Identification of fibroblast growth factor 1 (FGF‐1) in a black market product
Bibliographic record
Abstract
The use of growth factors for accelerated healing of sports injuries is restricted under the terms of the World Anti-Doping Agency (WADA) anti-doping code. Cheating athletes have used the black market as a source of performance-enhancing substances. Drugs that currently undergo clinical trials are frequently offered--despite the unknown health risks associated with the administration of unapproved pharmaceuticals. Recently, a new growth factor (referred to as fibroblast growth factor 1/FGF-1) with known effects on the repair and regeneration of damaged tissue was detected in an unlabelled black market product confiscated by the German customs. The identification of the protein was achieved by one- and two-dimensional polyacrylamide gel electrophoresis (SDS-PAGE and 2D-PAGE), different proteolytic digestions, immunological methods and nano-liquid chromatography high-resolution/high-accuracy Orbitrap mass spectrometry. The SDS-PAGE analysis revealed slight differences concerning the molecular weight of recombinant human and black market FGF-1. Using in-gel proteolysis, a truncation or modification located at the N-terminus of the protein was suggested. These findings demonstrate that drug candidates without clinical approval can be readily obtained from the black market, regardless of potential dangerous consequences for the consumer, which corroborates the necessity of proactive and preventive doping control approaches. In that regard, physiological concentrations of blood and urine specimens collected from healthy individuals were analyzed and were found to range below 28 pg/ml in urine, while there was no detectable FGF-1 in plasma.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".