Evidence map›Paper›PMID 36741187›Full record

ArticleRSC advances2023

Evaluation of a biosensor-based graphene oxide-DNA nanohybrid for lung cancer.

Mustafa M Kadhim, Ahmed Mahdi Rheima, Zainab S Abbas, Haider Hussain Jlood, Safa K Hachim, Wesam R Kadhum, Ehsan Kianfar

Abstract read
In one paragraph

Article in RSC advances, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. Article
  2. Review
  3. Biomedical Applications of Metal-Organic Frameworks Revisited.Industrial & engineering chemistry research · 2025
    Review
  4. Review
  5. Review
  6. Review
  7. Review
  8. Article
  9. Article
  10. Review
  11. Optical properties of graphene oxide.Frontiers in chemistry · 2023
    Article
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

7 authors.

Mustafa M KadhimMedical Laboratory Techniques Department, Al-Farahidi University Baghdad 10022 Iraq.
Ahmed Mahdi RheimaDepartment of Chemistry, College of Science, Mustansiriyah University Baghdad Iraq.
Zainab S AbbasResearch Center, The University of Mashreq 10021 Baghdad Iraq.
Haider Hussain JloodPharmacy Department, Mazaya University College Thi-Qar Iraq.
Safa K HachimCollege of Technical Engineering, The Islamic University Najaf Iraq.
Wesam R KadhumDepartment of Pharmacy, Kut University College Kut 52001 Wasit Iraq.
Ehsan KianfarIstanbul Medeniyet University Istanbul Turkey ehsan_kianfar2010@yahoo.com +90 917-744-1049.ORCID https://orcid.org/0000-0002-7213-3772

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Lung cancer is nowadays among the most prevalent diseases worldwide and features the highest mortality rate among various cancers, indicating that early diagnosis of the disease is of paramount importance. Given that the conventional methods of cancer detection are expensive and time-consuming, special attention has been paid to the provision of less expensive and faster techniques. In recent years, the dramatic advances in nanotechnology and the development of various nanomaterials have led to activities in this context. Recent studies indicate that the graphene oxide (GO) nanomaterial has high potential in the design of nano biosensors for lung cancer detection owing to its unique properties. In the current article, a nano biosensor based on a DNA-GO nanohybrid is introduced to detect deletion mutations causing lung cancer. In this method, mutations were detected using a FAM-labeled DNA probe with fluorescence spectrometry. GO was synthesized according to Hummers' method and examined and confirmed using Fourier Transform Infrared (FT-IR) Spectrometry and UV-vis spectrometry methods and Transmission Electron Microscopy (TEM) images.

Identifiers

PMID36741187
PMCPMC9843741

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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.