Relapsing/remitting multiple sclerosis: A speculative model and its implications for a novel treatment
Bibliographic record
Abstract
The clinical pattern in relapsing/remitting multiple sclerosis may be accounted for if an autoreactive immune response can transition back and forth between inflammatory, pathogenic, and non-inflammatory, non-pathogenic modes. Such 'back-and-forth' immune responses are rare. I speculate how such back-and-forth immune responses may arise. Understanding the nature of these different modes, and what controls their mutual transition, may help in designing strategies to favour the nonpathogenic mode, thus constituting treatment. Antigen dose is known to be critical in determining the class/subclass of primary immune responses. Observations have led us to suggest the level of antigen also similarly influences the class/subclass of on-going immune responses. I propose the relapsing, inflammatory and the remitting modes are respectively sustained by relatively low and high amounts of the responsible autoantigens, as is the case, for example, for Th1 and Th2 responses to foreign antigens. In addition, I propose more self-antigens are released during an inflammatory than during a remitting mode. The decrease in the amount of antigen released, as the response transitions from an inflammatory to a remitting mode, results in time in a decreased level of antigen and so the response again evolves towards the inflammatory mode. The inflammatory mode then leads to an increased release of antigen and so, in time, to remission. This model thus explains the transition between different modes. I outline non-invasive, testable predictions of the hypothesis. If confirmed, it may be ethical to examine whether the non-inflammatory mode can be sustained by administering myelin antigens during the remitting phase.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".