COMPREHENSIVE IMMUNOPROFILING OF PERIPHERY BLOOD IDENTIFIES KEY IMMUNE FEATURES ASSOCIATED WITH DISEASE PROGRESSION AND PATIENT STRATIFICATION OF SLE
Bibliographic record
Abstract
PV242 / #5 Poster Topic: AS23 - SLE-Diagnosis, Manifestations, & Outcomes Background/Purpose To advance the development of immune biomarkers for diagnosing and treating Systemic Lupus Erythematosus (SLE), we have established a robust evaluation platform that integrates immune repertoire sequencing, T cell subtype profiling, and transcriptional sequencing. Methods This platform facilitates a systematic exploration of the peripheral immune status of SLE patients, such as TCR, BCR, bulk Rnaseq and flow cytometry. Then we compare these immune characteristics with status of SLE patients, which are associated with their clinical conditions (Figure 1). Figure 1. The flowchart of this study. Results Notably, we observed significant clonal expansion, loss of naive T cells, and heightened activation and stress in both CD8+ and CD4+ T cells, suggesting a critical role of T cells in the pathogenesis of SLE. Furthermore, our analysis identified unique features in the immune repertoires of SLE patients compared to healthy donors, such as differential TRV-TRJ usage and the presence of public clones, which could serve as early diagnostic markers for SLE. For B cells, we identified a notable isotype switching and a prevalent usage of IgHV-IgHJ gene segments. This finding highlights the dynamic adaptability of the B cell compartment in response to autoimmune challenges. By integrating several immune indices, we have developed a novel approach to categorize SLE patients into groups based on their adaptive immune activity, which shows a partial correlation with their disease status (Figure 2). Moving forward, we aim to amalgamate all gathered immune profiling data and apply cutting-edge analytical techniques, including machine learning, to discern various immunotypes. Figure 2. The model to categorize SLE patients into groups based on their adaptive immune activity. Conclusions This stratification will enhance our ability to diagnose and tailor treatments for SLE patients to achieve favorable prognosis. Our study not only sheds light on the complexities of the immune system in SLE but also demonstrates the potential of comprehensive immunoprofiling in enhancing patient management in this challenging autoimmune disorder.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".