Manipal University

About: Manipal University is a education organization based out in Manipal, Karnataka, India. It is known for research contribution in the topics: Population & Health care. The organization has 9525 authors who have published 11207 publications receiving 110687 citations.

Antigenotoxic potential of rutin and quercetin in Swiss mice exposed to gamma radiation

Abstract: Background: Ionizing radiation induces a variety of genetic damages through the formation free radicals such as reactive oxygen species (ROS). Appropriate antioxidant intervention may inhibit or reduce free radical toxicity and thus offer protection against radiation. Rutin (RUT) and quercetin (QRT) are flavonoids known to be potent dietary antioxidants. Methods: The present study tested the antigenotoxic effect of RUT and QRT in vivo against radiation- induced chromosomal damage. Swiss albino mice were administered orally with RUT and QRT (10 and 20 mg/kg b.wt.) once daily for five consecutive days. One hour after the last administration of RUT and QRT on the fifth day, the animals were whole body exposed to 3 Gy gamma radiation. The anti-genotoxic potential was assessed in terms of chromosomal aberrations, micronucleus test, and alkaline comet assay. Results: Significant decline in dicentric formation was observed in RUT and QRT treated group. Further, the antigenotoxic potential of RUT and QRT caused a significant ( p p Conclusions: Present findings demonstrate the potential of RUT and QRT in mitigating radiation-induced mortality and cytogenetic damage, which may be attributed to scavenging of radiation-induced free radicals.

Antidiabetic effect of Dodonaea viscosa (L). Lacq. aerial parts in high fructose-fed insulin resistant rats: a mechanism based study.

Abstract: Received 25 February 2010; revised 10 May 2010 To study the effect and mode of action of water extract (DVW) and polar fraction of ethanol extract (DVE-4) of D. viscosa in high-fructose diet induced insulin resistance in male Wistar rats. D. viscosa’ s effects were evaluated on a battery of targets involved in glucose homeostasis ( in vitro studies). Rats were rendered insulin resistant by feeding 66% (w/w) fructose and 1.1% (v/w) coconut oil mixed with normal pellet diet (NPD) for six weeks. DVW and DVE4 at different doses were administered simultaneously. At the end of the study, blood glucose, oral glucose tolerance test, lipid profile and insulin were estimated and homeostatic model assessment (HOMA) levels were calculated. In addition, enzymatic and nonenzymatic liver antioxidant levels were also estimated. Quantification of biomarker quercetin was done using HPLC. Fructose diet with DVW, DVE-4 significantly reduced blood glucose, serum insulin, HOMA, lipid profiles and significantly improved glucose tolerance and HDL-c levels. In addition, these extract and fraction also decreased oxidative stress by improving endogenous antioxidants. In different bioassays, DVW and DVE-4 inhibited protein tyrosine phosphatase-1B with IC 50 65.8 and 54.9 μg/ml respectively and showed partial inhibition of dipeptidyl peptidase-IV. Moreover, DVW and DVE-4, at 10 μg/ml showed 60 and 54.2% binding to peroxisome proliferator-activated receptor-γ. Further, 2.1% (w/w) of quercetin was quantified in bioactive-DVE-4 using HPLC method. The results provide pharmacological evidence of D. viscosa in treatment of prediabetic conditions and these effects may be mediated by interacting with multiple targets operating in diabetes mellitus.

Effects of padding on LSTMs and CNNs.

Abstract: Long Short-Term Memory (LSTM) Networks and Convolutional Neural Networks (CNN) have become very common and are used in many fields as they were effective in solving many problems where the general neural networks were inefficient. They were applied to various problems mostly related to images and sequences. Since LSTMs and CNNs take inputs of the same length and dimension, input images and sequences are padded to maximum length while testing and training. This padding can affect the way the networks function and can make a great deal when it comes to performance and accuracies. This paper studies this and suggests the best way to pad an input sequence. This paper uses a simple sentiment analysis task for this purpose. We use the same dataset on both the networks with various padding to show the difference. This paper also discusses some preprocessing techniques done on the data to ensure effective analysis of the data.