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Record W4386592256 · doi:10.1002/dta.3572

Insulin‐mimetic peptides in sports drug testing

2023· article· en· W4386592256 on OpenAlexfundno aff
Andreas Thomas, Sophia Krombholz, Johanna Breuer, Katja Walpurgis, Mario Thevis

Bibliographic record

VenueDrug Testing and Analysis · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle metabolism and nutrition
Canadian institutionsnot available
FundersWorld Anti-Doping Agency
KeywordsInsulinDrugChemistryPeptideInsulin receptorAnabolismPharmacologyChromatographyBiochemistryComputational biologyMedicineBiologyInternal medicineInsulin resistance

Abstract

fetched live from OpenAlex

Because of its influence on carbohydrate metabolism and, at the same time, anti-catabolic effects, the misuse of the peptide hormone insulin and its synthetic analogs is prohibited in sports at all times according to the regulations of the World Anti-Doping Agency (WADA). The biological effects of insulin and its analogs are mediated through binding to the insulin receptor, which was also found to be activated by different peptides structurally largely unrelated to insulin. Such insulin-mimetic peptides or selective-insulin receptor modulators (SIRMs) represent a novel class of potential performance-enhancing agents, which is currently not explicitly mentioned on the WADA Prohibited List. Within this research project, advanced solid-phase extraction (SPE) and liquid chromatography coupled to high-resolution tandem mass spectrometry (LC-HRMS/MS) were employed to develop a fast, reliable, and specific assay for the detection of the insulin-mimetic peptides S597 and S519 from plasma. Method validation demonstrated a detection limit of 0.5 ng/mL and successfully illustrated the applicability of the approach to routine sports drug testing programs. Moreover, sophisticated and comprehensive in vitro metabolism experiments were conducted, and several metabolic degradation products were identified, which will enhance the information generated from future analyses of doping control 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)0.001
metaresearch head score (Gemma)0.001
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 score0.065
Threshold uncertainty score0.532

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
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 head0.016
GPT teacher head0.246
Teacher spread0.230 · 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

Citations5
Published2023
Admission routes1
Has abstractyes

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