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- 저자 : Tiwari Arun Kumar (검색결과 3 건)
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Tiwari Arun Kumar,Gupta Pushpraj S,Prasad Mahesh,Malairajan Paraman 대한약침학회 2022 Journal of pharmacopuncture Vol.25 No.4
Objectives: Hyperlipidemia (HL) is a major cause of ischemic heart diseases. The size-limiting effect of ischemic preconditioning (IPC), a cardioprotective phenomenon, is reduced in HL, possibly because of the opening of the mitochondrial permeability transition pore (MPTP). The objective of this study is to see what effect pretreatment with Inula racemosa Hook root extract (IrA) had on IPC-mediated cardioprotection on HL Wistar rat hearts. An isolated rat heart was mounted on the Langendorff heart array, and then ischemia reperfusion (I/R) and IPC cycles were performed. Atractyloside (Atr) is an MPTP opener. Methods: The animals were divided into ten groups, each consisting of six rats (n = 6), to investigate the modulation of I. racemosa Hook extract on cardioprotection by IPC in HL hearts: Sham control, I/R Control, IPC control, I/R + HL, I/R + IrA + HL, IPC + HL, IPC + NS + HL, IPC + IrA+ HL, IPC + Atr + oxidative stress, mitochondrial function, integrity, and hemodynamic parameters are evaluated for each group. Results: The present experimental data show that pretreatment with IrA reduced the LDH, CK-MB, size of myocardial infarction, content of cardiac collagen, and ventricular fibrillation in all groups of HL rat hearts. This pretreatment also reduced the oxidative stress and mitochondrial dysfunction. Inhibition of MPTP opening by Atr diminished the effect of IrA on IPC-mediated cardioprotection in HL rats. Conclusion: The study findings indicate that pretreatment with IrA e restores IPC-mediated cardioprotection in HL rats by inhibiting the MPTP opening.
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Exploring the Utility of Vague Concept for Uncertainty and Hesitation Management
Arun Kumar Singh,Akhilesh Tiwari 보안공학연구지원센터 2015 International Journal of Hybrid Information Techno Vol.8 No.12
In the realistic situation, there are many areas which contain imprecisely specified data. This imprecise data indicates the presence of vagueness, incompleteness and uncertainty which causes the problem during important decision-making task. The present paper focuses on the problem of mining important inference from supermarket basket data (in the presence of vagueness). The paper specifically studies the usefulness of vague set theory for the exploration of hesitation information and vague association rules. The hesitation information of an item plays a vital role in making selling strategies for the exhilaration of business. For this purpose, the vague set concept can be used as an important tool which can assist in the identification of hesitated item. The vague set theory with its two membership function provides more intuitive way to interact with the vague situation that causes the hesitation for any item. The effectiveness of the hesitated pattern and rule provide advanced decision-making capabilities that transform ‘almost sold’ items to ‘sold items’.
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Rohit Verma,Raj Mehrotra,Chinmay Rane,Ritu Tiwari,Arun Kumar Agariya 대한의용생체공학회 2020 Biomedical Engineering Letters (BMEL) Vol.10 No.3
Proteins are complex macromolecules accountable for the biological processes in the cell. In biomedical research, the imagesof protein are extensively used in medicine. The rate at which these images are produced makes it diffi cult to evaluate themmanually and hence there exists a need to automate the system. The quality of images is still a major issue. In this paper, wepresent the use of diff erent image enhancement techniques that improves the contrast of these images. Besides the qualityof images, the challenge of gathering such datasets in the fi eld of medicine persists. We use generative adversarial networksfor generating synthetic samples to ameliorate the results of CNN. The performance of the synthetic data augmentationwas compared with the classic data augmentation on the classifi cation task, an increase of 2.7% in Macro F1 and 2.64%in Micro F1 score was observed. Our best results were obtained by the pretrained Inception V4 model that gave a fi vefoldcross-validated macro F1 of 0.603. The achieved results are contrasted with the existing work and comparisons show thatthe proposed method outperformed.
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