Prolactin in combination with interferon-β reduces disease severity in an animal model of multiple sclerosis
Bibliographic record
Abstract
Previous work has demonstrated that the hormone prolactin promotes oligodendrocyte precursor proliferation and remyelination following lysolecithin-induced demyelination of the mouse spinal cord. Prolactin, however, can elicit pro-inflammatory responses, and its use in the prototypical demyelinating and inflammatory condition, multiple sclerosis (MS), should thus be approached cautiously. Here, we sought to determine whether recombinant prolactin could alter the course of experimental autoimmune encephalomyelitis (EAE), an inflammatory animal model of MS. Consistent with previous literature, we found that prolactin activated leukocytes in vitro. Daily treatment with prolactin from around the time of onset of clinical signs, for 9 (days 9 to 17) or 25 (days 9 to 33) days did not increase clinical or histological signs of EAE over that of vehicle-treated mice. Instead, the combination of prolactin and a suboptimal dose of recombinant murine interferon-β resulted in (days 9 to 17 group) or trended towards (days 9 to 33 group), a greater amelioration of clinical signs of EAE, compared to either treatment alone or to vehicle controls. Histological analyses corroborated the clinical EAE data. These results suggest that prolactin may be beneficial when administered in combination with interferon-β in MS.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.001 |
| 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 teacher head, 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".