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Record W2898580896 · doi:10.1002/dta.2528

Detection of erythropoiesis stimulating agents in urine samples using a capillary Western system

2018· article· en· W2898580896 on OpenAlexaff
Philippe Desharnais, Jean‐François Naud, Christiane Ayotte

Bibliographic record

VenueDrug Testing and Analysis · 2018
Typearticle
Languageen
FieldMedicine
TopicErythropoietin and Anemia Treatment
Canadian institutionsInstitut National de la Recherche Scientifique
FundersPurdue Climate Change Research Center, Purdue University
KeywordsUrineErythropoiesisStandardizationChromatographyCapillary electrophoresisCarbohydrate deficient transferrinMedicineComputer scienceChemistryInternal medicineBiochemistry

Abstract

fetched live from OpenAlex

The presence of erythropoiesis stimulating agents (ESAs) in the urine samples collected from athletes is detected using traditional Western blotting following either size-based separation (SDS/SAR-PAGE) or isoelectric focusing (IEF). Although there is an important testing effort, there is little doubt that ESAs are still abused in sports and that reducing the costs of the tests might increase the number of tests and improve deterrence. The capillary electrophoresis system developed by Protein Simple may be useful to this end. This platform is fully automated and could be easily implemented in anti-doping laboratories, which would contribute to the improvement of the overall assay performance and standardization of the method. Such an automated system could be of interest during major sports events, such as the Olympic Games, where a high number of samples needs to be analyzed in a short period of time. From the experiments conducted so far, we conclude that the technique is promising, with the sensitivity and reproducibility needed to screen ESAs in human urine samples.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.315
Threshold uncertainty score0.740

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.055
GPT teacher head0.311
Teacher spread0.256 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations10
Published2018
Admission routes1
Has abstractyes

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