificity were comparable with current MRI and CT images using artificial intelligence.