Alzheimer’s disease (AD) is one of the most important causes of mortality in elderly people, and it is often challenging to use traditional manual procedures when diagnosing a disease in the early stages. The successful implementation of machine learning (ML) techniques has also shown their effectiveness and its reliability as one of the better options for an early diagnosis of AD. But the heterogeneous dimensions and composition of the disease data have undoubtedly made diagnostics more difficult, needing a sufficient model choice to overcome the difficulty. Therefore, in this paper, four different 2D and 3D convolutional neural network (CNN) frameworks based on Bayesian search optimization are proposed to develop an optimized deep learning model to predict the early onset of AD binary and ternary classification on magnetic resonance imaging (MRI) scans. Moreover, certain hyperparameters such as learning rate, optimizers, and hidden units are to be set and adjusted for the performance boosting of the deep learning model. Bayesian optimization enables to leverage advantage throughout the experiments: A persistent hyperparameter space testing provides not only the output but also about the nearest conclusions. In this way, the series of experiments needed to explore space can be substantially reduced. Finally, alongside the use of Bayesian approaches, long short-term memory (LSTM) through the process of augmentation has resulted in finding the better settings of the model that too in less iterations with an relative improvement (RI) of 7.03%, 12.19%, 10.80%, and 11.99% over the four systems optimized with manual hyperparameters tuning such that hyperparameters that look more appealing from past data as well as the conventional techniques of manual selection.
Objective: To propose a theoretical formulation of engeletin-nanostructured lipid nanocarriers for improved delivery and increased bioavailability in treating Huntington’s disease (HD).Methods: We conducted a literature review of the pathophysiology of HD and the limitations of currently available medications. We also reviewed the potential therapeutic benefits of engeletin, a flavanol glycoside, in treating HD through the Keap1/nrf2 pathway. We then proposed a theoretical formulation of engeletin-nanostructured lipid nanocarriers for improved delivery across the blood-brain barrier (BBB) and increased bioavailability.Results: HD is an autosomal dominant neurological illness caused by a repetition of the cytosine-adenine-guanine trinucleotide, producing a mutant protein called Huntingtin, which degenerates the brain’s motor and cognitive functions. Excitotoxicity, mitochondrial dysfunction, oxidative stress, elevated concentration of ROS and RNS, neuroinflammation, and protein aggregation significantly impact HD development. Current therapeutic medications can postpone HD symptoms but have long-term adverse effects when used regularly. Herbal medications such as engeletin have drawn attention due to their minimal side effects. Engeletin has been shown to reduce mitochondrial dysfunction and suppress inflammation through the Keap1/NRF2 pathway. However, its limited solubility and permeability hinder it from reaching the target site. A theoretical formulation of engeletin-nanostructured lipid nanocarriers may allow for free transit over the BBB due to offering a similar composition to the natural lipids present in the body a lipid solubility and increase bioavailability, potentially leading to a cure or prevention of HD.Conclusion: The theoretical formulation of engeletin-nanostructured lipid nanocarriers has the potential to improve delivery and increase the bioavailability of engeletin in the treatment of HD, which may lead to a cure or prevention of this fatal illness.
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