Restoring immune tolerance in neuromyelitis optica
Bibliographic record
Abstract
Neuromyelitis optica spectrum disorder (NMO/SD) and its clinical variants have at their core the loss of immune tolerance to aquaporin-4 and perhaps other autoantigens. The characteristic phenotype is disruption of astrocyte function and demyelination of spinal cord, optic nerves, and particular brain regions. In this second of a 2-part article, we present further perspectives regarding the pathogenesis of NMO/SD and how this disease might be amenable to emerging technologies aimed at restoring immune tolerance to disease-implicated self-antigens. NMO/SD appears to be particularly well-suited for these strategies since aquaporin-4 has already been identified as the dominant autoantigen. The recent technical advances in reintroducing immune tolerance in experimental models of disease as well as in humans should encourage quantum leaps in this area that may prove productive for novel therapy. In this part of the article series, the potential for regulatory T and B cells is brought into focus, as are new approaches to oral tolerization. Finally, a roadmap is provided to help identify potential issues in clinical development and guide applications in tolerization therapy to solving NMO/SD through the use of emerging technologies. Each of these perspectives is intended to shine new light on potential cures for NMO/SD and other autoimmune diseases, while sparing normal host defense mechanisms.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".