David Amilo^1,*, Khadijeh Sadri¹, Evren Hincal^1,2, Mohamed Hafez^3,4

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2189-2222, 2025, DOI:10.32604/cmes.2025.070298 - 26 November 2025

Abstract Breast cancer’s heterogeneous progression demands innovative tools for accurate prediction. We present a hybrid framework that integrates machine learning (ML) and fractional-order dynamics to predict tumor growth across diagnostic and temporal scales. On the Wisconsin Diagnostic Breast Cancer dataset, seven ML algorithms were evaluated, with deep neural networks (DNNs) achieving the highest accuracy (97.72%). Key morphological features (area, radius, texture, and concavity) were identified as top malignancy predictors, aligning with clinical intuition. Beyond static classification, we developed a fractional-order dynamical model using Caputo derivatives to capture memory-driven tumor progression. The model revealed clinically interpretable patterns: More >

Sha Chen^1,#, Minkai Cao^1,#, Kerong Liu^2,*, Ying Gu^1,*

BIOCELL, Vol.49, No.2, pp. 181-198, 2025, DOI:10.32604/biocell.2025.058745 - 28 February 2025

Abstract The increasing prevalence of gestational diabetes mellitus (GDM) is associated with an array of pregnancy complications and enduring health challenges in both mothers and their offspring. Studies have indicated that exposure to the intrauterine environment can prompt adaptations in the offspring, thereby programming transgenerational inheritance. Physical activity during pregnancy, as a non-pharmacological intervention, mitigates metabolic risks through epigenetic modifications, mediating placental adaptations, the action of exercise factors, and gut microbiota. Here, we provide a review summarizing how regular exercise can reduce the risk of GDM and positively influence pregnancy outcomes. It also discusses the exercise-induced 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 - 20 March 2024

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,… More >