Evidence map›Paper›PMID 39636776›Full record

ReviewJournal of food and drug analysis2024

Unlocking early detection of Alzheimer's disease: The emerging role of nanomaterial-based optical sensors.

Chun-Hsien Chen, Hsin-Hua Liang, Chun-Chi Wang, Yi-Ting Yang, Yi-Hui Lin, Yen-Ling Chen

Abstract readReview
In one paragraph

Review in Journal of food and drug analysis, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Chun-Hsien ChenSchool of Pharmacy, College of Pharmacy, Kaohsiung Medical University, Kaohsiung 807378, Taiwan.
Hsin-Hua LiangSchool of Pharmacy, China Medical University, Taichung 406040, Taiwan.
Chun-Chi WangSchool of Pharmacy, College of Pharmacy, Kaohsiung Medical University, Kaohsiung 807378, Taiwan.
Yi-Ting YangDepartment of Chemistry and Biochemistry, National Chung Cheng University, Chia-Yi 621301, Taiwan.
Yi-Hui LinSchool of Pharmacy, China Medical University, Taichung 406040, Taiwan.
Yen-Ling ChenSchool of Pharmacy, College of Pharmacy, Kaohsiung Medical University, Kaohsiung 807378, Taiwan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Alzheimer's disease (AD) is a chronic and progressive neurodegenerative disorder that affects millions of individuals worldwide. Researchers have conducted numerous studies to find accurate biomarkers for early AD diagnosis and develop more effective treatments. The main pathological hallmarks of AD are amyloid beta and Tau proteins. Other biomarkers, such as DNA, RNA, and proteins, can also be helpful in early AD diagnosis. To diagnose and treat AD promptly, it is essential to accurately measure the concentration of biomarkers in the cerebrospinal fluid or blood. However, due to the low concentrations of these biomarkers in the body, highly sensitive analytical techniques are required. To date, sensors have become increasingly important due to their high sensitivity, swift detection, and adaptable manipulation features. These qualities make them an excellent substitute for conventional instruments. Nanomaterials are commonly employed in sensors to amplify signals and improve sensitivity. This review paper summarized the integration of nanomaterials in optical sensor systems, including colorimetric, fluorescent, and surface-enhanced Raman scattering sensors for AD biomarkers detection.

Indexed as

Alzheimer DiseaseBiomarkersBiosensing TechniquesNanostructuresAmyloid beta-PeptidesAnimalsColorimetryEarly DiagnosisHumansSpectrum Analysis, Ramantau ProteinsAmyloid beta-PeptidesBiomarkerstau Proteins

Identifiers

PMID39636776
PMCPMC11464041

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.