HANY M. ABD EL-LATEEF^1,2,*, SAFA H. QAHL³, EMAN FAYAD⁴, SARAH A. ALTALHI⁴, IBRAHIM JAFRI⁴, EL SHAIMAA SHABANA⁵, MARWA K. DARWISH^6,7, REHAB MAHER⁸, SAAD SHAABAN^1,9, SHADY G. EL-SAWAH^10,*

BIOCELL, Vol.47, No.8, pp. 1885-1895, 2023, DOI:10.32604/biocell.2023.030680

Abstract Background: The increasing occurrence of diabetes mellitus (DM) noted worldwide has considerably elicited concern in the recent past. DM is associated with elevated vascular complications, morbidity, mortality, and poor quality of life. In this context, mesenchymal stem cells (MSCs) have shown significant therapeutic potentialities in managing and curing type 1 DM owing to their self-renewable, immunosuppressive, and differentiation capacities. We investigated the potential action of N, N′-diphenyl-1,4-phenylenediamine (DPPD), a well-known synthetic antioxidant to enhance the therapeutic ability of the adipose-derived stem cells (AD-MSCs) in alleviating kidney and liver complications in diabetic rats. Methods: Over the four weeks of experiments, albino… More > Graphic Abstract

B. Ramesh, Kuruva Lakshmanna^*

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2513-2528, 2023, DOI:10.32604/cmes.2023.028944

Abstract Major chronic diseases such as Cardiovascular Disease (CVD), diabetes, and cancer impose a significant burden on people and healthcare systems around the globe. Recently, Deep Learning (DL) has shown great potential for the development of intelligent mobile Health (mHealth) interventions for chronic diseases that could revolutionize the delivery of health care anytime, anywhere. The aim of this study is to present a systematic review of studies that have used DL based on mHealth data for the diagnosis, prognosis, management, and treatment of major chronic diseases and advance our understanding of the progress made in this rapidly developing field. Type 2… More > Graphic Abstract

Alawi Alqushaibi^1,2,*, Mohd Hilmi Hasan^1,2, Said Jadid Abdulkadir^1,2, Amgad Muneer^1,2, Mohammed Gamal^1,2, Qasem Al-Tashi³, Shakirah Mohd Taib^1,2, Hitham Alhussian^1,2

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

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 performance due to the hyperparameters… More >

YI-NONG CHEN^1,#, YING-LIN CHEN^1,#, WAN-MING CHEN^2,3, MINGCHIH CHEN^2,3, BEN-CHANG SHIA^2,3, JENQ-YUH KO^1,4, SZU-YUAN WU^{2,3,5,6,7,8,9,10,11,*}

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

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 0.64) and the incidence rate… More >

Vaibhav Rupapara¹, Furqan Rustam², Abid Ishaq², Ernesto Lee³, Imran Ashraf^4,*

Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1931-1949, 2023, DOI:10.32604/iasc.2023.028257

Abstract Diabetes mellitus is a metabolic disease that is ranked among the top 10 causes of death by the world health organization. During the last few years, an alarming increase is observed worldwide with a 70% rise in the disease since 2000 and an 80% rise in male deaths. If untreated, it results in complications of many vital organs of the human body which may lead to fatality. Early detection of diabetes is a task of significant importance to start timely treatment. This study introduces a methodology for the classification of diabetic and normal people using an ensemble machine learning model… More >

Xuelian Du^1,#, Hao Shi^2,#, Haiyan Liu¹, Linghua Zhou¹, Anqun Xie¹, Jufang Guo^1,*

Oncologie, Vol.24, No.4, pp. 769-787, 2022, DOI:10.32604/oncologie.2022.026248

Abstract Background: Gestational diabetes mellitus (GDM) affects the health of numerous women around the world. A recent study has shown that GDM is associated with an increased incidence of cancer. In this study, we aimed to explore the possible shared mechanisms and potential common therapeutic targets between GDM and cancer. Methods: The limma package was used to identify differentially expressed genes (DEGs) in GDM. The Cytoscape plugin cytoHubba was used to screen hub genes. The CIBERSORT algorithm was used to explore the correlation between hub genes and immunity. Cox regression analysis was used to assess the relationship between protein tyrosine kinase… More >

G. Geetha^1,2,*, K. Mohana Prasad¹

Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 703-718, 2023, DOI:10.32604/iasc.2023.032530

Abstract Diabetes mellitus is a metabolic disease in which blood glucose levels rise as a result of pancreatic insulin production failure. It causes hyperglycemia and chronic multiorgan dysfunction, including blindness, renal failure, and cardiovascular disease, if left untreated. One of the essential checks that are needed to be performed frequently in Type 1 Diabetes Mellitus is a blood test, this procedure involves extracting blood quite frequently, which leads to subject discomfort increasing the possibility of infection when the procedure is often recurring. Existing methods used for diabetes classification have less classification accuracy and suffer from vanishing gradient problems, to overcome these… More >

YUEHUA SHI^1,#, QIUYING YAN^2,#, QIN LI³, WEI QIAN¹, DONGYAN QIAO¹, DONGDONG SUN², HONG YU^1,*

BIOCELL, Vol.47, No.1, pp. 165-173, 2023, DOI:10.32604/biocell.2022.023043

Abstract The placenta plays an important role in nutrient transport to maintain the growth and development of the embryo. Gestational diabetes mellitus (GDM), the most common complication during pregnancy, highly affects placental function in late gestation. Advanced glycation end-products (AGEs), a complex and heterogeneous group of compounds engaged by the receptor for AGEs (RAGE), are closely associated with diabetes-related complications. In this study, AGEs induced a decrease in the expression of tight junction (TJ) proteins in BeWo cells and increased the paracellular permeability of trophoblast cells by regulating RAGE/NF-κB. Sprague-Dawley (SD) rats injected with 100 mg/kg AGEs-rat serum albumin (RSA) via… More >

Saleh Albahli^1,2,*, Ghulam Nabi Ahmad Hassan Yar³

Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 837-853, 2022, DOI:10.32604/iasc.2022.024427

Abstract Diabetic retinopathy is an eye deficiency that affects the retina as a result of the patient having Diabetes Mellitus caused by high sugar levels. This condition causes the blood vessels that nourish the retina to swell and become distorted and eventually become blocked. In recent times, images have played a vital role in using convolutional neural networks to automatically detect medical conditions, retinopathy takes this to another level because there is need not for just a system that could determine is a patient has retinopathy, but also a system that could tell the severity of the procession and if it… More >

YANQIU LI^#,*, CHENJUN HAO^#, WEIYI CHEN, QINGJU MENG

BIOCELL, Vol.46, No.6, pp. 1565-1573, 2022, DOI:10.32604/biocell.2022.018347

Abstract This work aimed to clarify the interaction between the fetus and pregnant patients with gestational diabetes mellitus (GDM), the lipid metabolomics analysis of the fetal umbilical cord blood of GDM patients and normal pregnant women were performed to screen out the specific lipid metabolites for pathogenesis of GDM. From 2019–2020, 21 patients with GDM and 22 normal pregnant women were enrolled in Hexian Memorial Hospital, Panyu District, Guangzhou. The general information such as weight, height, age, body mass index (BMI) before pregnancy were analyzed. Non-targeted metabonomic detection and analysis were performed in umbilical cord plasma using LC-MS method. The age,… More >