Affinity Sensing for Transgenes Detection in Antidoping Control
Bibliographic record
Abstract
Sports authorities fear that a new form of doping called gene doping, based on the misuse of gene therapy, represents an emerging important problem and so far no methods are available for detecting it. The World Anti-Doping Agency (WADA) has included since 2003 for the first time gene doping methods in the "Prohibited List of Substances and Methods", thus detection of this new form of doping is challenging for analytical chemists. In this work, we apply affinity-based biosensors (ABBs), in particular DNA piezoelectric sensing, for detection of target DNA sequences selected as transgenosis markers. In this work, two sequences widely used in transgenosis experiments have been identified as markers: the enhanced green fluorescence protein (EGFP) gene and the promoter of Cytomegalovirus (CMV). The biosensors are characterized in their analytical performances using synthetic oligonucleotides and amplified DNA obtained from purified plasmid used as a template. Finally they have been applied to transgenic human cell cultures (human embryonic kidney HEK-EGFP), transformed with the same plasmid and carrying the target markers. This represents the closest human real matrix available for our transgenes.
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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".