Reconstitution of the peripheral immune repertoire following withdrawal of fingolimod
Bibliographic record
Abstract
BACKGROUND: Following fingolimod cessation, immune reconstitution or lack thereof may have consequences for disease rebound or safety of commencing alternative therapies. OBJECTIVE: To examine the degree and profile of peripheral blood lymphocyte reconstitution following fingolimod withdrawal. METHODS: Total lymphocyte counts (TLC) and CD4+/CD8+ T-cell counts were measured in 18 multiple sclerosis (MS) patients pre-treatment, on fingolimod, and up to 8-9 months post-cessation. T-cell subsets were analyzed using flow cytometry. RESULTS: At 2-week post-fingolimod cessation, TLC reconstitution was variable and not correlated with age, treatment duration, pre-, or on-treatment TLC. Despite normalization of TLC and CD4+:CD8+ ratios over months, naive subsets remained lower and effector memory subsets higher in frequency compared with pre-treatment. Drug-induced increases in ratios of regulatory to pathogenic Th17-containing central memory populations appeared to rapidly return to baseline. CONCLUSION: Early peripheral lymphocyte reconstitution after fingolimod withdrawal remains partial and heterogeneous. Relative frequencies of circulating naive and memory T-cell subsets may not recover for many months, even when clinical laboratory tests have normalized. Analyzing specific components of the peripheral immune repertoire helps define the overall immune status of patients. To be determined is whether assessment of such immune measures will have implications for the timing and safety of commencing alternative therapies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".