Determination of IGF-1 and IGF-2, their degradation products and synthetic analogues in urine by LC-MS/MS
Bibliographic record
Abstract
Peptide analysis in doping controls by means of nano-UPLC coupled high resolution/high mass accuracy mass spectrometry provides the state-of-the-art technique in modern sports drug testing. The present study describes a recent application of this technique for the qualitative determination of different urinary insulin-like growth factor (IGF) related peptides. After simultaneous isolation by solid phase extraction and magnetic particle-based immunoaffinity purification, target analytes (IGF-1, IGF-2, Des1-3-IGF-1, R(3)-IGF-1 and longR(3)-IGF-1) were separated by nano-liquid chromatography prior to mass spectrometric detection. Endogenously produced IGF-1 and IGF-2, as well as the degradation product Des1-3-IGF-1, were frequently detected in urine samples from healthy volunteers in a concentration range of 20-400 pg mL(-1). The impact of IGF binding proteins (IGFBPs), being also present in urine, was potentially estimated by an additional ultrafiltration step in the sample preparation procedure. The synthetic analogue longR(3)-IGF-1, which is assumed to be subject to misuse by cheating athletes, was also analysed and detected in fortified urine samples. Besides the intact molecule, an N-terminally truncated degradation product Des1-10-longR(3)-IGF-1 was identified as the more stable target for doping controls using urine samples. The method was validated for qualitative purposes considering the parameters specificity, limit of detection (20-50 pg mL(-1)), recovery (10-35%), precision (<20%), linearity, robustness and stability.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".