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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".