Biomarkers for Alzheimer’s Disease: Where Do We Stand and Where Are We Going?

Clara, Villa

doi:10.3390/jpm10040238

Cited by 8 publications

(5 citation statements)

References 23 publications

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“…Several biomarkers have been detected in blood and cerebrospinal fluid (CSF) and through the availability of imaging techniques [ 60 , 61 ]. However, not all known biomarkers are suitable for use in clinical practice due to their level of invasiveness and, more strictly, to their high costs, as well as the need for specialists able to interpret the results [ 62 , 63 ]. It is therefore urgent to find less invasive, more cost-effective, and easily obtainable biomarkers, and possibly biomarkers sensitive and specific for predicting disease onset and progression.…”

Section: Epigenetic Modifications As Possible Diagnostic/prognostic B...mentioning

confidence: 99%

Epigenetics in Alzheimer’s Disease: A Critical Overview

Villa,

Combi

2024

IJMS

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Epigenetic modifications have been implicated in a number of complex diseases as well as being a hallmark of organismal aging. Several reports have indicated an involvement of these changes in Alzheimer’s disease (AD) risk and progression, most likely contributing to the dysregulation of AD-related gene expression measured by DNA methylation studies. Given that DNA methylation is tissue-specific and that AD is a brain disorder, the limitation of these studies is the ability to identify clinically useful biomarkers in a proxy tissue, reflective of the tissue of interest, that would be less invasive, more cost-effective, and easily obtainable. The age-related DNA methylation changes have also been used to develop different generations of epigenetic clocks devoted to measuring the aging in different tissues that sometimes suggests an age acceleration in AD patients. This review critically discusses epigenetic changes and aging measures as potential biomarkers for AD detection, prognosis, and progression. Given that epigenetic alterations are chemically reversible, treatments aiming at reversing these modifications will be also discussed as promising therapeutic strategies for AD.

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Section: Epigenetic Modifications As Possible Diagnostic/prognostic B...mentioning

confidence: 99%

Epigenetics in Alzheimer’s Disease: A Critical Overview

Villa,

Combi

2024

IJMS

Self Cite

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“…As the pathological changes silently occur in the brain over years before the onset of symptoms, the current challenge is the search for novel biomarkers for an easy and accurate diagnosis of the disease in its initial stages. The actual diagnostic methods rely on the measures of Aβ42, phosphorylated (p-tau), and total tau (t-tau) protein levels in the cerebrospinal fluid (CSF) of patients, in combination with advanced neuroimaging techniques such as magnetic resonance imaging (MRI) and positron emission tomography [51]. Different reliable biomarkers comprising several signaling proteins in blood plasma have also been discovered that can detect Alzheimer's with approximately 90% accuracy even in patients with a mild cognitive impairment (MCI) which may later progress to AD [52].…”

Section: Diagnostic Biomarkers and Therapeuticsmentioning

confidence: 99%

Microfluidic Platforms to Unravel Mysteries of Alzheimer’s Disease: How Far Have We Come?

Prasanna

Rathee

Rahul

et al. 2021

Life

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Alzheimer’s disease (AD) is a significant health concern with enormous social and economic impact globally. The gradual deterioration of cognitive functions and irreversible neuronal losses are primary features of the disease. Even after decades of research, most therapeutic options are merely symptomatic, and drugs in clinical practice present numerous side effects. Lack of effective diagnostic techniques prevents the early prognosis of disease, resulting in a gradual deterioration in the quality of life. Furthermore, the mechanism of cognitive impairment and AD pathophysiology is poorly understood. Microfluidics exploits different microscale properties of fluids to mimic environments on microfluidic chip-like devices. These miniature multichambered devices can be used to grow cells and 3D tissues in vitro, analyze cell-to-cell communication, decipher the roles of neural cells such as microglia, and gain insights into AD pathophysiology. This review focuses on the applications and impact of microfluidics on AD research. We discuss the technical challenges and possible solutions provided by this new cutting-edge technique to understand disease-associated pathways and mechanisms.

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“…Mitochondria are powerhouses of cells that provide energy for various functions. Oxidative stress means that during multiple processes in mitochondria, reactive oxygen species (ROS) are produced, which cause DNA/RNA mutations, Alzheimer's disease [ 1 – 3 ], ageing, and cell death, as shown in Figure 1 . Image segmentation, a renowned term used in medical imaging, refers to the partitioning of digital cell images into multiple subsegments intended to analyse an idea to get something meaningful.…”

Section: Introductionmentioning

confidence: 99%

Segmentation of Drug-Treated Cell Image and Mitochondrial-Oxidative Stress Using Deep Convolutional Neural Network

Nawabi

Sheng

Abbasi

et al. 2022

Oxidative Medicine and Cellular Longevity

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Most multicellular organisms require apoptosis, or programmed cell death, to function properly and survive. On the other hand, morphological and biochemical characteristics of apoptosis have remained remarkably consistent throughout evolution. Apoptosis is thought to have at least three functionally distinct phases: induction, effector, and execution. Recent studies have revealed that reactive oxygen species (ROS) and the oxidative stress could play an essential role in apoptosis. Advanced microscopic imaging techniques allow biologists to acquire an extensive amount of cell images within a matter of minutes which rule out the manual analysis of image data acquisition. The segmentation of cell images is often considered the cornerstone and central problem for image analysis. Currently, the issue of segmentation of mitochondrial cell images via deep learning receives increasing attention. The manual labeling of cell images is time-consuming and challenging to train a pro. As a courtesy method, mitochondrial cell imaging (MCI) is proposed to identify the normal, drug-treated, and diseased cells. Furthermore, cell movement (fission and fusion) is measured to evaluate disease risk. The newly proposed drug-treated, normal, and diseased image segmentation (DNDIS) algorithm can quickly segment mitochondrial cell images without supervision and further segment the highly drug-treated cells in the picture, i.e., normal, diseased, and drug-treated cells. The proposed method is based on the ResNet-50 deep learning algorithm. The dataset consists of 414 images mainly categorised into different sets (drug, diseased, and normal) used microscopically. The proposed automated segmentation method has outperformed and secured high precision (90%, 92%, and 94%); moreover, it also achieves proper training. This study will benefit medicines and diseased cell measurements in medical tests and clinical practices.

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Biomarkers for Alzheimer’s Disease: Where Do We Stand and Where Are We Going?

Abstract: Alzheimer’s disease (AD) is an age-related neurodegenerative and progressive disorder representing the most common form of dementia in older adults [...]

Cited by 8 publications

References 23 publications

Epigenetics in Alzheimer’s Disease: A Critical Overview

Epigenetics in Alzheimer’s Disease: A Critical Overview

Microfluidic Platforms to Unravel Mysteries of Alzheimer’s Disease: How Far Have We Come?

Segmentation of Drug-Treated Cell Image and Mitochondrial-Oxidative Stress Using Deep Convolutional Neural Network

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