A role for galanin in human and experimental inflammatory demyelination
Bibliographic record
Abstract
The neuropeptide galanin is widely expressed by many differing subsets of neurons in the nervous system. There is a marked upregulation in the levels of the peptide in a variety of nerve injury models and in the basal forebrain of humans with Alzheimer's disease. Here we demonstrate that galanin expression is specifically and markedly upregulated in microglia both in multiple sclerosis (MS) lesions and shadow plaques. Galanin expression is also upregulated in the experimental autoimmune encephalomyelitis (EAE) model of MS, although solely in oligodendrocytes. To study whether the observed increase in expression of galanin in inflammatory demyelination might modulate disease activity, we applied the EAE model to a panel of galanin transgenic lines. Over-expression of galanin in transgenic mice (Gal-OE) abolishes disease in the EAE model, whilst loss-of-function mutations in galanin or galanin receptor-2 (GalR2) increase disease severity. The pronounced effects of altered endogenous galanin or GalR2 expression on EAE disease activity may reflect a direct neuroprotective effect of the neuropeptide via activation of GalR2, similar to that previously described in a number of neuronal injury paradigms. Irrespective of the mechanism(s) by which galanin alters EAE disease activity, our findings imply that galanin/GalR2 agonists may have future therapeutic implications for MS.
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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 | 0.000 |
| 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 | 0.001 |
| 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".