C. Mallika^1,*, S. Selvamuthukumaran²

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

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 vital for the deterrence of… 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

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. In this research, the authors… More >

A. Sumathi^*, S. Meganathan

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

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 namely, format conversion, class labeling,… 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

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 caused by T2DM. Therefore, it… 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

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 proposed OD-DSAE model is to… More >

JACQUELINE NELISIS ZANONI, RENATA VIRGINIA FERNANDES PEREIRA

BIOCELL, Vol.32, No.2, pp. 163-168, 2008, DOI:10.32604/biocell.2008.32.163

Abstract The objective of this work was to evaluate the effect of the ascorbic acid supplementation on the cellular proliferation on the ileum mucosa of diabetic rats. Fifteen 90-days rats were divided in the groups: control, diabetic and diabetic supplemented with ascorbic acid (DA). Two hours prior the sacrifice, they were injected with Vincristin. Semi-seriate histological cuts stained with HE were accomplished. About 2500 crypt cells from the intestinal mucosa were counted in order to obtain the metaphasic indexes. The height and depth of 30 villi and 30 crypts were measured for each animal, respectively. The metaphasic indexes showed no significant… More >

MARLI APARECIDA DOS SANTOS PEREIRA, MARIA CLAÚDIA BAGATIN, JACQUELINE NELISIS ZANONI

BIOCELL, Vol.30, No.2, pp. 295-300, 2006, DOI:10.32604/biocell.2006.30.295

Abstract We assessed the ascorbic acid (AA) supplementation on the myenteric neurons in the duodenum of rats. Fifteen rats with 90 days of age were divided into three groups: control (C), diabetics (D) and ascorbic acid treated diabetics (DA). After 120 days of daily treatment with AA, the duodenum was submitted to the NADH-diaphorase (NADH-d) histochemical technique, which allowed us to evaluate the neuronal density in an area of 8.96 mm² for each duodenum, and also to measure the cellular profile area of 500 neurons per group. The supplementation promoted an increase on AA levels. The neuronal density (p <0.05) was… More >

Maximiliano GIRAUD-BILLOUD^{1, 2*}, Fernando EZQUER², Javiera BAHAMONDE², Marcelo EZQUER²

BIOCELL, Vol.41, No.1, pp. 1-12, 2017, DOI:10.32604/biocell.2017.41.001

Abstract A decrease in the tubular expression of albumin endocytic transporters megalin and cubilin has been associated with diabetic nephropathy, but there are no comprehensive studies to date relating early tubulointerstitial injury and the effect of the disease on both transporters in type 1 diabetes mellitus (T1DM). We used eight-weekold male C57BL/6 mice divided into two groups; one of them received the vehicle (control group), while the other received the vehicle + 200 mg/kg streptozotocin (T1DM). Ten weeks after the injection, we evaluated plasma insulin, enzymuria, urinary vitamin D-binding protein (VDBP), tubulointerstitial fibrosis and proximal tubule histology, markers of autophagy, and… More >

Maximiliano GIRAUD-BILLOUD^1,2,*, Claudio M. FADER^1,3, 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 vesicles larger than those observed… More >