Restoring immune tolerance in neuromyelitis optica
Bibliographic record
Abstract
Neuromyelitis optica (NMO) and spectrum disorder (NMO/SD) represent a vexing process and its clinical variants appear to have at their pathogenic core the loss of immune tolerance to the aquaporin-4 water channel protein. This process results in a characteristic pattern of astrocyte dysfunction, loss, and demyelination that predominantly affects the spinal cord and optic nerves. Although several empirical therapies are currently used in the treatment of NMO/SD, none has been proven effective in prospective, adequately powered, randomized trials. Furthermore, most of the current therapies subject patients to long-term immunologic suppression that can cause serious infections and development of cancers. The following is the first of a 2-part description of several key immune mechanisms in NMO/SD that might be amenable to therapeutic restoration of immune tolerance. It is intended to provide a roadmap for how potential immune tolerance restorative techniques might be applied to patients with NMO/SD. This initial installment provides a background rationale underlying attempts at immune tolerization. It provides specific examples of innovative approaches that have emerged recently as a consequence of technical advances. In several autoimmune diseases, these strategies have been reduced to practice. Therefore, in theory, the identification of aquaporin-4 as the dominant autoantigen makes NMO/SD an ideal candidate for the development of tolerizing therapies or cures for this increasingly recognized disease.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".