Emerging drugs: mechanism of action, mass spectrometry and doping control analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The number of compounds and doping methods in sports is in a state of constant flux. In addition to 'traditional' doping agents, such as anabolic androgenic steroids or erythropoietin, new therapeutics and emerging drugs have considerable potential for misuse in elite sport. Such compounds are commonly based on new chemical structures, and the mechanisms underlying their modes of action represent new therapeutic approaches arising from recent advances in medical research; therefore, sports drug testing procedures need to be continuously modified and complementary methods developed, preferably based on mass spectrometry, to enable comprehensive doping controls. This tutorial not only discusses emerging drugs that can be categorized as anabolic agents (selective androgen receptor modulators, SARMs), gene doping [hypoxia-inducible factor stabilizers, peroxisome-proliferator-activated receptor (PPAR)delta-agonists] and erythropoietin-mimetics (Hematide) but also compounds with potentially performance-enhancing properties that are not classified in the current list of the World Anti-Doping Agency. Compounds such as ryanodine-calstabin-complex modulators (benzothiazepines) are included, their mass spectrometric properties discussed, and current approaches in sports drug testing outlined.
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.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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 it