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A relational hypothesis analysis for cell death mechanism


Article Information

Title: A relational hypothesis analysis for cell death mechanism

Authors: Kumud Tiwari, Sachin Kumar, R. K. Tiwari

Journal: ARPN Journal of Engineering and Applied Sciences

HEC Recognition History
Category From To
Y 2023-07-01 2024-09-30
Y 2022-07-01 2023-06-30
Y 2021-07-01 2022-06-30
X 2020-07-01 2021-06-30

Publisher: Khyber Medical College, Peshawar

Country: Pakistan

Year: 2019

Volume: 14

Issue: 14

Language: English

Categories

Abstract

Cell death in the cell development cycle, forms one of the most potential areas for experimental as well as theoretical research and an exciting question as well. Regardless of the importance of the cell death process, its mechanisms are not understood and are still being studied. According to the studies, the cell death takes place through two processes: (i) Apoptosis is a form of cell death which is programmed or controlled, (ii) Necrosis is a type of cell death that is accidental or un-programmed [1]. Apoptosis which is a significant part of the molecular mechanism, it has attracted a considerable amount of attention over the last two decades. Apoptosis is characterized by distinct biochemical mechanism and morphological characteristics, it plays a major role in diversified processes including natural cell development, proper functioning of a cell, a cell dies due to chemicals and development of the immune system. The cell death signalling pathway responds to both normal and pathologic stimuli and human diseases like neurodegenerative disorders, ischemic damage, immune deficiency, cancer and autoimmune disorders are caused by unseemly apoptosis. This paper presents a theoretical investigation on the cell death process, its role in cell development, its different forms and its mechanism signalling pathways, a summarized comparison of Apoptosis and Necrosis. Further, a relational hypothesis analysis for cell death mechanism has been conducted. Our results give valuable insights into the factors that are influencing the sample dataset to be malignant. The result shows that p-value had a clear dependency on the percentage of malignant cases in a sample and hence population, as the value increased the percentage share of malignant cases increased.


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