Expanded test method for peptides >2 kDa employing immunoaffinity purification and LC‐HRMS/MS
Bibliographic record
Abstract
Bioactive peptides with an approximate molecular mass of 2-12 kDa are of considerable relevance in sports drug testing. Such peptides have been used to manipulate several potential performance-enhancing processes in the athlete's body and include for example growth hormone releasing hormones (sermorelin, CJC-1293, CJC-1295, tesamorelin), synthetic/animal insulins (lispro, aspart, glulisine, glargine, detemir, degludec, bovine and porcine insulin), synthetic ACTH (synacthen), synthetic IGF-I (longR(3) -IGF-I) and mechano growth factors (human MGF, modified human MGF, 'full-length' MGF). A combined initial test method using one analytical procedure is a desirable tool in doping controls and related disciplines as requests for higher sample throughput with utmost comprehensiveness preferably at reduced costs are constantly issued. An approach modified from an earlier assay proved fit-for-purpose employing pre-concentration of all target analytes by means of ultrafiltration, immunoaffinity purification with coated paramagnetic beads, nano-ultra high performance liquid chromatography (UHPLC) separation, and subsequent detection by means of high resolution tandem mass spectrometry. The method was shown to be applicable to blood and urine samples, which represent the most common doping control specimens. The method was validated considering the parameters specificity, recovery (11-69%), linearity, imprecision (<25%), limit of detection (5-100 pg in urine, 0.1-2 ng in plasma), and ion suppression. The analysis of administration study samples for insulin degludec, detemir, aspart, and synacthen provided the essential data for the proof-of-principle of the method.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.010 |
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".