Frequency of type I and II diabetes in newly diagnosed diabetic patients: Measuring C-Peptide level

Shahbazian, Hajieh; Aleali, Armaghan Moravej; Rashidi, Homeira; Latifi, Seyed Mahmoud; Rashidi, Mojtaba; Yazdanpanah, Leila; Zaman, Ferdos; Payami, Seyed Peyman; Jahanshahi, Alireza; Sedaghat, Alireza; Zakerkish, Mehrnoosh; Moradi, Mitra

doi:10.1016/j.dsx.2019.04.018

Cited by 6 publications

(3 citation statements)

References 16 publications

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“…IF || U (k+1) -U (k) ||<δ then we are to stop. Otherwise, we have to return to step 2 by updating the cluster centers iteratively and the membership grades for data points [13].…”

Section: Algorithmic Steps For Gaussian Kernel Fuzzy C-means Clusteringmentioning

confidence: 99%

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Dp-Rnn: Type Ii Diabetic Prediction Using Gkfcm And Rnn

Mrs.K.Gandhimathi¹

2021

TURCOMAT

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Diabetes is a form of metabolic disorder marked by elevated persistent blood glucose (BG), leading to several severe problems in the long term. Continuous monitoring and prediction of BG concentration are needed to help diabetic patients maintain their wellbeing. If insulin is minimized, machine learning models, such as CNN, RNN, and others, are standard data-driven BG prediction solutions. They use several patients' BG data to train the prediction model. However, all of the training data with the same parameters can not accurately capture BG fluctuation characteristics. Motivated by the possibility that various subgroups of diabetic patients have different BG fluctuation trends, we suggest a new BG prediction method called DP-RNN focused on recurrent neural networks (RNN) and incorporates a clustering pre-process using GKFCM. In terms of BG estimation precision, numerical finding shows that the suggested DP-RNN methodology uses more than one cluster for type II diabetes and outperforms Logistic regression (LR) and other CNN approaches.

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“…IF || U (k+1) -U (k) ||<δ then we are to stop. Otherwise, we have to return to step 2 by updating the cluster centers iteratively and the membership grades for data points [13].…”

Section: Algorithmic Steps For Gaussian Kernel Fuzzy C-means Clusteringmentioning

confidence: 99%

“…In this article, we will concentrate on a data-driven method. Motivated by [15] and the reality that various subgroups of diabetic patients have different BG fluctuation trends, we propose DP-RNN, a modern BG prediction method focused on recurrent neural networks (RNN) that incorporates a clustering pre-process into the classical RNN [13].…”

Section: Introductionmentioning

confidence: 99%

Dp-Rnn: Type Ii Diabetic Prediction Using Gkfcm And Rnn

Mrs.K.Gandhimathi¹

2021

TURCOMAT

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“…In this sense, plant secondary metabolites capable of mitigating the activity of enzymes targeted in the management of diabetes type II, Alzheimer’s disease and epidermal hyperpigmentation represent interesting possibilities for new drug development. Diabetes type II, the most common type of diabetes, is a chronic metabolic condition, which is characterised by hyperglycemia as a result of defective insulin secretion or functioning [ 8 ]. Alzheimer’s disease is the most common neurodegenerative geriatric condition, characterized by progressive memory impairment and cognitive deficits [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

A Study on Chemical Characterization and Biological Abilities of Alstonia boonei Extracts Obtained by Different Techniques

et al. 2022

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In the quest for novel therapeutic agents from plants, the choice of extraction solvent and technique plays a key role. In this study, the possible differences in the phytochemical profile and bioactivity (antioxidant and enzyme inhibitory activity) of the Alstonia boonei leaves and stem bark extracted using water, ethyl acetate and methanol, and different techniques, namely infusion, maceration and Soxhlet extraction, were investigated. Data collected showed that methanol extracts of both A. boonei leaves (48.34–53.08 mg gallic acid equivalent [GAE]/g dry extract) and stem bark (37.08–45.72 mg GAE/g dry extract) possessed higher phenolic content compared to the ethyl acetate extracts (leaves: 30.64–40.19 mg GAE/g; stem bark: 34.25–35.64 mg GAE/g). The methanol extracts of A. boonei leaves showed higher radical scavenging and reducing capacity, and these findings were in accordance with phenolic content results. In general, water extracts of A. boonei leaves and stem bark obtained by infusion were poor inhibitors of acetylcholinesterase, α-amylase, α-glucosidase, and tyrosinase, except for butyrylcholinesterase. The chemical profiles of the extracts were determined by UHPLC–MS and the presence of several compounds, such as phenolic acids (caffeic, chlorogenic and ferulic acids, etc.), flavonoids (rutin and isoquercetin) and flavonolignans (Cinchonain isomers). Cell viability was tested using the human peripheral blood monocytic cell line (THP-1), and the extracts were safe up to 25 μg/mL. In addition, anti-inflammatory effects were investigated with the releasing of IL-6 TNF-α and IL-1β. In particular, stem bark extracts exhibited significant anti-inflammatory effects. Data presented in this study highlight the key role of solvent choice in the extraction of bioactive secondary metabolites from plants. In addition, this study appraises the antioxidant and enzyme inhibitory action of A. boonei leaves and stem bark, which are extensively used in traditional medicine.

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