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Potencjał zastosowania fitozwiązków z dzikorosnących roślin zielnych w prewencji i terapii wybranych chorób cywilizacyjnych

Rył Bartosz, Jasicka-Misiak Izabela

Wiadomości Chemiczne

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2024

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[Z] 78, 5-6

625--650

EN

The prevalence of individual civilisational diseases has been constantly increasing in recent years. Their occurrence is related, among other factors, to changes in lifestyle (stress, low levels of physical activity, low-quality diet), increasing urbanisation and industrialisation (environmental pollution). Lifestyledependent diseases include type 2 diabetes and Alzheimer's disease which is not only related to lifestyle - one of the main risk factors is age. The increasing life expectancy of the population associated with the development of civilisation (health, social and welfare) therefore has a significant impact on its occurrence. In 2017, the number of people with diabetes was approximately 476 million, which represented an increase of 129.7% compared to 1990 [1]. For Alzheimer's disease, incidence and prevalence increased by 147.95% and 160.84% respectively between 1990 and 2019 [2]. The economic and social costs associated with the occurrence of these diseases are enormous [3,4]. Among the drugs used in the treatment of Alzheimer's disease, compounds of natural origin that have the cholinesterase inhibitor activity - the isoquinoline alkaloid galantamine - have been successfully used [101], as well as the semi-synthetic phisostigmine derivative isolated from Physostigma venenosum – rivastigmine (Fig. 4.) [5,6]. One of the most commonly used drugs in the treatment of diabetes is metformin, a synthetic derivative of galegin isolated from Galega officinalis (Fig. 2.) [7]. Natural-derived compounds can therefore be highly active and safe preparations used in both the prevention and treatment of certain diseases. The present work aims to review and summarise information on the potential use of herbaceous wild plants: Stellaria media, Epilobium angustifolium and Chenopodium album in the prevention and therapy of diabetes and Alzheimer's disease. Promising results have been obtained in both in vitro and in vivo studies on the potential use of extracts from these plants. They exhibited protective (neuroprotective activity, protection of organs from damage in the progress of diabetes, effects on body weight control and obesity reduction) and therapeutic (effects on lowering blood glucose levels, reducing insulin resistance, inhibitory effects on cholinesterase, α-glucosidase and α-amylase) activities. Polyphenolic compounds and flavonoids were shown to be important in determining the biological activity of the extracts. Available literature data indicate a high potential for the use of extracts obtained from these plants, both in the prevention and therapy of type 2 diabetes and Alzheimer's disease.

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Pretrained models for classification of dementia degree and treatment of Alzheimer’s disease

Benbakreti Soumia, Benbakreti Samir, Benyahia Kadda, Khobzaoui Abdelkader

Przegląd Elektrotechniczny

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2024

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R. 100, nr 5

135--138

EN

The early diagnosis of Alzheimer’s disease poses a significant challenge in the health sector, and the integration of deep learning and artificial intelligence (AI) holds promising potential for enhancing early detection through the classification of dementia levels, enabling more effective disease treatment. Deep neural networks have the capacity to autonomously learn and identify discriminative characteristics associated with this pathology. In this study, three pre-trained CNN-based models are employed to classfify MRI images of Alzheimer’s patients, with ResNet18 yielding excellent results and achieving an accuracy rate of 97.3%.

PL

Wczesna diagnoza choroby Alzheimera stanowi powazne wyzwanie w sektorze zdrowia, a integracja głębokiego uczenia się i sztucznej inteligencji (AI) niesie obiecujący potencjał w zakresie poprawy wczesnego wykrywania poprzez klasyfikację poziomów demencji, umozliwiając skuteczniejsze leczenie chorób. Głębokie sieci neuronowe mają zdolność autonomicznego uczenia się i identyfikowania cech dyskryminacyjnych związanych z tą patologią. W tym badaniu do klasyfikacji obrazów MRI pacjentów z chorobą Alzheimera wykorzystano trzy wstępnie wyszkolone modele oparte na CNN, przy czym ResNet18 zapewnia doskonałe wyniki i osiąga współczynnik dokładności wynoszący 97,3%

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Development and Optimization of Deep Learning Systems for MRI Analysis in Alzheimer’s Disease Monitoring

Podolszańska Jolanta

Journal of Telecommunications and Information Technology

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2024

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nr 4

56--61

EN

Alzheimer’s disease is one of the leading causes of dementia worldwide, and its increasing prevalence presents significant diagnostic and therapeutic challenges, particularly in an aging population. Current diagnostic methods, including patient history reviews, neuropsychological tests, and MRI scans, often fail to achieve adequate sensitivity and specificity levels. In response to these challenges, this study introduces an advanced convolutional neural network (CNN) model that combines ResNet-50 and Inception V3 architectures to classify, with a high degree of precision, the stages of Alzheimer’s disease based on MRI. The model was developed and evaluated using data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and classifies MRI scans into four clinical categories representing different stages of disease severity. The evaluation results, based on the precision, sensitivity and F1 score metrics, demonstrate the effectiveness of the model. Additional augmentation techniques and differential class weighting further enhance the accuracy of the model. Visualization of results using the t-SNE method and the confusion matrix underscores the ability to distinguish between disease categories, supporting the model’s potential to aid in neurological diagnosis and classification.