The cellular prion protein in multiple sclerosis: A potential target for neurotherapeutics?
Bibliographic record
Abstract
Abstract Multiple sclerosis (MS) is a debilitating disease that affects millions. There is no known cure for the disease and neither is the cause of the disease known. Recent studies have indicated that it is a multi-factorial disease with several genes involved. Importantly, sunlight and vitamin D have been implicated in the progression of the disease. The pathogenesis of MS chiefly involves loss of oligodendrocytes, which in addition to being killed by inflammatory mediators in the CNS, also succumbs to loss of trophic support from astrocytes. Neurotrophins play an important role in myelination and the cellular prion protein (PrPC) is a key player in this process. Although the physiological roles of PrPC remain to be fully understood, increasing evidence suggests multiple roles for PrPC in regulation of cellular immunity and for its interaction with several neurotrophins that are necessary for homeostasis of the nervous system. This mini-review focuses on the findings establishing a crucial role for PrPC in the neuropathogenesis of MS, emphasizing its neuroprotective role. Since MS is a multi-factorial disease with unknown etiology and no cure, this review aims to highlight endogenous repair mechanisms mediated by PrPC that might contribute to functional recovery in MS patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".