| Title |
Comparison of tumor markers using different detection devices
|
|---|---|
| Published in |
OncoTargets and therapy, May 2015
|
| DOI | 10.2147/ott.s79109 |
| Pubmed ID | |
| Authors |
Rong Tao, Shaohua Tu, Chong Liu, Qi Yang, Min Zhu, Jiangfan Shen |
| Abstract |
With the development of proteomics, tumor markers have attracted increasing attention for the early diagnosis and treatment of lung cancer. As biochip technology and nanotechnology continues to grow, rapid and highly sensitive joint detection of multi-tumor markers has become possible. Eighty-six patients with lung cancer and 42 healthy controls were recruited for this study. Based on analysis of the detection results, we plotted four standard tumor marker graphs, and compared the results of the highly sensitive nanogold probe and protein chip detection with the results of electrochemiluminescence immunoassay and Dickkopf-1 (DKK1) detection used in the clinic. We then analyzed the relationship between the detection results and our clinical data. Four plotted standard protein graphs all had stages with sound linear relationships. It was found in a correlation analysis of the detection results that overall the two methods showed consistency. We developed a detection method for ultra-trace protein that can detect four tumor markers, namely carcinoembryonic antigen, cytokeratin-19 fragments, neuron-specific enolase, and DKK1 in a highly sensitive way within 1.5 hours by magnifying the signal of nanogold deposition based on protein chips and nanogold probes. By comparing the results from the different detection devices, we have developed an experimental basis for detection of tumor markers in the clinic. |
Mendeley readers
The data shown below were compiled from readership statistics for 6 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Netherlands | 1 | 17% |
| Unknown | 5 | 83% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 2 | 33% |
| Student > Master | 2 | 33% |
| Other | 1 | 17% |
| Unknown | 1 | 17% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 3 | 50% |
| Medicine and Dentistry | 1 | 17% |
| Unknown | 2 | 33% |
Attention Score in Context
This research output has an Altmetric Attention Score of 6. 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 10 August 2016.
All research outputs
#6,736,990
of 25,374,647 outputs
Outputs from OncoTargets and therapy
#331
of 3,016 outputs
Outputs of similar age
#73,469
of 278,920 outputs
Outputs of similar age from OncoTargets and therapy
#8
of 49 outputs
Altmetric has tracked 25,374,647 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 3,016 research outputs from this source. They receive a mean Attention Score of 2.9. This one has done well, scoring higher than 88% 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 278,920 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 73% of its contemporaries.
We're also able to compare this research output to 49 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 83% of its contemporaries.