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  • Open Access

    ARTICLE

    Type 2 Diabetes Risk Prediction Using Deep Convolutional Neural Network Based-Bayesian Optimization

    Alawi Alqushaibi1,2,*, Mohd Hilmi Hasan1,2, Said Jadid Abdulkadir1,2, Amgad Muneer1,2, Mohammed Gamal1,2, Qasem Al-Tashi3, Shakirah Mohd Taib1,2, Hitham Alhussian1,2

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3223-3238, 2023, DOI:10.32604/cmc.2023.035655 - 31 March 2023

    Abstract Diabetes mellitus is a long-term condition characterized by hyperglycemia. It could lead to plenty of difficulties. According to rising morbidity in recent years, the world’s diabetic patients will exceed 642 million by 2040, implying that one out of every ten persons will be diabetic. There is no doubt that this startling figure requires immediate attention from industry and academia to promote innovation and growth in diabetes risk prediction to save individuals’ lives. Due to its rapid development, deep learning (DL) was used to predict numerous diseases. However, DL methods still suffer from their limited prediction… More >

  • Open Access

    ARTICLE

    H1-antihistamine use and head and neck cancer risk in type 2 diabetes mellitus

    YI-NONG CHEN1,#, YING-LIN CHEN1,#, WAN-MING CHEN2,3, MINGCHIH CHEN2,3, BEN-CHANG SHIA2,3, JENQ-YUH KO1,4, SZU-YUAN WU2,3,5,6,7,8,9,10,11,*

    Oncology Research, Vol.31, No.1, pp. 23-34, 2023, DOI:10.32604/or.2022.028449 - 01 March 2023

    Abstract This study aimed to examine the association between the use of H1-antihistamines (AHs) and head and neck cancer (HNC) risk in patients with type 2 diabetes mellitus (T2DM). Data from the National Health Insurance Research Database of Taiwan were analyzed for the period from 2008 to 2018. A propensity-score-matched cohort of 54,384 patients each in the AH user and nonuser groups was created and analyzed using Kaplan-Meier method and Cox proportional hazards regression. The results showed that the risk of HNC was significantly lower in AH users (adjusted hazard ratio: 0.55, 95% CI: 0.48 to More >

  • Open Access

    ARTICLE

    Deep Learning Based Process Analytics Model for Predicting Type 2 Diabetes Mellitus

    A. Thasil Mohamed, Sundar Santhoshkumar*

    Computer Systems Science and Engineering, Vol.40, No.1, pp. 191-205, 2022, DOI:10.32604/csse.2022.016754 - 26 August 2021

    Abstract Process analytics is one of the popular research domains that advanced in the recent years. Process analytics encompasses identification, monitoring, and improvement of the processes through knowledge extraction from historical data. The evolution of Artificial Intelligence (AI)-enabled Electronic Health Records (EHRs) revolutionized the medical practice. Type 2 Diabetes Mellitus (T2DM) is a syndrome characterized by the lack of insulin secretion. If not diagnosed and managed at early stages, it may produce severe outcomes and at times, death too. Chronic Kidney Disease (CKD) and Coronary Heart Disease (CHD) are the most common, long-term and life-threatening diseases… More >

  • Open Access

    ARTICLE

    Diabetic nephropathy, autophagy and proximal tubule protein endocytic transport: A potentially harmful relationship

    Maximiliano GIRAUD-BILLOUD1,2,*, Claudio M. FADER1,3, Rocío AGÜERO2, Fernando EZQUER4, Marcelo EZQUER4

    BIOCELL, Vol.42, No.2, pp. 35-40, 2018, DOI:10.32604/biocell.2018.07010

    Abstract Diabetic nephropathy (DN) is the most frequent cause of chronic renal failure. Until now, the pathophysiological mechanisms that determine its development and progression have not yet been elucidated. In the present study, we evaluate the role of autophagy at early stages of DN, induced in type 2 diabetes mellitus (T2DM) mouse, and its association with proximal tubule membrane endocytic receptors, megalin and cubilin. In T2DM animals we observed a tubule-interstitial injury with significantly increased levels of urinary GGT and ALP, but an absence of tubulointerstitial fibrosis. Kidney proximal tubule cells of T2DM animals showed autophagic… More >

  • Open Access

    ARTICLE

    Mechanism Based Pharmacokinetic Pharmacodynamic Modeling of Vildagliptin as an Add-on to Metformin for Subjects with Type 2 Diabetes

    Marziyeh Eftekhari1, Omid Vahidi1,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.114, No.2, pp. 153-171, 2018, DOI:10.3970/cmes.2018.114.153

    Abstract Various drugs are used to maintain normoglycemia in subjects with type 2 diabetes mellitus. The combination of the drugs from different classes in one single tablet may enhance the effectiveness of the anti-diabetic drugs. To investigate the impact of combining drugs on the glucose regulation of subjects with type 2 diabetes, we propose a pharmacokinetic/pharmacodynamics (PK/PD) mathematical modeling approach for a combination of metformin and vildagliptin drugs. In the proposed modeling approach, two separate PK models representing oral administration of metformin and vildagliptin for diabetic subjects are interconnected to a PD model comprising a detailed More >

  • Open Access

    ARTICLE

    Research on Arterial Stiffness Status in Type 2 Diabetic Patients Based on Pulse Waveform Characteristics

    Gaoyang Li1, Xiaorui Song2, Aike Qiao3, Makoto Ohta4,5,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.117, No.2, pp. 143-155, 2018, DOI:10.31614/cmes.2018.04100

    Abstract For patients with type 2 diabetes, the evaluation of pulse waveform characteristics is helpful to understand changes in arterial stiffness. However, there is a lack of comprehensive analysis of pulse waveform parameters. Here, we aimed to investigate the changes in pulse waveform characteristics in patients with type 2 diabetes due to increased arterial stiffness. In this study, 25 patients with type 2 diabetes and 50 healthy subjects were selected based on their clinical history. Age, height, weight, blood pressure, and pulse pressure were collected as the subjects’ basic characteristics. The brachial-ankle pulse wave velocity (baPWV)… More >

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