Neuroprotective Benefits of Rosmarinus officinalis and Its Bioactives against Alzheimer’s and Parkinson’s Diseases

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(15), P. 6417 - 6417

Published: July 23, 2024

Neurodegenerative disorders (NDs) are conditions marked by progressively escalating inflammation that leads to the degeneration of neuronal structure and function. There is an increasing interest in natural compounds, especially those from pharmaceutical plants, with neuroprotective properties as part potential therapeutic interventions. Thus, rich bioactive content perennial herb rosemary (Rosmarinus officinalis) thoroughly reviewed this article, emphasis on its pleiotropic pharmacological properties, including antioxidant, anti-inflammatory, health-promoting effects. In addition, a comprehensive analysis existing scientific literature use constituents treating neurodegenerative was also conducted. Rosemary bioactives’ chemical mechanisms discussed, focusing their ability mitigate oxidative stress, reduce inflammation, modulate neurotransmitter activity. The role enhancing cognitive function, attenuating apoptosis, promoting neurogenesis outlined. Key components, such rosmarinic acid carnosic acid, highlighted for act. promising outcomes conducted pre-clinical studies or clinical trials confirm efficacy preventing alleviating Alzheimer’s Parkinson’s diseases both vitro (in cells) vivo animal models NDs). From perspective, applications rosemary’s bio-functional compounds extracts food, cosmetics, sectors presented; latter, we discuss against disorders, either alone adjuvant therapies. This paper critically evaluates these studies’ methodological approaches outcomes, providing insights into current state research identifying avenues future investigation. All findings presented herein contribute growing body support exploration candidates novel interventions, paving way more applied research.

Language: Английский

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Patient-Tailored Dementia Diagnosis with CNN-Based Brain MRI Classification

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(9), P. 4652 - 4652

Published: April 23, 2025

This study explores the potential of using convolutional neural networks (CNNs) to diagnose dementia early and manage it in an individualized way. Segmented brain magnetic resonance imaging (MRI) images from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database represented disease (AD), mild cognitive impairment (MCI), cognitively normal (CN) subjects. These classes served train, validate, test CNN-based models. The first four models were developed entirely scratch, other employed transfer learning (TL). While both approaches demonstrated high classification accuracy (93.69% on average), TL-based outperformed independently ones, achieving 97.64% compared with 89.75%. yielded information about detected type, diagnosis confidence level, gradient-weighted class activation mapping (Grad-CAM)-generated heatmaps highlighting pathologically affected regions. results indicate for enhancing detection differentiation offer a promising basis developing deep (DL)-based clinical decision support systems (CDSSs). Such could assist healthcare professionals reducing time, optimizing patient-tailored management treatment strategies, improving quality life individuals dementia.

Language: Английский

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Advancements in deep learning for early diagnosis of Alzheimer’s disease using multimodal neuroimaging: challenges and future directions

Frontiers in Neuroinformatics, Journal Year: 2025, Volume and Issue: 19

Published: May 2, 2025

Introduction Alzheimer’s disease is a progressive neurodegenerative disorder challenging early diagnosis and treatment. Recent advancements in deep learning algorithms applied to multimodal brain imaging offer promising solutions for improving diagnostic accuracy predicting progression. Method This narrative review synthesizes current literature on applications using neuroimaging. The process involved comprehensive search of relevant databases (PubMed, Embase, Google Scholar ClinicalTrials.gov ), selection pertinent studies, critical analysis findings. We employed best-evidence approach, prioritizing high-quality studies identifying consistent patterns across the literature. Results Deep architectures, including convolutional neural networks, recurrent transformer-based models, have shown remarkable potential analyzing neuroimaging data. These models can effectively structural functional modalities, extracting features associated with pathology. Integration multiple modalities has demonstrated improved compared single-modality approaches. also promise predictive modeling, biomarkers forecasting Discussion While approaches show great potential, several challenges remain. Data heterogeneity, small sample sizes, limited generalizability diverse populations are significant hurdles. clinical translation these requires careful consideration interpretability, transparency, ethical implications. future AI neurodiagnostics looks promising, personalized treatment strategies.

Language: Английский

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Multi-modality radiomics of conventional T1 weighted and diffusion tensor imaging for differentiating Parkinson’s disease motor subtypes in early-stages

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Sept. 5, 2024

Language: Английский

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Cardiovascular Medical Image and Analysis based on 3D Vision: A Comprehensive Survey

Meta-Radiology, Journal Year: 2024, Volume and Issue: unknown, P. 100102 - 100102

Published: Sept. 1, 2024

Language: Английский

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Impact of Harmonization on MRI Radiomics Feature Variability Across Preprocessing Methods for Parkinson’s Disease Motor Subtype Classification

Deleted Journal, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 11, 2024

This study aimed to assess the reproducibility of MRI-derived radiomic features across multiple preprocessing methods for classifying Parkinson's disease (PD) motor subtypes and evaluate impact ComBat harmonization on feature stability machine learning performance. T1-weighted MRI scans from 140 PD patients (70 tremor-dominant 70 postural instability gait difficulty) healthy controls were obtained Progression Markers Initiative (PPMI) database, acquired using different scanner models. Radiomic extracted 16 brain regions various pipelines. was applied a combined batch variable incorporating both models methods. Intraclass correlation coefficients (ICC) Kruskal-Wallis tests assessed before after harmonization. Feature selection performed Linear Support Vector Classifier with L1 regularization. vector classifiers used subtype classification. significantly improved all groups. The percentage showing excellent robustness (ICC ≥ 0.90) increased 40.2 56.3% First-order statistic showed highest robustness, 71.11% demonstrating ICC proportion affected by reduced following Classification accuracy dramatically, range 34-75% 89-96% AUC values similarly 0.28-0.87 0.95-0.99 enhanced classification highlights importance in radiomics research suggests potential clinical applications personalized treatment planning.

Language: Английский

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