High‐throughput ultra‐high‐performance liquid chromatography/tandem mass spectrometry quantitation of insulin‐like growth factor‐I and leucine‐rich α‐2‐glycoprotein in serum as biomarkers of recombinant human growth hormone administration
Bibliographic record
Abstract
Insulin-like growth factor-I (IGF-I) is a known biomarker of recombinant human growth hormone (rhGH) abuse, and is also used clinically to confirm acromegaly. The protein leucine-rich alpha-2-glycoprotein (LRG) was recently identified as a putative biomarker of rhGH administration. The combination of an ACN depletion method and a 5-min ultra-high-performance liquid chromatography/tandem mass spectrometry (uHPLC/MS/MS)-based selected reaction monitoring (SRM) assay detected both IGF-I and LRG at endogenous concentrations. Four eight-point standard addition curves of IGF-I (16-2000 ng/mL) demonstrated good linearity (r(2) = 0.9991 and coefficients of variance (CVs) <13%). Serum samples from two rhGH administrations were extracted and their uHPLC/MS/MS-derived IGF-I concentrations correlated well against immunochemistry-derived values. Combining IGF-I and LRG data improved the separation of treated and placebo states compared with IGF-I alone, further strengthening the hypothesis that LRG is a biomarker of rhGH administration. Artificial neural networks (ANNs) analysis of the LRG and IGF-I data demonstrated an improved model over that developed using IGF-I alone, with a predictive accuracy of 97%, specificity of 96% and sensitivity of 100%. Receiver operator characteristic (ROC) analysis gave an AUC value of 0.98. This study demonstrates the first large scale and high throughput uHPLC/MS/MS-based quantitation of a medium abundance protein (IGF-I) in human serum. Furthermore, the data we have presented for the quantitative analysis of IGF-I suggest that, in this case, monitoring a single SRM transition to a trypsin peptide surrogate is a valid approach to protein quantitation by LC/MS/MS.
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 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 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".