GLOBAL IMMUNE REPERTOIRE PROFILING SUGGESTS MULTIFACETED UNRESOLVED DYSREGULATION UPON SYSTEMIC LUPUS ERYTHEMATOSUS IMMUNOSUPPRESSANT THERAPY
Bibliographic record
Abstract
PV004 / #575 Poster Topic: AS01 - Adaptive Immunity Background/Purpose Examination of cross-treatment immune dynamics and its potential contribution to the relapse of systemic lupus erythematosus (SLE) in the real clinical setting. Methods We performed single-cell RNA and VDJ profiling on 31 samples from 24 individuals in total, consisting of 20 naïve SLE patients, 7 matched standard-of-care-treated SLE patients, and 4 health control samples. Along with published SLE data, we compiled a comprehensive SLE immune atlas of ~1.6 million cells. Results While the treatment did lead to the suppression of global immune overactivity, we discovered for the first time its failure to effectively restore several undocumented abnormalities in treatment-naïve patients, including the drop in somatic hypermutation (SHM) of memory and DN B cells, and the clonal expansion of CD4+ Th1 and CD8+ memory T cells. Conclusions Our study provides valuable data and sheds lights on the underlying multifaceted unresolved dysregulation upon SLE immunosuppressant therapy under the real clinical setting; such dysregulation may further contribute to the high post-treatment relapse rate and thus call for additional target-specific treatment strategies.
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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 | 0.000 | 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".