B‐cell and T‐cell receptor repertoire in chronic inflammatory demyelinating polyneuropathy, a prospective cohort study
Bibliographic record
Abstract
The immunopathophysiological mechanisms underlying chronic inflammatory demyelinating polyneuropathy (CIDP) in an individual patient are largely unknown. Better understanding of these mechanisms may aid development of biomarkers and targeted therapies. Both B- and T-cell dominant mechanisms have been implicated. We therefore investigated whether B-cell and T-cell receptor (BCR/TCR) repertoires might function as immunological biomarkers in CIDP. In this prospective cohort study, we longitudinally sampled peripheral blood of CIDP patients in three different phases of CIDP: starting induction treatment (IT), starting withdrawal from IVIg maintenance treatment (MT), and patients in remission (R). BCR and TCR repertoires were analyzed using RNA based high throughput sequencing. In baseline samples, the number of total clones, the number of dominant BCR and TCR clones and their impact on the repertoire was similar for patients in the IT, MT, and remission groups compared with healthy controls. Baseline samples in the IT or MT did not predict treatment response or potential relapse at follow-up. Treatment responders in the IT group showed a potential IVIg-induced increase in the number of dominant BCR clones and their impact at follow-up (baseline1.0 [IQR 1.0-2.8] vs. 6 m 3.5 [0.3-6.8]; P < .05, Wilcoxon test). Although the BCR repertoire changed over time, the TCR repertoire remained robustly stable. We conclude that TCR and BCR repertoire distributions do not predict disease activity, treatment response or response to treatment withdrawal.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".