Correlation Between Oxidative Stress and Antioxidant in Diabetic Nephropathy

Meena, B. P.; Agarwal, B.K.; Mishra, Arun Kumar; Kumar, Mahesh

doi:10.32553/ijmsdr.v4i9.666

Cited by 2 publications

(2 citation statements)

References 2 publications

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“…The result shows that the passive infrared sensor along with radio frequency identification-based IoT localization gives better accuracy of 93% than light dependent register [2]. Smartphone-based localization gives better accuracy than Bluetooth low energy-based localization [4]. In [6], Geetha et al has proposed a model to predict diseases in tomato leaves and also proposed a machine learning methodology that focuses on generating an advanced and efficient system that makes processes of creating high-yield tomatoes much easier for farmers.…”

Section: Literature Reviewmentioning

confidence: 98%

“…IoT devices may be used for various applications such as user localization [1,2] home automation and to control of electric appliances in the office/home as per user movement [3,4,5]. The user/device localization and home automation have been done using IoT devices such as passive infrared sensors, radio frequency identification, light-dependent register, Bluetooth low energy, and smartphone.…”

Section: Literature Reviewmentioning

confidence: 99%

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Plant Health Prediction and Monitoring Based on convolution Neural Network in North-East India

Meena¹

2023

IJRITCC

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Agriculture is the main backbone of any society. In this modern era as the population continuously increases, resources for farming are also decreasing. If the health condition of the plant’s determined at a regular interval, farmers can take action timely to improve the plant’s health condition. Plant monitoring and plant health status at regular intervals may lead to better growth of foods. But the regular physical visit to the crop field to monitor plants and plant health is a critical task for a large crop field. To overcome this situation, we need to shift from a traditional cropping system to smart agriculture. Now, these days, a smart agriculture-based approach can use the internet of things, machine learning, and deep learning to predict the health condition of plants. In this paper, the internet of things (IoT) based smart agriculture system has been presented along with machine learning, deep learning, and transfer learning to monitor and health prediction of plants. The IoT-based system has been used to monitor the plant’s surrounding parameters such as humidity, temperature, light intensity, and soil water moisture. The leaf images of plants have been used in deep learning (DL), machine learning (ML), and transfer learning (TL) to predict the health condition of plants. In this paper, the convolution neural network (CNN) based model has been proposed and it has been compared with the support vector machine (SVM), random forest (RF), VGG19, and mobilenet model. It has been concluded that the accuracy of the proposed CNN model is 81.5 %, which is the highest among SVM, RF, VGG19, and mobilenet..

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Section: Literature Reviewmentioning

confidence: 98%

Section: Literature Reviewmentioning

confidence: 99%

Plant Health Prediction and Monitoring Based on convolution Neural Network in North-East India

Meena¹

2023

IJRITCC

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Correlation of Serum Albumin and Creatinine With Oxidative Stress Markers in Patients Having Nephrotic Syndrome

Parmar¹,

Mistry²,

Gang

2021

Int J Pharm Pharm Sci

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Objective: Children with nephrotic syndrome (NS) have a stressful condition, and oxidative damage may impair their treatment response. This study aims to gain a better understanding of the relationship between oxidative stress and NS to lay the basis for further research into improved diagnostic options, treatment, and prevention of the disease. Methods: We took a blood sample from 100 Indian patients aged 2-14 y. Each patient was tested for oxidative stress. The buege method was used to assess MDA levels in patients. The pyrogallol method was used to measure SOD activity in blood serum, and the jollow method was used to measure glutathione levels. Results: The levels of oxidative stress markers (MDA, SOD, and GSH) were compared between NS patients and the control. SOD and GSH concentrations were significantly decreased in the NS group when compared to the control. In contrast, MDA level was significantly higher in the NS group than in the control. In the correlation analysis, we found that the serum SOD activity was significantly positively correlated with serum albumin and creatinine level in patients with NS. Thus, oxidative stress in children with NS is indicated by reduced antioxidant potential because of low albumin. Therefore, it is thought that oxidative stress is implicated in the development of NS in Indian children. Conclusion: We concluded that oxidative stress was intensified in children with NS due to decreased antioxidant levels caused by hypoalbuminemia.

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Correlation Between Oxidative Stress and Antioxidant in Diabetic Nephropathy

Cited by 2 publications

References 2 publications

Plant Health Prediction and Monitoring Based on convolution Neural Network in North-East India

Plant Health Prediction and Monitoring Based on convolution Neural Network in North-East India

Correlation of Serum Albumin and Creatinine With Oxidative Stress Markers in Patients Having Nephrotic Syndrome

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