Quantification of intact human insulin‐like growth factor‐I in serum by nano‐ultrahigh‐performance liquid chromatography/tandem mass spectrometry
Bibliographic record
Abstract
RATIONALE: Insulin-like growth factor-I is one of the biomarkers used to detect growth hormone administration prohibited in human sport. Current testing approaches for IGF-I rely on commercial immunoassays, which may change from time to time requiring complex revalidation. Mass spectrometry (MS)-based approaches often rely on enzymatically digesting the protein and measuring specific peptide concentrations. In order to reinforce the current available methodology for IGF-I testing, a reliable and equally sensitive MS method is required for the analysis of intact protein using small sample volumes (<25 μL). METHODS: IGF-I was extracted from human serum samples by a simple protein precipitation procedure. Separation was achieved via nano-ultrahigh-performance liquid chromatography and MS analysis was conducted by nano-electrospray ionisation triple-quadrupole mass spectrometry in the selected reaction monitoring mode using a stable-isotope-labelled internal standard. RESULTS: A six-point calibration curve ranging from 50 to 1000 ng/mL of human IGF-I in rat serum was used to establish instrument response. The method provided a limit of quantification of 50 ng/mL, with intra- and inter-day precision ≤5% and intra- and inter-day accuracy ≥95%. CONCLUSIONS: A quantitative method was developed for the quantification of intact IGF-I in human serum samples. The data generated provided important information for the development of a new reference method for the growth hormone biomarker test and helped create a reliable system for monitoring peptide hormones in individual athletes, a possible extension to the athlete biological passport system. Nano-electrospray has here been shown to be sufficiently robust for routine use in an analytical laboratory, allowing for the analysis of minute sample volumes.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".