Evidence map›Paper›PMID 35721665›Full record

ArticleNanotheranostics2022

Surface enhanced deep Raman detection of cancer tumour through 71 mm of heterogeneous tissue.

Priyanka Dey, Alexandra Vaideanu, Sara Mosca, Marzieh Salimi, Benjamin Gardner, Francesca Palombo, Ijeoma Uchegbu, Jeremy Baumberg, Andreas Schatzlein, Pavel Matousek and 1 more

Abstract read
In one paragraph

Article in Nanotheranostics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Article
  2. Review
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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

11 authors.

Priyanka DeySchool of Physics and Astronomy, University of Exeter, Exeter EX4 4QL, UK.
Alexandra VaideanuSchool of Pharmacy, University College London, London, UK.
Sara MoscaCentral Laser Facility, Research Complex at Harwell, STFC Rutherford Appleton Laboratory, UK Research and Innovation, Harwell Campus OX11 0QX, UK.
Marzieh SalimiSchool of Physics and Astronomy, University of Exeter, Exeter EX4 4QL, UK.
Benjamin GardnerSchool of Physics and Astronomy, University of Exeter, Exeter EX4 4QL, UK.
Francesca PalomboSchool of Physics and Astronomy, University of Exeter, Exeter EX4 4QL, UK.
Ijeoma UchegbuSchool of Pharmacy, University College London, London, UK.
Jeremy BaumbergNanoPhotonics Centre, Cavendish Laboratory, Cambridge, CB3 0HE, UK.
Andreas SchatzleinSchool of Pharmacy, University College London, London, UK.
Pavel MatousekCentral Laser Facility, Research Complex at Harwell, STFC Rutherford Appleton Laboratory, UK Research and Innovation, Harwell Campus OX11 0QX, UK.
Nick StoneSchool of Physics and Astronomy, University of Exeter, Exeter EX4 4QL, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Detection of solid tumours through tissue- from depths relevant to humans- has been a significant challenge for biomedical Raman spectroscopy. The combined use of surface enhanced Raman scattering (SERS) imaging agents with deep Raman spectroscopy (DRS), i.e., surface enhanced deep Raman spectroscopy (SEDRS), offer prospects for overcoming such obstacles. In this study, we investigated the maximum detection depth through which the retrieval of SERS signal of a passively targeted biphenyl-4-thiol tagged gold nanoparticle (NP) imaging agent, injected subcutaneously into a mouse bearing breast cancer tumour, was possible. A compact 830 nm set-up with a hand-held probe and the flexibility of switching between offset, transmission and conventional Raman modalities was developed for this study.

Indexed as

Metal NanoparticlesNeoplasmsAnimalsBone and BonesGoldMiceSpectrum Analysis, RamanGoldcancer tumour detection.Plasmonic gold nanoparticlesSEDRS mouse breast cancerSERSSESORSSORSTRS

Identifiers

PMID35721665
PMCPMC9194587

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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.