Gene expression profiling in human whole blood samples after controlled testosterone application and exercise
Bibliographic record
Abstract
Doping with anabolic agents is regulated within a number of sports. Testosterone and its functional analogs are popular compounds for increasing muscle mass, physical performance, recovery, and reducing body fat. While routine tests for anabolic drugs exist (e.g. hair, urine, and blood analysis), the aim of the present study is to determine specific gene expression profiles (induced by testosterone and exercise) which may be used as effective biomarkers to determine the use of anabolic drugs. In this study, whole blood samples of 19 male volunteers were analyzed by semi-quantitative real-time polymerase chain reaction (RT-PCR) for gene expression profiles in the context of exercise and transdermal testosterone application (1.5 mg/kg body weight). The hormone application was monitored by urine and saliva analysis for testosterone. Both urinary and saliva levels indicate that transdermal testosterone application leads to an increase of testosterone, especially after exercise. RT-PCR results showed a clear variation in the expression of target genes as well as established housekeeping genes. Only one of the nine common housekeeping genes, cyclophilin b (PPIB), appears to be independent of both exercise and testosterone. Out of 14 candidate genes, five are unregulated; all others were more or less influenced by the mentioned variables. Only interleukin-6 appeared to be exclusively dependent on long-term testosterone application. This study indicates that many genes are not influenced by testosterone alone while exercise modulates gene expression in whole blood samples. As such, exercise must be considered when validating gene expression techniques for doping analysis.
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 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".