Evidence map›Paper›PMID 41070235›Full record

ArticleCurrent research in food science2025

Unveiling the global landscape of anabolic steroid residues in beef via integrated targeted quantification and high-resolution suspect screening.

Huijing Shen, Xuanyu Wu, Xinyu Ge, Chi Zhang, Rong Liu, Shuang Zhou, Yongning Wu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Current research in food science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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.

Huijing ShenSchool of Food and Health, Beijing Technology and Business University, Beijing, 100048, PR China.
Xuanyu WuNHC Key Laboratory of Food Safety Risk Assessment, Food Safety Research Unit (2019RU014) of Chinese Academy of Medical Science, China National Center for Food Safety Risk Assessment, Beijing, 100021, PR China.
Xinyu GeSchool of Food and Health, Beijing Technology and Business University, Beijing, 100048, PR China.
Chi ZhangNHC Key Laboratory of Food Safety Risk Assessment, Food Safety Research Unit (2019RU014) of Chinese Academy of Medical Science, China National Center for Food Safety Risk Assessment, Beijing, 100021, PR China.
Rong LiuSCIEX, Analytical Instrument Trading Co., Ltd, Beijing, 102200, PR China.
Shuang ZhouNHC Key Laboratory of Food Safety Risk Assessment, Food Safety Research Unit (2019RU014) of Chinese Academy of Medical Science, China National Center for Food Safety Risk Assessment, Beijing, 100021, PR China.
Yongning WuSchool of Food and Health, Beijing Technology and Business University, Beijing, 100048, PR China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Anabolic steroid (AS) residues in animal-derived foods continues to pose a significant concern due to their potential risks to food safety and human health. In this study, we developed an integrated strategy combining targeted quantification and suspect screening for the comprehensive detection of AS residues in beef. The analytical workflow included optimized solid-phase extraction followed by Ultra-high-performance liquid chromatography coupled with tandem mass spectrometry (UHPLC-MS/MS) in scheduled Multiple Reaction Monitoring mode for targeted analysis, and UHPLC coupled with high resolution mass spectrometry (UHPLC-HRMS) in data-independent acquisition mode for suspect screening. Method validation confirmed satisfactory linearity (R

Indexed as

Anabolic steroidsAnimal-derived foodFood safetyLiquid chromatography-tandem mass spectrometry (LC-MS/MS)Suspect screening

Identifiers

PMID41070235
PMCPMC12506569

What OpenQuestion holds

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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.