Browsing by Author "Kumar, M"

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Epidermal growth factor receptor and its trafficking regulation by acetylation: Implication in resistance and exploring the newer therapeutic avenues in cancer
(Bentham Science Publishers, 2020) Kumar, M; Joshi, G; Chatterjee, J; Kumar, R.
Background: The EGFR is overexpressed in numerous cancers. So, it becomes one of the most favorable drug targets. Single-acting EGFR inhibitors on prolong use induce resistance and side effects. Inhibition of EGFR and/or its interacting proteins by dual/combined/multitargeted therapies can deliver more efficacious drugs with less or no resistance. Objective: The review delves deeper to cover the aspects of EGFR mediated endocytosis, leading to its trafficking, internalization, and crosstalk(s) with HDACs. Methods and Results: This review is put forth to congregate relevant literature evidenced on EGFR, its impact on cancer prognosis, inhibitors, and its trafficking regulation by acetylation along with the current strategies involved in targeting these proteins (EGFR and HDACs) successfully by involving dual/hybrid/combination chemotherapy. Conclusion: The current information on cross-talk of EGFR and HDACs would likely assist researchers in designing and developing dual or multitargeted inhibitors through combining the required pharmacophores. � 2020 Bentham Science Publishers.
Global trends in pesticides: A looming threat and viable alternatives
(Academic Press, 2020) Sharma, A; Shukla, A; Attri, K; Kumar, M; Kumar, P; Suttee, A; Singh, G; Barnwal, R.P; Singla, N.
Pesticides are widely used chemical compounds in agriculture to destroy insects, pests and weeds. In modern era, they form an indispensable part of agricultural and health practices. Globally, nearly 3 billion kg of pesticides are used every year with a budget of ~40 billion USD. This extensive usage has increased the crop yield as well as led to significant reduction in harvest losses and thereby, enhanced food availability. On the other hand, indiscriminate usage of these chemicals has led to several environmental implications and caused adverse effects on human health. Epidemiological evidences have revealed the harmful effects of pesticides exposure on various organs including liver, brain, lungs and colon. Recent investigations have shown that pesticides can also lead to fatal consequences such as cancer among individuals. These chemicals enter ecosystem, thus hampering the sensitive environmental equilibrium through bio-accumulation. Due to their non-biodegradable nature, they can persist in nature for years and are regarded as potent biohazard. Worldwide, very few surveillance methods have been considered, which can bring awareness among the individuals, therefore the present review is an attempt to delineate consequences induced by various types of pesticide exposure on the environment. Further, the prospective of biopesticides use could facilitate the increase of crop production without compromising human health.- 2020 Elsevier Inc.
Protective Effect of Hemin Against Experimental Chronic Fatigue Syndrome in Mice: Possible Role of Neurotransmitters
(Springer, 2020) Thakur, V; Jamwal, S; Kumar, M; Rahi, V; Kumar, P.
Chronic fatigue syndrome (CFS) is a disorder characterized by persistent and relapsing fatigue along with long-lasting and debilitating fatigue, myalgia, cognitive impairment, and many other common symptoms. The present study was conducted to explore the protective effect of hemin on CFS in experimental mice. Male albino mice were subjected to stress-induced CFS in a forced swimming test apparatus for 21 days. After animals had been subjected to the forced swimming test, hemin (5 and 10 mg/kg; i.p.) and hemin (10 mg/kg) + tin(IV) protoporphyrin (SnPP), a hemeoxygenase-1 (HO-1) enzyme inhibitor, were administered daily for 21 days. Various behavioral tests (immobility period, locomotor activity, grip strength, and anxiety) and estimations of biochemical parameters (lipid peroxidation, nitrite, and GSH), mitochondrial complex dysfunctions (complexes I and II), and neurotransmitters (dopamine, serotonin, and norepinephrine and their metabolites) were subsequently assessed. Animals exposed to 10 min of forced swimming session for 21 days showed a fatigue-like behavior (as increase in immobility period, decreased grip strength, and anxiety) and biochemical alteration observed by increased oxidative stress, mitochondrial dysfunction, and neurotransmitter level alteration. Treatment with hemin (5 and 10 mg/kg) for 21 days significantly improved the decreased immobility period, increased locomotor activity, and improved anxiety-like behavior, oxidative defense, mitochondrial complex dysfunction, and neurotransmitter level in the brain. Further, these observations were reversed by SnPP, suggesting that the antifatigue effect of hemin is HO-1 dependent. The present study highlights the protective role of hemin against experimental CFS-induced behavioral, biochemical, and neurotransmitter alterations. - 2020, Springer Science+Business Media, LLC, part of Springer Nature.
Time series data analysis of stock price movement using machine learning techniques
(Springer, 2020) Parray, I.R; Khurana, S.S; Kumar, M; Altalbe, A.A.
Stock market also called as equity market is the aggregation of the sellers and buyers. It is concerned with the domain where the shares of various public listed companies are traded. For predicting the growth of economy, stock market acts as an index. Due to the nonlinear nature, the prediction of the stock market becomes a difficult task. But the application of various machine learning techniques has been becoming a powerful source for the prediction. These techniques employ historical data of the stocks for the training of machine learning algorithms and help in predicting their future behavior. The three machine learning algorithms used in this paper are support vector machine, perceptron, and logistic regression, for predicting the next day trend of the stocks. For the experiment, dataset from about fifty stocks of Indian National Stock Exchange�s NIFTY 50 index was taken, by collecting stock data from January 1, 2013, to December 31, 2018, and lastly by the calculation of some technical indicators. It is reported that the average accuracy for the prediction of the trend of fifty stocks obtained by support vector machine is 87.35%, perceptron is 75.88%, and logistic regression is 86.98%. Since the stock data are time series data, another dataset is prepared by reorganizing previous dataset into the supervised learning format which improves the accuracy of the prediction process which reported the results with support vector machine of 89.93%, perceptron of 76.68%, and logistic regression of 89.93%, respectively. � 2020, Springer-Verlag GmbH Germany, part of Springer Nature.