Role of mitochondria in progression of cancer: a semi-quantitative study

Mitochondria have been an area of scientific study for more than 100 years. It was in early 20th century that Otto Warburg first described differences in the mitochondria of tumors v/s normal cells. It was observed that tumor cells have increased rate of aerobic glycolysis compared with normal cells. The study was carried out in patients diagnosed as premalignant and malignant conditions which had three objectives that is to demonstrate the presence or absence of mitochondria in cytological smears, in order to perform a semi-quantitative analysis on the number of mitochondria and to identify the difference in distribution mitochondria if any. The study was carried out in the Department of Oral and Maxillofacial Pathology of S.P.D.C., Sawangi, Wardha with consent from patients and approval from the institutional ethical committee. 20 patients each diagnosed clinically and histo-pathologically with OSCC and Premalignant conditions or lesions respectively were selected for the purpose of the study. 20 subjects who had come for routine endodontic treatment were taken as control group for the purpose of the study. It was observed that there was even distribution of mitochondria throughout the cytoplasm in smear that had been taken from normal mucosa which appeared sharply defined whereas in premalignant mitochondria were located in the perinuclear zone and 10% in the peripheral zone and in malignant conditions distribution was sparse in the perinuclear area and appeared ill-defined

Cost-effectiveness of population-based screening for oral cancer in India: an economic modelling study

Background: Oral cancer screening reduces mortality associated with oral cancer. The current study evaluated the cost-effectiveness of commonly used screening techniques, namely conventional oral examination (COE), toluidine blue staining (TBS), oral cytology (OC), and light-based detection (LBD) in the Indian scenario. Methods: The study used a Markov modelling approach to estimate the cost and health outcomes of four different approaches (COE, TBS, OC, and LBD) for screening oral cancer over time from a societal perspective. The discount rate was assumed as 3%. The outcomes estimated were oral cancer incident cases, deaths averted, and quality-adjusted life years (QALYs). To address the high burden of risk factors (tobacco and/or alcohol) in India, two Markov models were developed: Model A adopted a mass-screening strategy, whereas Model B adopted a high-risk screening strategy versus no screening. Probabilistic sensitivity analysis (PSA) was undertaken to address any parameter uncertainty. Findings: Mass-screening using LBD at three years had the least incident cases (3271.68) and averted the maximum number of oral cancer deaths (459.76). High-risk screening using COE at ten years interval incurred the least lifetime cost of 2,292,816.21 US$(182,794,468.26 INR). The high-risk strategies (US$/QALY), namely COE 5 years (−29.21), COE 10 years (−90.68), TBS 10 years (−60.54), and LBD 10 years (−13.51), were dominant over no-screening. Interpretation: The most cost-saving approach was the conventional oral examination at an interval of 10 years for oral screening in high-risk populations above 30 years of age. Funding: Department of Health Research, Ministry of Health & Family Welfare, Government of India

Sclerosing mucoepidermoid carcinoma of minor salivary gland

Sclerosing mucoepidermoid carcinoma (SMEC) is extremely rare variant of the mucoepidermoid carcinoma, which is the most common primary malignancy of the salivary glands. As its name suggests, SMEC is characterized by an intense central sclerosis that occupies the entirety of an otherwise typical tumor, frequently with an inflammatory infiltrate of plasma cells, eosinophils, and/or lymphocytes at its peripheral regions, but its uncompanionship with inflammatory cell infiltration might explain its progressive stage of the sclerosis. The sclerosis associated with these tumors may obscure their typical morphologic features and result in diagnostic difficulties. Tumor infarction and extravasation of mucin eventuating in reactive fibrosis are two mechanisms of formation that have been suggested as underlying this morphologic variant. Morphologic evidence in support of the mucin extravasation hypothesis was identified, as small pools of mucin were present throughout the tumor

Hate speech detection: A comprehensive review of recent works

There has been surge in the usage of Internet as well as social media platforms which has led to rise in online hate speech targeted on individual or group. In the recent years, hate speech has resulted in one of the challenging problems that can unfurl at a fast pace on digital platforms leading to various issues such as prejudice, violence and even genocide. Considering the acceptance of Artificial Intelligence (AI) and Natural Language Processing (NLP) techniques in varied application domains, it would be intriguing to consider these techniques for automated hate speech detection. In literature, there have been efforts to recognize and categorize hate speech using varied Machine Learning (ML) and Deep Learning (DL) techniques. Hence, considering the need and provocations for hate speech detection we aim to present a comprehensive review that discusses fundamental taxonomy as well as recent advances in the field of online hate speech identification. There is a significant amount of literature related to the initial phases of hate speech detection. The background section provides a detailed explanation of the previous research. The subsequent section that follows is dedicated to examining the recent literature published from the year 2020 onwards. The paper presents some of the hate speech datasets considered for hate speech detection. Furthermore, the paper discusses different data modalities, namely, textual hate speech detection, multi-modal hate speech detection and multilingual hate speech detection. Apart from systematic review on hate speech detection, the paper also implement several multi-label models to compare the performance of hate speech detection by employing classic ML technique namely, Logistic Regression and DL technique namely, Long Short-Term Memory (LSTM) and a multiclass multi-label architecture. In the implemented architecture, we have derived two new elements to quantify the hatefulness and intensity of hatred to improve the results for hate speech detection using Indonesian tweet dataset. Empirical Analysis of the model reveals that the implemented approach outperforms and is able to achieve improved results for the underlying dataset