Molecular effects of supraphysiological doses of doping agents on health
Bibliographic record
Abstract
Performance-enhancing drugs (PEDs) gained wide popularity not only among sportsmen but also among specific subsets of population, such as adolescents. Apart from their claimed effects on athletic performance, they are very appealing due to the body shaping effect exerted on fat mass and fat-free mass. Besides the "underestimated" massive misuse of PEDs, the short- as well as long-term consequences of such habits remain largely unrecognized. They have been strictly associated with serious adverse effects, but molecular mechanisms are yet to be elucidated. Here, we analyze the current understanding of the molecular effects of supraphysiological doses of doping agents in healthy biological systems, at genomic and proteomic levels, in order to define the molecular sensors of organ/tissue impairment, determined by their misuse. The focus is put on the anabolic androgenic steroids (AASs), specifically testosterone (T) and its most potent derivative dihydrotestosterone (DHT), and on the peptide hormones, specifically the growth hormone (GH) and the insulin-like growth factor-1 (IGF-1). A map of molecular targets is defined and the risk incidence for human health is taken into account.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".