Search

This study focuses on the induction of EMT by HPV16 in the C33A cell line. Expression of β-catenin, EMT-transcription factors (EMT-TFs), and c-myc in the nuclei of HPV16 E6/E7 oncogene transfected and non-transfected C33A cells were investigated through immunofluorescence and RT-PCR. Microphotographs of β-catenin, c-myc, and DAPI-stained nuclei were processed and analyzed by Python and ImageJ respectively. Microphotographs of immunocytochemically stained transfected and control cells were then processed and analyzed with the help of ImageJ and Python programming. The intensity and the integrated density of β-catenin were computed at the cell membrane area as well as the cytoplasmic area along with the integrated density of c-myc and Renyi entropy of DAPI-stained nuclei was quantified by ImageJ software. Python programming was implemented to determine the total percentage of white pixels depicting the presence of β-catenin in the cytoplasmic area of cells. The signal of β-catenin at the cytoplasmic area was found significantly higher in transfected samples which implies the nuclear accumulation of β-catenin. The expression of the c-myc protein was found significantly higher in transfected cells along with significantly higher nuclear entropy. RT-PCR result shows two folds of up-regulation of EMT-TFs Snail1, Twist1, and Zeb2 and down-regulation of Snail2 and Twist2. The study concludes that HPV16 E6/E7 oncogene can induce EMT., Competing Interests: Conflict of interest The authors would like to declare that there is no conflict of interest., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Abnormal epithelial stratification is a sign of oral dysplasia and hence evaluation of surface characteristics of oral epithelial region can help in detection of cancerous progression. Surface characteristics can be better visualised by Scanning Electron Microscopy (SEM) in comparison to light microscopy. In our study we have developed automated image processing algorithms i.e. Gaussian with median filtering and Gradient filtering, using MATLAB 2016b, to segment the surface characteristics i.e. the ridges and pits in the SEM images of oral tissue of normal (13 samples) and Oral Submucous Fibrosis (OSF) (36 samples) subjects. After segmentation, quantitative measurement of the parameters like area, thickness and textural features like entropy, contrast and range filter of ridges as well as area of pit and the ratio of area of ridge vs. area of pit was done. Statistical significant differences were obtained in between normal and OSF study groups for thickness (p=0.0107), entropy (p<0.00001) and contrast of ridge (p<0.00001) for Gaussian with median filtering and for all the parameters except thickness of the ridge(p=1.386), for Gradient filtering. Thus, computer aided image processing by Gradient filter followed by quantitative measurement of the surface characteristics provided precise differentiation between normal and precancerous oral condition., (Copyright © 2019 Elsevier Ltd. All rights reserved.)