Enhancing Early Detection of Alzheimer's Disease through MRI using Explainable Artificial Intelligence DOI Creative Commons
Teuku Rizky Noviandy,

Ghifari Maulana Idroes,

Adi Purnawarman

et al.

Indonesian Journal of Case Reports, Journal Year: 2024, Volume and Issue: 2(2), P. 43 - 51

Published: Dec. 21, 2024

Alzheimer’s disease is a progressive brain disorder that causes memory loss and cognitive decline, affecting millions of people worldwide. Early detection critical for slowing the disease's progression improving patient outcomes. Magnetic Resonance Imaging (MRI) widely used to identify changes associated with AD, but subtle abnormalities in early stages are often difficult detect using traditional methods. In this study, we deep learning approach model called ResNet-50 analyze MRI scans classify patients into four categories: Non-Demented, Very Mild Demented, Moderate Demented. The was trained images, achieving an accuracy 95.63%, strong sensitivity, precision, specificity. To make model’s predictions understandable healthcare professionals, applied technique Grad-CAM, which highlights areas influenced decisions. These visual explanations help clinicians see trust reasoning behind AI's results. While performed well overall, misclassifications between adjacent were observed, likely due class imbalance changes. This study demonstrates explainable AI tools can improve disease, supporting making accurate timely diagnoses. Future work will focus on expanding dataset combining other clinical information enhance tool's reliability real-world settings.

Language: Английский

Modulation of the Neuro–Cancer Connection by Metabolites of Gut Microbiota DOI Creative Commons
Alice Njolke Mafe, Dietrich Büsselberg

Biomolecules, Journal Year: 2025, Volume and Issue: 15(2), P. 270 - 270

Published: Feb. 12, 2025

The gut-brain-cancer axis represents a novel and intricate connection between the gut microbiota, neurobiology, cancer progression. Recent advances have accentuated significant role of microbiota metabolites in modulating systemic processes that influence both brain health tumorigenesis. This paper explores emerging concept metabolite-mediated modulation within connection, focusing on key such as short-chain fatty acids (SCFAs), tryptophan derivatives, secondary bile acids, lipopolysaccharides (LPS). While microbiota's impact immune regulation, neuroinflammation, tumor development is well established, gaps remain grasping how specific contribute to neuro-cancer interactions. We discuss with potential implications for neurobiology cancer, indoles polyamines, which yet be extensively studied. Furthermore, we review preclinical clinical evidence linking dysbiosis, altered metabolite profiles, tumors, showcasing limitations research gaps, particularly human longitudinal studies. Case studies investigating microbiota-based interventions, including dietary changes, fecal transplantation, probiotics, demonstrate promise but also indicate hurdles translating these findings therapies. concludes call standardized multi-omics approaches bi-directional frameworks integrating microbiome, neuroscience, oncology develop personalized therapeutic strategies patients.

Language: Английский

Citations

1
Arsenic Exposure Induces Neural Cells Senescence and Abnormal Lipid Droplet Accumulation Leading to Social Memory Impairment in Mice DOI
Bo Zhang, Junhong Chen, Jiaojiao Wang

et al.

Environmental Pollution, Journal Year: 2025, Volume and Issue: 368, P. 125779 - 125779

Published: Jan. 31, 2025

Language: Английский

Citations

0
Modulating lipid droplet dynamics in neurodegeneration: an emerging area of molecular pharmacology DOI

RS Verma,

Prateek Sharma, Veerta Sharma

et al.

Molecular Biology Reports, Journal Year: 2025, Volume and Issue: 52(1)

Published: March 3, 2025

Language: Английский

Citations

0
Lipid droplets deposition in perihematoma tissue is associated with neurological dysfunction after intracerebral hemorrhage DOI

Zhangze Wu,

Quan Zhao, Ziqi Hu

et al.

Neuroreport, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 20, 2025

Secondary brain injury following intracerebral hemorrhage (ICH) significantly reduces patients’ quality of life due to impaired neurological function. Lipid droplets are implicated in secondary various central nervous system diseases. Thus, the role and mechanisms lipid post-ICH require further investigation. We analyzed changes genes related metabolism tissue ICH mice. around hematoma were detected by BODIPY staining. Mice received intraperitoneal injections Triacsin C (10 mg/kg, once daily) after ICH. Subsequently, neuronal damage was evaluated using TUNEL Nissl staining, ethological tests assessed sensorimotor After ICH, notable occurred pathways (Plin2, Ucp2, Apoe), a large number accumulated hematoma. reduced deposition, decreased damage, improved sensory motor functions. Peripheral administration prevent formation can greatly reduce nerve enhance Our findings indicate that targeting could be promising treatment for

Language: Английский

Citations

0
Lipid Droplet in Lipodystrophy and Neurodegeneration DOI

Priyatama Behera,

Monalisa Mishra

Biology of the Cell, Journal Year: 2025, Volume and Issue: 117(4)

Published: April 1, 2025

ABSTRACT Lipid droplets are ubiquitous yet distinct intracellular organelles that gaining attention for their uses outside of energy storage. Their formation, role in the physiological function, and onset pathology have been recently. structure, synthesis, turnover play dynamic roles both lipodystrophy neurodegeneration. Factors like development, aging, inflammation, cellular stress regulate synthesis lipid droplets. The biogenesis has a critical reducing stress. droplets, response to stress, sequester hazardous lipids into neutral core, preserving redox balance while guarding against lipotoxicity. Thus, maintenance droplet homeostasis adipose tissue, CNS, other body tissues is essential maintaining organismal health. Insulin resistance, hypertriglyceridemia, accumulation severe metabolic abnormalities accompany lipodystrophy‐related fat deficit. Accumulation detected almost all neurodegenerative diseases Alzheimer's, Parkinson's, Huntington's, Hereditary spastic paraplegia. Hence, regulation can be used as an alternative approach treatment several diseases. current review summarizes composition, biogenesis, with emphasis on factors responsible importance disease.

Language: Английский

Citations

0
Enhancing Early Detection of Alzheimer's Disease through MRI using Explainable Artificial Intelligence DOI Creative Commons
Teuku Rizky Noviandy,

Ghifari Maulana Idroes,

Adi Purnawarman

et al.

Indonesian Journal of Case Reports, Journal Year: 2024, Volume and Issue: 2(2), P. 43 - 51

Published: Dec. 21, 2024

Alzheimer’s disease is a progressive brain disorder that causes memory loss and cognitive decline, affecting millions of people worldwide. Early detection critical for slowing the disease's progression improving patient outcomes. Magnetic Resonance Imaging (MRI) widely used to identify changes associated with AD, but subtle abnormalities in early stages are often difficult detect using traditional methods. In this study, we deep learning approach model called ResNet-50 analyze MRI scans classify patients into four categories: Non-Demented, Very Mild Demented, Moderate Demented. The was trained images, achieving an accuracy 95.63%, strong sensitivity, precision, specificity. To make model’s predictions understandable healthcare professionals, applied technique Grad-CAM, which highlights areas influenced decisions. These visual explanations help clinicians see trust reasoning behind AI's results. While performed well overall, misclassifications between adjacent were observed, likely due class imbalance changes. This study demonstrates explainable AI tools can improve disease, supporting making accurate timely diagnoses. Future work will focus on expanding dataset combining other clinical information enhance tool's reliability real-world settings.

Language: Английский

Citations

0