Basis for fluctuations in lymphocyte counts in fingolimod-treated patients with multiple sclerosis
Bibliographic record
Abstract
OBJECTIVE: To determine the range of fluctuation in total lymphocyte counts (TLCs) in peripheral blood over a 4- to 7-year period in patients with MS receiving fingolimod (FTY720) and the relation between TLCs and T-cell subsets (CD4+, CD8+, CCR7+/-) that are differentially regulated in the lymph nodes by fingolimod. METHODS: TLCs were measured every 3 months in patients (n = 23) receiving fingolimod for 4 to 7 years. T-cell subset (CD4+, CD8+, and CCR7+/-) analyses were performed on whole-blood samples and/or freshly isolated or cryopreserved mononuclear cells. RESULTS: All serially studied patients had mean TLCs <0.6 × 10(9) lymphocytes/L. In 30% of patients, 20% to 40% of TLCs were >0.6 × 10(9) lymphocytes/L vs mean 4.0% for "nonfluctuator" patients. Cross-sectional analysis indicated that TLCs of 0.2-0.6 × 10(9) lymphocytes/L correlated with numbers of CD8+ effector (CCR7-) cells. For patients discontinuing therapy, TLCs between 0.6 and 1.0 × 10(9) lymphocytes/L were associated with a relative increase of CD4 T cells and reappearance of CCR7+ (CD4+ and CD8+) T cells. Analysis of cryopreserved mononuclear cell samples from patients receiving therapy with TLCs >0.6 × 10(9) lymphocytes/L indicated no differences in total CD4 or CD8+ T cells but increased proportion of CD4+CCR7+ T cells compared to samples with TLCs <0.6 × 10(9) lymphocytes/L. CONCLUSION: Fluctuations of TLCs within 0.2-0.6 × 10(9) lymphocytes/L in patients receiving fingolimod reflect changes in total CCR7-CD8+ effector cells, a population less regulated by this agent. Although less apparent than for patients discontinuing therapy, cells expected to be sequestered by this therapy may begin to re-emerge when TLC values are >0.6 × 10(9) lymphocytes/L.
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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 | 0.002 |
| 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) | 0.001 | 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".