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Record W2019649855 · doi:10.1055/s-2007-989369

Discrimination of Recombinant and Endogenous Urinary Erythropoietin by Calculating Relative Mobility Values from SDS Gels

2007· article· en· W2019649855 on OpenAlexaff
Maxie Kohler, Christiane Ayotte, Philippe Desharnais, Ulrich Flenker, S.L. Ludke, Mario Thevis, E. Völker‐Schänzer, Wilhelm Schänzer

Bibliographic record

VenueInternational Journal of Sports Medicine · 2007
Typearticle
Languageen
FieldMedicine
TopicErythropoietin and Anemia Treatment
Canadian institutionsInstitut National de la Recherche Scientifique
FundersAmgen
KeywordsErythropoietinRecombinant DNAEndogenyIsoelectric focusingErythropoiesisUrinary systemUrineAnalyteExcretionEndocrinologyMedicineChemistryInternal medicineChromatographyBiochemistryEnzymeAnemia

Abstract

fetched live from OpenAlex

Erythropoietin (EPO) promotes the production of red blood cells, the key factor in the regulation of the oxygen transport, and has been abused by athletes for performance enhancement in endurance sports. Current methods to detect EPO misuse are based on isoelectric focussing (IEF), double blotting, and chemiluminescence detection. A new approach utilizing SDS-PAGE mobilities of target analytes is presented. Employing two internal standards (novel erythropoiesis stimulating protein and recombinant rat EPO), the assay provides a tool which allows the calculation of relative mobility values for endogenous urinary EPO and recombinant epoetins (e.g., Dynepo) and, thus, the distinction of these analytes in doping control samples. A reference group of 53 healthy volunteers and samples originating from a Dynepo (epoetin delta) excretion study conducted with a single person were analyzed and led to a significant discrimination of endogenous urinary and recombinant EPO. A clear differentiation was accomplished over a period of four days post-administration of a single injection of 50 IU/kg body weight. Hence, the method may be useful as a screening procedure in doping control or as complementary confirmation tool to the established IEF assay.

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)0.001
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 score0.302
Threshold uncertainty score0.471

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.023
GPT teacher head0.309
Teacher spread0.286 · 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

Citations88
Published2007
Admission routes1
Has abstractyes

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