Regulatory T cell profiles in patients with N-methyl-ᴅ-aspartate receptor-antibody encephalitis
Bibliographic record
Abstract
PurposePurpose Regulatory T cells (Tregs) have been implicated in the pathogenesis of several autoimmune disorders and used in adoptive cell transfer therapies. Neither have been explored in patients with autoimmune encephalitis where treated patient outcomes remain suboptimal with frequent relapses. Here, to identify new treatment strategies for autoimmune encephalitis, we sought to evaluate the proportion of circulating Tregs and Treg subpopulations in peripheral blood of patients with N-methyl-ᴅ-aspartate receptor-antibody encephalitis (NMDAR-Ab-E) and compared this with healthy controls.MethodsWe compared the phenotype of peripheral blood Tregs in four adult NMDAR-Ab-E patients and four age- and sex-matched healthy controls using an 11-color flow cytometry assay panel for characterization of Tregs (CD4+ CD25+ FoxP3+) cells into naïve (chemokine receptor [CCR] 7+ CD45RA+), central memory (CCR7+ CD45RA-), and effector memory (CCR7- CD45RA-) cells. We also examined and compared the expression of the CCR6 by circulating Tregs and the respective Treg subpopulations between the study groups.ResultsThe proportion of circulating Tregs was similar between patients with NMDAR-Ab-E and healthy controls but the proportion of naïve Tregs was lower in NMDAR-Ab-E patients (p = 0.0026). Additionally, the frequency of circulating effector memory Tregs was higher, and the proportion of circulating effector memory Tregs expressing CCR6 was lower, in NMDAR-Ab-E patients compared with healthy controls (p = 0.0026).ConclusionAltered Treg homeostasis may be a feature of patients with NMDAR-Ab-E. Future studies with larger samples are warranted to validate these findings.
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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 | 0.001 | 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".