Mass Spectrometry-Based Characterization of New Drugs and Methods of Performance Manipulation in 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
Efficient and comprehensive sports drug testing necessitates frequent updating and proactive, preventive anti-doping research, and the early implementation of new, emerging drugs into routine doping controls is an essential aspect. Several new drugs and drug candidates with potential for abuse, including so-called Rycals (ryanodine receptor calstabin complex stabilizers, for example, S-107), hypoxia-inducible factor (HIF) stabilizers, and peroxisome-proliferator-activated receptor (PPAR) delta agonists (for example, GW1516), were studied using different mass spectrometry- and ion mobility-based approaches, and their gas phase dissociation behaviors were elucidated. The detailed knowledge of fragmentation routes allows a more rapid identification of metabolites and structurally related, presumably "tailor-made", analogs potentially designed for doping purposes. The utility of product ion characterization is demonstrated in particular with GW1516, for which oxidation products were readily identified in urine samples by means of diagnostic fragment ions as measured using high resolution/high accuracy mass spectrometry and higher energy collision-induced dissociation (HCD).
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 | 0.002 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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