Online ISSN: 2515-8260

Keywords : Histopathology


HEPATOPROTECTIVE EFFECT OF d-LIMONENE AGAINST ADRIAMYCIN INDUCED HEPATOTOXICITY IN EXPERIMENTAL RATS

Dr. IndumathiSelvanathan; Dr.Sivasakthi Arumugham; S.M.Fazeela Mahaboob Begum

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 11, Pages 7712-7721

Objective: Monoterpenes plays an essential role to fight against various diseases. Among the various monoterpenes an efficientd-Limonene plays a fundamental role to fight against hepatotoxicity caused by cancer chemotheraphy treatment. Hence the presence study is to evaluate hepatoprotective function of d-limonene against adraimycin induced hepatotoxicity.
Methods: Male albino Wistar rats were administrated with ADR (15mg/Kg body weight) in six equal injection, and the protection efficacy of d-limonene (100mg/Kg body weight) was examined with reference to tissue AST level and the pathological studies was examined by microscopic study.
Results: Rats treated with ADR results in elevated level of liver AST marker enzymes, whereasthe level of AST was controlled when administrated with d-Limonene. However Histopathological proof added more protective role of rats treated with d-limonene against hepatotoxicity.
Conclusion:ADR administration of 15mg/Kg body weight of rats increase the level of hepatotoxicity by increasing the marker enzyme activity and show severe morphological changes. The final outcome from our result suggests that d-limonene (100mg/Kg body weight) a vibrant monoterpene act as latent hepatoprotective negotiator by attenuating ADR induced hepatotoxicity

Multi-Stage Classification Technique for Breast Cancer Detection in Histopathology Images using Deep Learning

Nagamani Gonthina; C. Jagadeeswari; Prabhavathi V; Sneha B

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 4, Pages 1104-1110

This research paper proposes the past decenary, substantial improvement in computational ability and betterment in algorithms for analysis of Images has gained vast fame in resolving challenges in the area of medical diagnosis. Subsequently, computerized tissue histopathology at present is becoming tractable towards the implementation of digitized analysis of images and deep learning methods. Cancer is a cluster of disorders involving irregular cell maturation with the capability to conquer or proliferate to other organs of the body. Detection of cancer in the earlier stages is a exacting task due to which many people are prone to death. Treatment of cancer benefits from the pace, perfection of Deep Learning-obliged practice of diagnosis. Deep Learning techniques are utilized to diagnose the features of progressed carcinoma with enhanced perfection compared to individual pathologist. This paper suggests a deep convolution neural network for categorizing a tissue as malicious, there after segregate the tissue then ultimately perform multi-class detection and classification of Breast Cancer disease and its stages in histopathology images

“Assessment of lymph node status in cases of metastatic malignancy by frozen section and imprint cytology”

Dr Miheer Milind Jagtap; Dr Samarth Shukla; Dr Sunita , Vagha; Dr Ankita Tamhane; Dr Sourya Acharya; Dr. Miheer Milind , Jagtap

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 2, Pages 2557-2551

Abstract: Introduction: Dissemination of cancers most commonly occurs by the lymphatic
route and is generally favoured by carcinomas. The best achievable goal of any surgical
procedure is removal of all the affected tissue and leave behind healthy tissue which is
entirely free of any malignant cells. Though histopathological examination is gold
standard, it is time consuming and cannot be implemented as an intraoperative diagnostic
tool. The present study utilises frozen section analysis and touch imprint cytology as
intraoperative tools and analyses their diagnostic accuracy to detect lymph node metastasis
in comparison with routine histopathology in epithelial malignancies.
Aim: To evaluate the efficacy of intraoperative diagnosis by touch imprint cytology and
frozen section analysis for the assessment of metastatic lymph node deposits.
Materials and methods: Total 76 cases of primary malignancy with suspicious metastatic
lymph nodes were investigated. Metastatic nodes were subjected to frozen section and 
touch imprint cytology and these findings were compared with routine histopathology and
the sensitivity, specificity, positive predictive value, negative predictive value and accuracy
were calculated.
Results: The sensitivity, specificity, positive predictive value, negative predictive value and
diagnostic accuracy of frozen section was found to be 97.30%, 100%, 100%, 97.5% and
98.68%. The values of the same parameters for imprint cytology was found to be 75.68%,
100%, 100%, 81.25% and 67% respectively.
Conclusions: Frozen section analysis proved superior to imprint cytology in detecting
lymph node metastasis intraoperatively. Apart from detecting the presence of metastasis,
frozen section is able to provide details regarding micro-metastasis, macro-metastasis and
perinodal fat invasion. This study predominantly evaluated epithelial malignancies and
thus proves the utility of these two intraoperative modalities in them. It also opens new
avenues for research pertaining to the utility of these modalities in various malignant
mesenchymal tumours.