APP processing and metabolism in corneal fibroblasts and epithelium as a potential biomarker for Alzheimer's disease

Choi, Seung Il; Lee, Boram; Woo, Jong Hwan; Jeong, Jang Bin; Jun, Ikhyun; Kim, Eung Kweon

doi:10.1016/j.exer.2019.03.012

Cited by 21 publications

(12 citation statements)

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“…Last, the main morbigenous type of Aβ in the brains of AD patients has to be verified and the specific and sensitive isoforms or combinations of Aβ in plasma that could highly reflect the CNS status must be found. At the same time, there are differences in the CNS and periphery between the expression of associated genes, such as APP , BACE1 (beta-secretase 1), BACE2 , PSEN1 (presenilin 1), and PSEN2 (presenilin 2), which could help to distinguish the source of Aβ[ 54 , 55 ]. For example, APP 695 and Aβ42 are mainly from the CNS, and APP 751 , APP 770 and Aβ 40 are mainly from the periphery [ 56 ].…”

Section: Aβmentioning

confidence: 99%

Biofluid Biomarkers of Alzheimer’s Disease: Progress, Problems, and Perspectives

2022

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Since the establishment of the biomarker-based A-T-N (Amyloid/Tau/Neurodegeneration) framework in Alzheimer’s disease (AD), the diagnosis of AD has become more precise, and cerebrospinal fluid tests and positron emission tomography examinations based on this framework have become widely accepted. However, the A-T-N framework does not encompass the whole spectrum of AD pathologies, and problems with invasiveness and high cost limit the application of the above diagnostic methods aimed at the central nervous system. Therefore, we suggest the addition of an “X” to the A-T-N framework and a focus on peripheral biomarkers in the diagnosis of AD. In this review, we retrospectively describe the recent progress in biomarkers based on the A-T-N-X framework, analyze the problems, and present our perspectives on the diagnosis of AD.

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Section: Aβmentioning

confidence: 99%

Biofluid Biomarkers of Alzheimer’s Disease: Progress, Problems, and Perspectives

2022

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“…AD is a progressive and very complicated neurodegenerative disease [12]. It is one of the most common causes of dementia worldwide [13]. Extracellular aggregates of Aβ plaques and intracellular aggregates of neurofibrillary tangles (NFTs) constituted hyperphosphorylated microtubule-associated-τ [14,15].…”

Section: A Pathophysiology Of Alzheimer's Diseasementioning

confidence: 99%

Alzheimer’s disease: Prevention Strategy

Kitisripanya¹

2022

ijcsrr

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Alzheimer’s disease (AD) is one of the age-related infections which can develop to Dementia and nowadays both pervasiveness and rate of dementia rise considerably with propelling age Aβ plaques are commonly found in Alzheimer’s patients. neurofibrillary tangles are irregular growth of protein in the brain. Paul Block and George Mannesco believe that Aβ plaques exist, after they discovered circular buildup. AD is eventually caused by neuronal malfunction and death. Aβ cellulose bodies are considered to be less poisonous than Aβ oligomers. A union of blood pressure in adulthood will increase their risk of dementia and Alzheimer’s disease. In the research they found out that old people with 75+ years and the following period are having more risk of dementia and AD. BMI has a bifacial association with dementia and AD has several studies. Due to the uncertain pathophysiological mechanism of Alzheimer’s disease, it is crucial for selecting a precise target population, need for a large sample size, and high cost of the prevention research, it has been a main issue to conduct primary prevention trials. Commonly, healthy seniors are a primary target demographic. Non-pharmacological treatments are helping the patient by enhancing a protective lifestyle like doing physical exercise and a healthy diet. Biomarkers have a priority role to design which group will get in preventative trials, a higher risk of progression of dementia will cut down the studies, however it will decrease the general public’s ability to find the entire community. Primary prevention will evade illness and accomplice pathologies before they arise. Additionally, secondary prevention contains screening to detect disease in its early stages. Tertiary prevention is treating an illness to avoid complex injuries and disability. In a research study, the use of drugs, vitamins, or lifestyle treatments can prevent Alzheimer’s disease, according to the natural definition term the outcome can be a lot of different paths. The possibility of clinical research of AD prevention is full of obstacles.

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“…Apart from pancreatic islets and thymus, the gallbladder (Choi et al, 2019;Sahu et al, 2009;Dutton et al, 2007) and brain (Devaskar et al, 1994;Mehran et al, 2012;Heller et al, 2010) are known to (naturally) transcribe the insulin gene. We observed insulin, glucagon/somatostatin immunopositivity in majority of the human gallbladder and brain samples that we have assessed (Figure 2A).…”

Section: Levels Of Insulin-associated Micrornas Correlate With (Pro-)insulin In Human Tissues That Naturally Transcribe the Insulin Genementioning

confidence: 99%

“…Studies comparing microRNA profiles of cells/tissues that naturally transcribe the (pro-)insulin gene (e.g. thymus, gallbladder, brain (Choi et al, 2019;Devaskar et al, 1994;Heller et al, 2010;Mehran et al, 2012;Sahu et al, 2009;Dutton et al, 2007;Joglekar et al, 2021) are also lacking. Here, we sought to identify microRNAs that fine-tune the expression of the human (pro-)insulin gene using an unbiased, machine learning discovery approach, followed by wet-lab validation.…”

Section: Introductionmentioning

confidence: 99%

Machine learning workflows identify a microRNA signature of insulin transcription in human tissues

Wong

Joglekar²,

Saini³

et al. 2021

iScience

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APP processing and metabolism in corneal fibroblasts and epithelium as a potential biomarker for Alzheimer's disease

Cited by 21 publications

References 50 publications

Biofluid Biomarkers of Alzheimer’s Disease: Progress, Problems, and Perspectives

Biofluid Biomarkers of Alzheimer’s Disease: Progress, Problems, and Perspectives

Alzheimer’s disease: Prevention Strategy

Machine learning workflows identify a microRNA signature of insulin transcription in human tissues

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