Evidence map›Paper›PMID 42613472›Full record

ReviewAnalytical and bioanalytical chemistry2026

Nanomaterial-enabled bioanalytical sensing for doping detection: a critical review of signal transduction, matrix effects, and translation barriers.

Shaoquan Chu, Qiyuan Jing

Abstract readReview
PubMed Publisher
In one paragraph

Review in Analytical and bioanalytical chemistry, 2026. 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
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.

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

2 authors.

Shaoquan ChuSchool of Physical Education and Health, Chongqing College of International Business and Economics, Chongqing, 401520, China.
Qiyuan JingSchool of Food Science and Engineering, South China University of Technology, Guangzhou, 510641, China. 202511087662@mail.scut.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Doping control requires rapid, field-deployable screening tools to complement laboratory-based confirmatory methods. Nanomaterial-enabled sensors offer portable and easy-to-use platforms by integrating signal amplification, molecular recognition, and small-volume sample analysis. However, despite increasing demonstrations in biological samples, many reported sensors still lack the comprehensive validation required for routine anti-doping practice, including standardized evaluation in diverse authentic samples, matrix-matched calibration, reproducibility assessment, and comparison with established analytical workflows. Therefore, this review evaluates nanomaterial-enabled doping sensors from a matrix-oriented and application-driven perspective. Noble-metal, fluorescent, carbon-based, magnetic, MOF-based, and nanozyme platforms are discussed together with integrated strip-based and wearable formats, covering plasmonic, fluorescence, electrochemical, magnetic, catalytic, SERS, LFIA, and flexible sensing strategies. Particular emphasis is placed on how biological matrices affect target capture, signal generation, quantitative reliability, and field applicability. Key translation barriers are identified, including matrix tolerance, batch-to-batch variability, insufficient standardization, limited authentic-sample validation, and weak integration with anti-doping workflows. By linking material design, sensing format, matrix-specific validation, and practical deployment requirements, this review defines a matrix-oriented framework for assessing the translational potential of nanomaterial-enabled anti-doping sensors.

Indexed as

Bioanalytical sensingDoping detectionMatrix effectsNanomaterialsTranslation challenges

Identifiers

What OpenQuestion holds

Textmetadata
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.