7 research outputs found

    The 2G allele of promoter region of Matrix metalloproteinase-1 as an essential pre-condition for the early onset of oral squamous cell carcinoma

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    <p>Abstract</p> <p>Background</p> <p>Matrix metalloproteinase (<it>MMP</it>) is known to be involved in the initial and progressive stages of cancer development, and in the aggressive phenotypes of cancer. This study examines the association of single nucleotide polymorphisms in promoter regions of <it>MMP-1 </it>and <it>MMP-3 </it>with susceptibility to oral squamous cell carcinoma (OSCC).</p> <p>Methods</p> <p>We compared 170 Japanese OSCC cases and 164 healthy controls for genotypes of <it>MMP-1 </it>and <it>MMP-3</it>.</p> <p>Results</p> <p>The frequency of the <it>MMP-1 </it>2G allele was higher and that of the 1G homozygote was lower in the OSCC cases (<it>p </it>= 0.034). A multivariate logistic regression analysis revealed that subjects who were 45 years old or older had a significantly increased (2.47-fold) risk of OSCC (95%CI 1.47–4.14, <it>p </it>= 0.0006), and those carrying the <it>MMP-1 </it>2G allele had a 2.30-fold risk (95%CI 1.15–4.58, <it>p </it>= 0.018), indicating independent involvement of these factors in OSCC. One of the key discoveries of this research is the apparent reduction of the <it>MMP-1 </it>1G/1G and 1G/2G genotype distributions among the early onset OSCC cases under the ages of 45 years. It should be noted that the tongue was the primary site in 86.2% of these early onset cases. This could suggest the specific carcinogenic mechanisms, i.e. specific carcinogenic stimulations and/or genetic factors in the tongue.</p> <p>Conclusion</p> <p>Since the 2G allele is a majority of the <it>MMP-1 </it>genotype in the general population, it seems to act as a genetic pre-condition in OSCC development. However this report suggests a crucial impact of the <it>MMP-1 </it>2G allele in the early onset OSCC.</p

    Hate and offensive language detection using BERT for English subtask A

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    Abstract This paper presents the results and main findings of the HASOC-2021 Hate/Offensive Language Identification Subtask A. The work consisted of fine-tuning pre-trained transformer networks such as BERT and an ensemble of different models, including CNN and BERT. We have used the HASOC-2021 English 3.8k annotated twitter dataset. We compare current pre-trained transformer networks with and without Masked-Language-Modelling (MLM) fine-tuning on their performance for offensive language detection. Among different BERT MLM fine-tuned BERT-base, BERT-large, and ALBERT outperformed other models; however, BERT and CNN ensemble classifier that applies majority voting outperformed other models, achieving 85.1% F1 score on both hate/non-hate labels. Our final submission achieved 77.0 F1 in the HASOC-2021 competition
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