Inhibitory and excitatory neurotransmitters in the cerebrospinal fluid of epileptic dogs
Bibliographic record
Abstract
OBJECTIVE: To determine concentrations of excitatory and inhibitory amino acids in CSF of a large number of dogs with idiopathic epilepsy or genetic epilepsy and to evaluate changes in CSF amino acid concentration with regard to drug treatment and sex. ANIMALS: 35 Labrador Retrievers with genetic epilepsy (20 male and 15 female), 94 non-Labrador Retrievers with idiopathic epilepsy (71 male and 23 female), and 20 control dogs (10 male and 10 female). PROCEDURE: Collection of CSF was performed > 72 hours after the occurrence of seizures. Cerebrospinal fluid concentrations of gamma-aminobutyric acid (GABA), glutamate (GLU), aspartate (ASP), serine, and glycine were determined by use of high performance liquid chromatography with electrochemical detection. RESULTS: CSF concentrations of GABA and GLU were significantly lower in Labrador Retrievers with genetic epilepsy (LR-group dogs) than in control-group dogs or in non-Labrador Retrievers with idiopathic epilepsy (non-LR-group dogs). The GLU-to-GABA ratio was significantly higher in LR-group dogs than in non-LR-group dogs. CSF concentrations of GLU and ASP were significantly lower when all dogs with epilepsy (non-LR- and LR-group dogs combined) were compared with control-group dogs. CONCLUSIONS AND CLINICAL RELEVANCE: A decrease in CSF concentrations of GABA appears to play a role in the pathogenesis of genetically determined epilepsy in Labrador Retrievers. However, this decrease in CSF concentrations of GABA may also be a consequence of seizure activity. The GLU-to-GABA ratio may prove to be a useful indicator of genetic epilepsy in Labrador Retrievers.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 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".