Evidence map›Paper›PMID 26110401›Full record

ReviewSensors (Basel, Switzerland)2015

Modulated Raman Spectroscopy for Enhanced Cancer Diagnosis at the Cellular Level.

Anna Chiara De Luca, Kishan Dholakia, Michael Mazilu

Open access · goldAbstract readReview
In one paragraph

Review in Sensors (Basel, Switzerland), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 1 pooled it
9.1field-weighted citation impact, top 3% of its field
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

13 citing papers in PubMed, 1 synthesis or guideline pooled it, 51 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. AccurateTheranostics · 2021
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  5. Neurophotonics · 2020
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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 at 2 institutions in 2 countries.

Anna Chiara De LucaInstitute of Protein Biochemistry, National Research Council, Via P. Castellino, 111, 80131 Naples, Italy. a.deluca@ibp.cnr.it.
Kishan DholakiaSUPA, School of Physics and Astronomy, University of St Andrews, North Haugh KY16 9SS, St Andrews, UK. kd1@st-andrews.ac.uk.
Michael MaziluSUPA, School of Physics and Astronomy, University of St Andrews, North Haugh KY16 9SS, St Andrews, UK. mm17@st-andrews.ac.uk.
University of St Andrews · GBInstitute of Protein Biochemistry · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Raman spectroscopy is emerging as a promising and novel biophotonics tool for non-invasive, real-time diagnosis of tissue and cell abnormalities. However, the presence of a strong fluorescence background is a key issue that can detract from the use of Raman spectroscopy in routine clinical care. The review summarizes the state-of-the-art methods to remove the fluorescence background and explores recent achievements to address this issue obtained with modulated Raman spectroscopy. This innovative approach can be used to extract the Raman spectral component from the fluorescence background and improve the quality of the Raman signal. We describe the potential of modulated Raman spectroscopy as a rapid, inexpensive and accurate clinical tool to detect the presence of bladder cancer cells. Finally, in a broader context, we show how this approach can greatly enhance the sensitivity of integrated Raman spectroscopy and microfluidic systems, opening new prospects for portable higher throughput Raman cell sorting.

Indexed as

Molecular ImagingSpectrum Analysis, RamanCell Line, TumorCell SeparationHumansMicrofluidic Analytical TechniquesOptical ImagingUrinary Bladder Neoplasmscancer detectioncell sensorfluorescence backgroundRaman spectroscopy

Identifiers

PMID26110401
PMCPMC4507596
OpenAlexW1563811927

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.