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A study on volatile organic compounds emitted by in-vitro lung cancer cultured cells using gas sensor array and SPME-GCMS

Overview of attention for article published in BMC Cancer, April 2018
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (80th percentile)
  • High Attention Score compared to outputs of the same age and source (85th percentile)

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1 news outlet
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2 X users

Citations

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Readers on

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136 Mendeley
Title
A study on volatile organic compounds emitted by in-vitro lung cancer cultured cells using gas sensor array and SPME-GCMS
Published in
BMC Cancer, April 2018
DOI 10.1186/s12885-018-4235-7
Pubmed ID
Authors

Reena Thriumani, Ammar Zakaria, Yumi Zuhanis Has-Yun Hashim, Amanina Iymia Jeffree, Khaled Mohamed Helmy, Latifah Munirah Kamarudin, Mohammad Iqbal Omar, Ali Yeon Md Shakaff, Abdul Hamid Adom, Krishna C. Persaud

Abstract

Volatile organic compounds (VOCs) emitted from exhaled breath from human bodies have been proven to be a useful source of information for early lung cancer diagnosis. To date, there are still arguable information on the production and origin of significant VOCs of cancer cells. Thus, this study aims to conduct in-vitro experiments involving related cell lines to verify the capability of VOCs in providing information of the cells. The performances of e-nose technology with different statistical methods to determine the best classifier were conducted and discussed. The gas sensor study has been complemented using solid phase micro-extraction-gas chromatography mass spectrometry. For this purpose, the lung cancer cells (A549 and Calu-3) and control cell lines, breast cancer cell (MCF7) and non-cancerous lung cell (WI38VA13) were cultured in growth medium. This study successfully provided a list of possible volatile organic compounds that can be specific biomarkers for lung cancer, even at the 24th hour of cell growth. Also, the Linear Discriminant Analysis-based One versus All-Support Vector Machine classifier, is able to produce high performance in distinguishing lung cancer from breast cancer cells and normal lung cells. The findings in this work conclude that the specific VOC released from the cancer cells can act as the odour signature and potentially to be used as non-invasive screening of lung cancer using gas array sensor devices.

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 136 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 136 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 23 17%
Student > Bachelor 16 12%
Researcher 13 10%
Student > Master 13 10%
Student > Doctoral Student 7 5%
Other 24 18%
Unknown 40 29%
Readers by discipline Count As %
Engineering 16 12%
Chemistry 16 12%
Medicine and Dentistry 14 10%
Biochemistry, Genetics and Molecular Biology 11 8%
Agricultural and Biological Sciences 6 4%
Other 24 18%
Unknown 49 36%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 10. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 09 December 2019.
All research outputs
#3,203,047
of 24,226,848 outputs
Outputs from BMC Cancer
#705
of 8,608 outputs
Outputs of similar age
#64,955
of 332,863 outputs
Outputs of similar age from BMC Cancer
#31
of 221 outputs
Altmetric has tracked 24,226,848 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 8,608 research outputs from this source. They receive a mean Attention Score of 4.5. This one has done particularly well, scoring higher than 91% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 332,863 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 80% of its contemporaries.
We're also able to compare this research output to 221 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 85% of its contemporaries.