Fatma Nese Efil¹, Sania Qureshi^1,2,3, Nezihal Gokbulut^1,4, Kamyar Hosseini^1,3, Evren Hincal^1,4,*, Amanullah Soomro²

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 485-512, 2024, DOI:10.32604/cmes.2024.047907

Abstract The global population has been and will continue to be severely impacted by the COVID-19 epidemic. The primary objective of this research is to demonstrate the future impact of COVID-19 on those who suffer from other fatal conditions such as cancer, heart disease, and diabetes. Here, using ordinary differential equations (ODEs), two mathematical models are developed to explain the association between COVID-19 and cancer and between COVID-19 and diabetes and heart disease. After that, we highlight the stability assessments that can be applied to these models. Sensitivity analysis is used to examine how changes in certain factors impact different aspects… More >

Yamixa Delgado^1,*, Caliani Gaytan¹, Naydi Perez², Eric Miranda³, Bryan Colón Morales¹, Mónica Santos¹

Congenital Heart Disease, Vol.19, No.1, pp. 19-31, 2024, DOI:10.32604/chd.2024.046339

Abstract Background: Given the pervasive issues of obesity and diabetes both in Puerto Rico and the broader United States, there is a compelling need to investigate the intricate interplay among body mass index (BMI), pregestational, and gestational maternal diabetes, and their potential impact on the occurrence of congenital heart defects (CHD) during neonatal development. Methods: Using the comprehensive System of Vigilance and Surveillance of Congenital Defects in Puerto Rico, we conducted a focused analysis on neonates diagnosed with CHD between 2016 and 2020. Our assessment encompassed a range of variables, including maternal age, gestational age, BMI, pregestational diabetes, gestational diabetes, hypertension,… More >

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

JING JIN^1,2,#, XIE LI^1,#, TING QIU^1,#, LEI SONG¹, YUANYUE CUI¹, GUANGYA ZHANG^3,4, SHU LI², WENCHENG ZHAO^5,*

BIOCELL, Vol.47, No.7, pp. 1645-1650, 2023, DOI:10.32604/biocell.2023.029493

Abstract Background: This work aimed to reveal the expression profiles of tRNA-derived small RNAs (tsRNAs) in diabetes. Methods: Thirty-five diabetes patients and thirty-three controls were enrolled. The serum samples of 4 diabetes patients and 4 controls were subjected to tRF and tiRNA polymerase chain reaction (PCR) Array analysis. Then quantitative PCR (qPCR) validation was performed on all the samples. Bioinformatics analyses were conducted to explore their functions. Results: We found 115 tsRNAs that significantly differed between the two groups. 3′tiR-080-ProTGG(mt) was selected for further qPCR validation in all participants, and it was significantly decreased in diabetes patients compared with controls. Bioinformatics… More >

JAMES L. ROSENBERG¹, WILLIAM WOOLLEY¹, IHSAN ELNUNU¹, JULIA KAMML², DAVID S. KAMMER², CLAIRE ACEVEDO^1,3,*

BIOCELL, Vol.47, No.7, pp. 1651-1659, 2023, DOI:10.32604/biocell.2023.028014

Abstract Age and diabetes have long been known to induce an oxidative reaction between glucose and collagen, leading to the accumulation of advanced glycation end-products (AGEs) cross-links in collagenous tissues. More recently, AGEs content has been related to loss of bone quality, independent of bone mass, and increased fracture risk with aging and diabetes. Loss of bone quality is mostly attributed to changes in material properties, structural organization, or cellular remodeling. Though all these factors play a role in bone fragility disease, some common recurring patterns can be found between diabetic and age-related bone fragility. The main pattern we will discuss… More >

Niharika Gupta¹, Baijnath Kaushik¹, Mohammad Khalid Imam Rahmani^2,*, Saima Anwar Lashari^2,*

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 347-366, 2023, DOI:10.32604/cmc.2023.038864

Abstract Diabetes is one of the fastest-growing human diseases worldwide and poses a significant threat to the population’s longer lives. Early prediction of diabetes is crucial to taking precautionary steps to avoid or delay its onset. In this study, we proposed a Deep Dense Layer Neural Network (DDLNN) for diabetes prediction using a dataset with 768 instances and nine variables. We also applied a combination of classical machine learning (ML) algorithms and ensemble learning algorithms for the effective prediction of the disease. The classical ML algorithms used were Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbor (KNN),… More >

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 >