Nitration and Increased α‐Synuclein Expression Associated With Dopaminergic Neurodegeneration In Equine Pituitary Pars Intermedia Dysfunction
Bibliographic record
Abstract
Equine pituitary pars intermedia dysfunction (PPID) is a spontaneously occurring progressive disease affecting aged horses and ponies. The pathogenesis of PPID is poorly understood, but the available evidence supports a loss of dopaminergic inhibition of the melanotropes of the pars intermedia. Horses with PPID have increased plasma concentrations of pars intermedia pro-opiomelanocortin-derived peptides that decrease in response to dopamine or dopamine agonist administration. Dopamine and dopamine metabolite concentrations are decreased in the pars intermedia of affected horses compared to age-matched control horses. Horses with disease that are treated with the dopamine agonist pergolide show improvement in clinical signs and normalisation of diagnostic test results. In the present study, immunohistochemical evaluation of pituitary and hypothalamic tissue demonstrated reduced tyrosine hydroxylase immunoreactivity in affected horses compared to age-matched and young controls, supporting the role of dopaminergic neurodegeneration in PPID. In addition, immunohistochemical evaluation revealed an increase in the oxidative stress marker, 3-nitrotyrosine and in nerve terminal protein, alpha-synuclein that colocalised in the pars intermedia of horses with disease. These findings suggest a role for nitration of overexpressed alpha-synuclein in the pathogenesis of neurodegeneration in PPID.
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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 | 0.001 |
| 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".