C. Mallika^1,*, S. Selvamuthukumaran²

Intelligent Automation & Soft Computing, Vol.31, No.3, pp. 1873-1885, 2022, DOI:10.32604/iasc.2022.020543 - 09 October 2021

Abstract Modern healthcare systems have become smart by synergizing the potentials of wireless sensors, the medical Internet of things, and big data science to provide better patient care while decreasing medical expenses. Large healthcare organizations generate and accumulate an incredible volume of data continuously. The already daunting volume of medical information has a massive amount of diagnostic features and logged details of patients for certain diseases such as diabetes. Diabetes mellitus has emerged as along-haul fatal disease across the globe and particularly in developing countries. Exact and early diagnosis of diabetes from big medical data is… More >

Noha E. El-Attar^1,*, Bossy M. Moustafa², Wael A. Awad³

Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 325-338, 2022, DOI:10.32604/iasc.2022.019970 - 03 September 2021

Abstract DNA sequence classification is considered a significant challenge for biological researchers to scientifically analyze the enormous volumes of biological data and discover different biological features. In genomic research, classifying DNA sequences may help learn and discover the new functions of a protein. Insulin is an example of a protein that the human body produces to regulate glucose levels. Any mutations in the insulin gene sequence would result in diabetes mellitus. Diabetes is one of the widely spread chronic diseases, leading to severe effects in the longer term if diagnosis and treatment are not appropriately taken.… More >

A. Sumathi^*, S. Meganathan

Computer Systems Science and Engineering, Vol.40, No.1, pp. 313-325, 2022, DOI:10.32604/csse.2022.017484 - 26 August 2021

Abstract Gestational Diabetes Mellitus (GDM) is an illness that represents a certain degree of glucose intolerance with onset or first recognition during pregnancy. In the past few decades, numerous investigations were conducted upon early identification of GDM. Machine Learning (ML) methods are found to be efficient prediction techniques with significant advantage over statistical models. In this view, the current research paper presents an ensemble of ML-based GDM prediction and classification models. The presented model involves three steps such as preprocessing, classification, and ensemble voting process. At first, the input medical data is preprocessed in four levels… More >

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 >

XIAOMEI HAN^#, SHUYING ZHANG^#, YIFU WANG, CHANGE QI, PENGNYU GUO, YALI XU, GUANGHUI LYU^*

BIOCELL, Vol.45, No.6, pp. 1509-1519, 2021, DOI:10.32604/biocell.2021.09506 - 01 September 2021

Abstract Diabetes Mellitus is a systematic disease with complications in multi-organs, including decreased implant osseointegration and a high failure rate of dental transplants. Accumulating evidence indicates that the signaling pathway directly impacts the process of bone metabolism and inflammatory response implicated with dental implants in diabetic patients. This review summarizes the recent advance in signaling pathways regulate osseointegration and inflammatory response in dental transplantation, aiming to identify the potential therapeutic target to reduce the dental transplant failure in diabetes patients, with emphasis on the surface characteristics of the implant, inflammatory signaling, AMPK, PPARγ, WNT, ROS, and More >

SHADY G. EL-SAWAH^1,*, FAYEZ ALTHOBAITI², ADIL ALDHAHRANI³, EMAN FAYAD², MARWA A. ABDEL-DAYEM⁴, REHAB M. AMEN⁵, EL SHAIMAA SHABANA⁶, EHAB I. EL-HALLOUS⁷, HANAN M. RASHWAN¹

BIOCELL, Vol.45, No.6, pp. 1561-1568, 2021, DOI:10.32604/biocell.2021.016869 - 01 September 2021

Abstract Diabetes mellitus (DM) could negatively affect patients’ health via inducing a lot of serious functional hazards in many tissues’ cells at molecular levels. Recently, many scientists had proposed stem cell therapy being an appropriate alternative treatment protocol for numerous health threatening issues including diabetes. Therefore, the current study was designed to investigate the antioxidant potentiality of two MSCs types in alleviating tissues’ oxidative stress dramatic elevation resulting as a consequence of Type 1 DM induction. In our 4 weeks study, animals were divided into four groups: control group, STZ-diabetic group (D), D+AD-MSCs group and D+BM-MSCs… More >

A. Sumathi^1,*, S. Meganathan¹, B. Vijila Ravisankar²

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3109-3126, 2021, DOI:10.32604/cmc.2021.017612 - 24 August 2021

Abstract Gestational Diabetes Mellitus (GDM) is one of the commonly occurring diseases among women during pregnancy. Oral Glucose Tolerance Test (OGTT) is followed universally in the diagnosis of GDM diagnosis at early pregnancy which is costly and ineffective. So, there is a need to design an effective and automated GDM diagnosis and classification model. The recent developments in the field of Deep Learning (DL) are useful in diagnosing different diseases. In this view, the current research article presents a new outlier detection with deep-stacked Autoencoder (OD-DSAE) model for GDM diagnosis and classification. The goal of the… More >

Mohammed Ali Ashehri^*

Phyton-International Journal of Experimental Botany, Vol.90, No.6, pp. 1749-1762, 2021, DOI:10.32604/phyton.2021.016290 - 28 June 2021

Abstract In most of Arabian courtiers Rhazya stricta is extensively utilized in public medicine for several diseases treatment. In this study, crude alkaloid extract of R. stricta (CAERS) coated with silver nanoparticles (CAERS-AgNPs) as potential treatment against diabetes in DM animal model was evaluated. Swiss albino male rats (n = 80) were injected with STZ to induce Diabetes Mellitus type-2 (DM). DM-rats were injected different doses of CAERS or CAERS-nanoparticles (CAERS-NPs) for 2 months. The results exhibited that mRNA expression of insulin and insulin receptor was down-regulated, activity levels of antioxidant enzymes were decreased, generation of ROS mediated DNA adducts… More >

Shabib Aftab^1,2, Saad Alanazi³, Munir Ahmad¹, Muhammad Adnan Khan^4,*, Areej Fatima⁵, Nouh Sabri Elmitwally^3,6

CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1341-1357, 2021, DOI:10.32604/cmc.2021.016814 - 22 March 2021

Abstract Diabetes mellitus, generally known as diabetes, is one of the most common diseases worldwide. It is a metabolic disease characterized by insulin deficiency, or glucose (blood sugar) levels that exceed 200 mg/dL (11.1 ml/L) for prolonged periods, and may lead to death if left uncontrolled by medication or insulin injections. Diabetes is categorized into two main types—type 1 and type 2—both of which feature glucose levels above “normal,” defined as 140 mg/dL. Diabetes is triggered by malfunction of the pancreas, which releases insulin, a natural hormone responsible for controlling glucose levels in blood cells. Diagnosis… More >

Daniel Sierra-Sosa^1,*, Juan D. Arcila-Moreno², Begonya Garcia-Zapirain³, Adel Elmaghraby¹

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 1849-1861, 2021, DOI:10.32604/cmc.2021.013196 - 05 February 2021

Abstract Quantum Machine Learning (QML) techniques have been recently attracting massive interest. However reported applications usually employ synthetic or well-known datasets. One of these techniques based on using a hybrid approach combining quantum and classic devices is the Variational Quantum Classifier (VQC), which development seems promising. Albeit being largely studied, VQC implementations for “real-world” datasets are still challenging on Noisy Intermediate Scale Quantum devices (NISQ). In this paper we propose a preprocessing pipeline based on Stokes parameters for data mapping. This pipeline enhances the prediction rates when applying VQC techniques, improving the feasibility of solving classification More >