Pathogenic<b>inflammation in the CNS of mice carrying human<i>PLP1</i>mutations</b>
Bibliographic record
Abstract
Progressive forms of multiple sclerosis lead to chronic disability, substantial decline in quality of life and reduced longevity. It is often suggested that they occur independently of inflammation. Here we investigated the disease progression in mouse models carrying PLP1 point mutations previously found in patients displaying clinical features of multiple sclerosis. These mouse models show loss-of-function of PLP1 associated with neuroinflammation; the latter leading to clinically relevant axonal degeneration, neuronal loss and brain atrophy as demonstrated by inactivation of the recombination activating gene 1. Moreover, these pathological hallmarks were substantially amplified when we attenuated immune regulation by inactivation of the programmed cell death-1 gene. Our observations support the view that primary oligodendroglial abnormalities can evoke pathogenically relevant neuroinflammation that drives neurodegeneration, as observed in some forms of multiple sclerosis but also in other, genetically-mediated neurodegenerative disorders of the human nervous system. As many potent immunomodulatory drugs have emerged during the last years, it is tempting to consider immunomodulation as a treatment option not only for multiple sclerosis, but also for so far non-treatable, genetically-mediated disorders of the nervous system accompanied by pathogenic neuroinflammation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".