Detecting peptidic drugs, drug candidates and analogs in sports doping: current status and future directions
Bibliographic record
Abstract
With the growing availability of mature systems and strategies in biotechnology and the continuously expanding knowledge of cellular processes and involved biomolecules, human sports drug testing has become a considerably complex field in the arena of analytical chemistry. Proving the exogenous origin of peptidic drugs and respective analogs at lowest concentration levels in biological specimens (commonly blood, serum and urine) of rather limited volume is required to pursue an action against cheating athletes. Therefore, approaches employing chromatographic-mass spectrometric, electrophoretic, immunological and combined test methods have been required and developed. These allow detecting the misuse of peptidic compounds of lower (such as growth hormone-releasing peptides, ARA-290, TB-500, AOD-9604, CJC-1295, desmopressin, luteinizing hormone-releasing hormones, synacthen, etc.), intermediate (e.g., insulins, IGF-1 and analogs, 'full-length' mechano growth factor, growth hormone, chorionic gonadotropin, erythropoietin, etc.) and higher (e.g., stamulumab) molecular mass with desired specificity and sensitivity. A gap between the technically possible detection and the day-to-day analytical practice, however, still needs to be closed.
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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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".