Evidence map›Paper›PMID 29250208›Full record

ReviewCancer nanotechnology2017

Electrochemical and optical biosensors for early-stage cancer diagnosis by using graphene and graphene oxide.

Aditya Balaji, Jin Zhang

Open access · goldAbstract readReview
In one paragraph

Review in Cancer nanotechnology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
1.2field-weighted citation impact, top 20% 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

16 citing papers in PubMed, 58 citations in OpenAlex.

  1. Article
  2. Review
  3. Fluorescent carbon dots for discriminating cell types: a review.Analytical and bioanalytical chemistry · 2024
    Review
  4. Review
  5. Review
  6. Review
  7. Article
  8. Review
  9. Review
  10. Article
  11. Review
  12. Review
  13. Review
  14. 2D Material-Based Optical Biosensor: Status and Prospect.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2022
    Review
  15. Review
  16. Review
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

2 authors at 1 institution in 1 country.

Aditya BalajiDepartment of Biomedical Engineering, University of Western Ontario, 1151 Richmond St., London, ON N6A 5B9 Canada.
Jin ZhangDepartment of Biomedical Engineering, University of Western Ontario, 1151 Richmond St., London, ON N6A 5B9 Canada.
Western University · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Conventional instruments for cancer diagnosis including magnetic resonance imaging, computed tomography scan, are expensive and require long-waiting time, whilst the outcomes have not approached to the successful early-stage diagnosis yet. Due to the special properties of graphene-based nanocomposites, e.g., good electrical and thermal conductivity, luminescence, and mechanic flexibility, these ultra-thin two-dimensional nanostructures have been extensively used as platforms for detecting biomolecules and cells. Herein, we discuss the development of two types of graphene and graphene oxide-based biosensors: electrochemical and optical, aimed for tumor detection and early diagnosis of cancer. Moreover, we highlight the challenges of their use as biosensors for cancer detection. Efficient surface modification and suitable bio-conjugation of graphene and graphene oxide is discussed, including key role in improvement of the biocompatibility, and improved performance in terms of selectivity and sensitivity towards the early diagnosis of cancer.

Indexed as

Electrochemical sensorGrapheneGraphene oxideOptical sensorProtein–protein interaction (PPI)

Identifiers

PMID29250208
PMCPMC5725514
OpenAlexW2772169959

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.