Evidence map›Paper›PMID 32111825›Full record

ArticleNature communications2020

Quantum scale organic semiconductors for SERS detection of DNA methylation and gene expression.

Swarna Ganesh, Krishnan Venkatakrishnan, Bo Tan

Abstract read
In one paragraph

Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing 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

21 citing papers in PubMed.

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  9. Decoding the metabolic response ofProceedings of the National Academy of Sciences of the United States of America · 2023
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  15. A bioinspired hollow g-CChemical science · 2022
    Article
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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

3 authors.

Swarna GaneshInstitute for Biomedical Engineering, Science and Technology (I BEST), Partnership between Ryerson University and St. Michael's Hospital, Toronto, ON, M5B 1W8, Canada.
Krishnan VenkatakrishnanDepartment of Mechanical and Industrial Engineering, Ultrashort Laser Nanomanufacturing Research Facility, Ryerson University, 350 Victoria Street, Toronto, ON, M5B 2K3, Canada. venkat@ryerson.ca.ORCID http://orcid.org/0000-0002-6491-3971
Bo TanDepartment of Mechanical and Industrial Engineering, Nano Bio Interface facility, Ryerson University, 350 Victoria Street, Toronto, ON, M5B 2K3, Canada.ORCID http://orcid.org/0000-0001-8141-4819

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cancer stem cells (CSC) can be identified by modifications in their genomic DNA. Here, we report a concept of precisely shrinking an organic semiconductor surface-enhanced Raman scattering (SERS) probe to quantum size, for investigating the epigenetic profile of CSC. The probe is used for tag-free genomic DNA detection, an approach towards the advancement of single-molecule DNA detection. The sensor detected structural, molecular and gene expression aberrations of genomic DNA in femtomolar concentration simultaneously in a single test. In addition to pointing out the divergences in genomic DNA of cancerous and non-cancerous cells, the quantum scale organic semiconductor was able to trace the expression of two genes which are frequently used as CSC markers. The quantum scale organic semiconductor holds the potential to be a new tool for label-free, ultra-sensitive multiplexed genomic analysis.

Indexed as

Genome, HumanSemiconductorsAnimalsBase CompositionBiocompatible MaterialsBiomarkers, TumorBiosensing TechniquesCell LineDNADNA MethylationEpigenesis, GeneticGene ExpressionHumansMiceNeoplastic Stem CellsQuantum DotsBiocompatible MaterialsBiomarkers, TumorDNA

Identifiers

PMID32111825
PMCPMC7048788

What OpenQuestion holds

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LicenceCC BY
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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.