Lymphocyte subset abnormalities in early severe scleroderma favor a Th2 phenotype and are not altered by prior immunosuppressive therapy
Bibliographic record
Abstract
OBJECTIVES: The Scleroderma: Cyclophosphamide or Transplantation (SCOT) trial compared hematopoietic stem cell transplant to CYC treatment in patients with early SSc with progressive skin and lung or kidney involvement. Here we describe lymphocyte phenotype abnormalities at study entry and the relation to prior DMARD therapy. METHODS: Lymphocyte subsets (n = 26) measured by flow cytometry were compared in 123 heathy controls and 71 SCOT participants, including those given (n = 57) or not given (n = 14) DMARDs within 12 months of randomization. RESULTS: Compared with healthy controls, individuals with SSc showed significant reductions in central memory CD8 T cells, activated total and CD4 T cells, γ/δ T cells, memory B cells, myeloid and plasmacytoid dendritic cells and FOXP3+CD25+ Treg cells and increases in naïve CD4 T cells, effector memory CD4 T cells and effector CD8 T cells. A greater bias towards a IL-4+ Th2/T cytotoxic 2 (Tc2) phenotype based on the Th2:Th1 CD4 ratio and Tc2:Tc1 CD8 T cells was also found. Notably, no difference in any lymphocyte subset was observed between those given or not given prior DMARDs. CONCLUSIONS: In patients with early, severe SSc, significant lymphocyte subset abnormalities were observed. Prior treatment with immunosuppressive therapy did not impact the immunophenotype, suggesting that lymphocyte disturbances in scleroderma appeared to be due to the disease itself. TRIAL REGISTRATION: ClinicalTrials.gov (https://clinicaltrials.gov), NCT00114530.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".