Quantitation of γ<i>‐</i>aminobutyric acid in equine plasma by hydrophilic interaction liquid chromatography with tandem mass spectrometry
Bibliographic record
Abstract
γ-Aminobutyric acid is the principal inhibitory neurotransmitter in the central nervous system and regulates the neuronal excitability. There has been anecdotal evidence that γ-aminobutyric acid has been used within a few hours prior to competition in equine sports to calm down nervous horses. However, regulating the use of γ-aminobutyric acid is challenging because it is an endogenous substance in the horse. γ-Aminobutyric acid is usually present at low ng/mL levels in equine plasma; therefore, a sensitive method has to be developed to quantify these low background levels. Measuring low concentrations of endogenous γ-aminobutyric acid is essential to establish a threshold that can be used to differentiate levels attributable to exogenous administrations of γ-aminobutyric acid. A hydrophilic interaction liquid chromatography coupled with tandem mass spectrometry method was developed and validated for the quantitation of γ-aminobutyric acid in equine plasma. Calibrators were prepared in artificial surrogate matrix consisting of 35 mg/mL equine serum albumin in phosphate buffered saline. Samples were prepared by protein precipitation with acetonitrile. Utilizing this methodology, a total of 403 equine plasma samples collected post-competition from horses participating in equestrian events in Canada were analyzed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".