Establishing Personalized Blood Protein Reference Ranges Using Noninvasive Microsampling and Targeted Proteomics: Implications for Antidoping Strategies
Bibliographic record
Abstract
To prevent doping practices in sports, the World Anti-Doping Agency implemented the Athlete Biological Passport (ABP) program, monitoring biological variables over time to indirectly reveal the effects of doping rather than detect the doping substance or the method itself. In the context of this program, a highly multiplexed mass spectrometry-based proteomics assay for 319 peptides corresponding to 250 proteins was developed, including proteins associated with blood-doping practices. "Baseline" expression profiles of these potential biomarkers in capillary blood (dried blood spots (DBS)) were established using multiple reaction monitoring (MRM). Combining DBS microsampling with highly multiplexed MRM assays is the best-suited technology to enhance the effectiveness of the ABP program, as it represents a cost-effective and robust alternative analytical method with high specificity and selectivity of targets in the attomole range. DBS data were collected from 10 healthy athlete volunteers over a period of 140 days (28 time points per participant). These comprehensive findings provide a personalized targeted blood proteome "fingerprint" showcasing that the targeted proteome is unique to an individual and likely comparable to a DNA fingerprint. The results can serve as a baseline for future studies investigating doping-related perturbations.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".