Evidence map›Paper›PMID 38051722›Full record

ArticlePloS one2023

Application of Skyline software for detecting prohibited substances in doping control analysis.

Hyeon-Jeong Lee, Mijin Jeon, Yoondam Seo, Inseon Kang, Wooyeon Jeong, Junghyun Son, Eugene C Yi, Hophil Min

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact, top 84% 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

0 citing papers in PubMed, 0 citations in OpenAlex.

No citing paper in PubMed yet.

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

8 authors at 2 institutions in 1 country.

Hyeon-Jeong LeeDoping Control Center, Korea Institute of Science and Technology, Seongbuk-gu, Seoul, Republic of Korea.
Mijin JeonDoping Control Center, Korea Institute of Science and Technology, Seongbuk-gu, Seoul, Republic of Korea.
Yoondam SeoDoping Control Center, Korea Institute of Science and Technology, Seongbuk-gu, Seoul, Republic of Korea.
Inseon KangDoping Control Center, Korea Institute of Science and Technology, Seongbuk-gu, Seoul, Republic of Korea.
Wooyeon JeongDoping Control Center, Korea Institute of Science and Technology, Seongbuk-gu, Seoul, Republic of Korea.
Junghyun SonDoping Control Center, Korea Institute of Science and Technology, Seongbuk-gu, Seoul, Republic of Korea.
Eugene C YiDepartment of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Yeongeon-dong, Jongno-gu, Seoul, Republic of Korea.
Hophil MinDoping Control Center, Korea Institute of Science and Technology, Seongbuk-gu, Seoul, Republic of Korea.ORCID 0000-0002-2791-406X
Korea Institute of Science and Technology · KRSeoul National University · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As the number of prohibited drugs has been progressively increasing and analytical methods for detecting such substances are renewed continuously for doping control, the need for more sensitive and accurate doping analysis has increased. To address the urgent need for high throughput and accurate analysis, liquid chromatography with tandem mass spectrometry is actively utilized in case of most of the newly designated prohibited substances. However, because all mass spectrometer vendors provide data processing software that is incapable of handling other instrumental data, it is difficult to cover all doping analysis procedures, from method development to result reporting, on one platform. Skyline is an open-source and vendor-neutral software program invented for the method development and data processing of targeted proteomics. Recently, the utilization of Skyline has been expanding for the quantitative analysis of small molecules and lipids. Herein, we demonstrated Skyline as a simple platform for unifying overall doping control, including the optimization of analytical methods, monitoring of data quality, discovery of suspected doping samples, and validation of analytical methods for detecting newly prohibited substances. For method optimization, we selected the optimal collision energies for 339 prohibited substances. Notably, 195 substances exhibited a signal intensity increase of >110% compared with the signal intensity of the original collision energy. All data related to method validation and quantitative analysis were efficiently visualized, extracted, or calculated using Skyline. Moreover, a comparison of the time consumed and the number of suspicious samples screened in the initial test procedure highlighted the advantages of using Skyline over the commercially available software TraceFinder in doping control.

Indexed as

Doping in SportsTandem Mass SpectrometryChromatography, LiquidProteomicsSoftware

Identifiers

PMID38051722
PMCPMC10697575
OpenAlexW4389362804

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.