Computer methods and programs in biomedicine · 2025
Computational modelling for risk assessment of neurological disorder in diabetes using Hodgkin-Huxley model.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Govindaraju Divya, Subbian Sutha, Narayanan S Nambi
Аннотация
BACKGROUND: Diabetes mellitus, characterized by chronic glucose dysregulation, significantly increases the risk of neurological disorders such as cognitive decline, seizures, and Alzheimer's disease. As neurons depend on glucose for energy, fluctuations in glucose levels can disrupt sodium (Na⁺) and potassium (K⁺) ion channel dynamics, leading to altered membrane potential. Modeling these ionic changes enables the simulation of neuronal responses under glycemic extremes, providing valuable insights for risk assessment and personalized treatment.
METHOD: The methodology utilizes Support Vector Machines (SVM) and Convolutional Neural Networks (CNN) to classify hyperglycemic and hypoglycemic events based on variations in blood glucose levels. A glucose-sensing neuron model is developed using the Hodgkin-Huxley (HH) framework to examine how glycemic fluctuations influence Na⁺ and K⁺ channel conductance. The study uniquely alters maximal conductance values to precisely simulate the effects of hyper- and hypoglycemia on ion channel behaviour and neuronal excitability.
RESULTS: The blood glucose classification results indicate that the CNN classifier effectively detects hyperglycemia and hypoglycemia, achieving an accuracy of 90.23 %, sensitivity of 87.45 %, specificity of 88.56 %, and precision of 89.31 %. Computational modeling shows that hyperglycemia decreases Na⁺ currents and increases K⁺ conductance, reducing neuronal excitability. In contrast, hypoglycemia increases Na⁺ activity and decreases K⁺ conductance, leading to excessive neuronal firing and rapid action potentials.
CONCLUSION: The proposed glucose-sensing neuron model captures how glycemic variations affect Na⁺ and K⁺ conductance and neuronal excitability. Integrating machine learning with HH modeling enables risk assessment of hypoglycemia-induced neuronal hyperexcitability and seizures, as well as hyperglycemia-associated insulin resistance and long-term risk of cognitive decline and Alzheimer's disease.
METHOD: The methodology utilizes Support Vector Machines (SVM) and Convolutional Neural Networks (CNN) to classify hyperglycemic and hypoglycemic events based on variations in blood glucose levels. A glucose-sensing neuron model is developed using the Hodgkin-Huxley (HH) framework to examine how glycemic fluctuations influence Na⁺ and K⁺ channel conductance. The study uniquely alters maximal conductance values to precisely simulate the effects of hyper- and hypoglycemia on ion channel behaviour and neuronal excitability.
RESULTS: The blood glucose classification results indicate that the CNN classifier effectively detects hyperglycemia and hypoglycemia, achieving an accuracy of 90.23 %, sensitivity of 87.45 %, specificity of 88.56 %, and precision of 89.31 %. Computational modeling shows that hyperglycemia decreases Na⁺ currents and increases K⁺ conductance, reducing neuronal excitability. In contrast, hypoglycemia increases Na⁺ activity and decreases K⁺ conductance, leading to excessive neuronal firing and rapid action potentials.
CONCLUSION: The proposed glucose-sensing neuron model captures how glycemic variations affect Na⁺ and K⁺ conductance and neuronal excitability. Integrating machine learning with HH modeling enables risk assessment of hypoglycemia-induced neuronal hyperexcitability and seizures, as well as hyperglycemia-associated insulin resistance and long-term risk of cognitive decline and Alzheimer's disease.