Detection of insulin analogues and large peptides >2 kDa in urine
Bibliographic record
Abstract
Insulin analogues and large bioactive peptides may be used by athletes to enhance performance and are banned by the World Anti-Doping Agency (WADA). In addition to insulin analogues, the large peptides include a structurally diverse set of peptides including analogues of growth hormone releasing hormone (GHRH), insulin-like growth factor-1 (IGF-1), and mechano-growth factor (MGF). Detection of this class of peptides is difficult due to their absorptive losses and presence at very low concentrations in urine. In this report, a high throughput method is described that allows sensitive detection of four classes of large peptides in one assay. Sample extraction is performed by ultrafiltration to concentrate the urine followed by solid phase extraction in a 96-well micro-elution plate. Peptides in the urine samples are detected on a triple quadrupole mass spectrometer coupled to standard flow liquid chromatography. The method was validated and evaluated for limit of detection, limit of identification, specificity, precision, carryover, recovery, matrix interference, and post-extraction stability. The limit of detection for insulin analogues is between 5 and 25 pg/ml and between 5 and 50 pg/ml for the other peptide classes. Specificity was good with no detection of interfering peaks in blank urine samples. Carryover from a high concentration sample was not observed and the post-extraction stability was between 77% and 107%. The method was able to detect insulin analogues in three diabetic urine samples. Increased screening for this class of peptides will improve detection and deterrence.
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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 | 0.000 |
| 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".