Unexpected additive effects of minocycline and hydroxychloroquine in models of multiple sclerosis: Prospective combination treatment for progressive disease?
Bibliographic record
Abstract
BACKGROUND: Most multiple sclerosis (MS) patients succumb to a progressive phenotype. Continued lymphocyte activity in the brain, microglia-mediated injury, iron deposition, and oxidative stress are characteristics of progressive MS. OBJECTIVE: As minocycline and hydroxychloroquine have been shown to inhibit microglia, we evaluated their effects on other outcomes relevant for progression. METHODS: Medications were evaluated in culture and in mice with acute and chronic experimental autoimmune encephalomyelitis (EAE). RESULTS: Both medications individually reduced iron neurotoxicity and a combination effect was not observed. Hydroxyl radical scavenging activity was manifested by minocycline only. Minocycline reduced T-cell proliferation more prominently than hydroxychloroquine; an aggregate effect occurred at low but not high concentrations. B-cell proliferation was mitigated to a greater extent by hydroxychloroquine and an additive effect was not evident. In EAE, suboptimal doses of minocycline and hydroxychloroquine individually delayed onset of clinical signs, while their combination suppressed clinical manifestations until treatment was stopped. In Biozzi ABH mice, a model of progressive MS, the chronic phase was beneficially altered using the combination. CONCLUSION: While minocycline and hydroxychloroquine did not manifest additive effects in most culture assays, their combination at suboptimal doses in EAE unexpectedly exceeded their individual activity. Minocycline and hydroxychloroquine combined are candidate treatments for progressive MS.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".