195 Investigating T cell immunity in anti-NMDA receptor encephalitis
Bibliographic record
Abstract
a:2:{s:4:"lang";s:2:"en";s:7:"content";s:1482:" Background Anti-NMDAR encephalitis causes significant morbidity including prolonged ICU or hospital admissions. There is a pressing need to better understand disease immunopathogenesis and identify new treatments. The role of T cells is unexplored. Methods Peripheral blood mononuclear cells from 8 patients and 8 healthy volunteers underwent three rounds of stimulation with pooled, overlapping 15mer peptides spanning the NMDAR-NR1 subunit. Flow cytometry was used to identify NR1-specific CD4+ and CD8+ T cell responses after each stimulation. Peptide matrix and single peptide stimulations were used to determine immunogenic NR1 peptides from peptide pools. Results NR1-specific CD4+ and CD8+ T cells were identified in patients and healthy volunteers. NR1 responses were more frequent and occurred earlier in patients. NR1-specific CD8+ T cells were more frequent than NR1-specific CD4+ T cells. Although CD4+ and CD8+ T cell responses spanned the whole NR1 protein, responses were most often found using a limited number of peptides localising to specific regions of NR1. Conclusions NR1-specific CD4+ and CD8+ T cells can be identified in patients and healthy volunteers. However, patterns of response suggest disease-associated changes in the T cell repertoire in patients. Further work must determine the role of these cells in disease pathogenesis or as potential treatment targets. rachel.brown{at}ucl.ac.uk ";}
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".