Evidence map›Paper›PMID 38505618›Full record

ReviewTheranostics2024

Cancer diagnosis using label-free SERS-based exosome analysis.

Yajuan Liu, Mei Li, Haisha Liu, Chao Kang, Cheng Wang

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Theranostics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.

0numbers the graph read from it
0cells of the map it votes in
32citing 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

32 citing papers in PubMed.

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  20. [Advances in exosome-targeting aptamer-screening techniques].Se pu = Chinese journal of chromatography · 2025
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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

5 authors.

Yajuan LiuKey Laboratory of Molecular Target & Clinical Pharmacology, and the NMPA & State Key Laboratory of Respiratory Disease, School of Pharmaceutical Sciences & the Fifth Affiliated Hospital, Guangzhou Medical University, 511436, Guangzhou, China.
Mei LiSchool of Chemistry and Chemical Engineering, Guizhou University, 550025, Guiyang, China.
Haisha LiuSchool of Chemistry and Chemical Engineering, Guizhou University, 550025, Guiyang, China.
Chao KangSchool of Chemistry and Chemical Engineering, Guizhou University, 550025, Guiyang, China.
Cheng WangSmurfit Institute of Genetics, Trinity College Dublin, Dublin 2, Ireland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Exosomes, carrying distinctive biomolecules reflective of their parent cell's status and origin, show promise as liquid biopsy biomarkers for cancer diagnosis. However, their clinical translation remains challenging due to their relatively low concentration in body fluids. Surface-Enhanced Raman spectroscopy (SERS) has recently gained significant attention as a label-free and sensitive technique for exosome analysis. This review explores label-free SERS for exosome detection, covering exosome isolation and characterization methods, advancements in SERS substrates, and fingerprint analysis techniques using machine learning. Furthermore, we emphasize the challenges and offer insights into the future prospects of SERS-based exosome analysis to enhance cancer diagnosis.

Indexed as

ExosomesNeoplasmsBiomarkersHumansLiquid BiopsySpectrum Analysis, RamanBiomarkersCancer diagnosisExosomelabel-free SERSRaman SpectroscopySERS

Identifiers

PMID38505618
PMCPMC10945334

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Registered trials

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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.