Comprehensive B Cell Phenotyping Profile for Chronic Graft-versus-Host Disease Diagnosis
Bibliographic record
Abstract
Previous studies have reported single B cell-related chronic graft-versus-host disease diagnostic (cGVHD) biomarkers, such as B cell-activating factor (BAFF), CD21 low , and immature B cells, but research on the performance of biomarker combinations and the covariate effect of steroids is lacking. The primary objective of this study was to determine the most accurate combination of B cell populations using cell surface staining flow cytometry in an independent cohort of patients with cGVHD. Secondary objectives included assessing the effect of corticosteroid use at sample collection on the makeup and accuracy of the diagnostic panel and identifying the mechanism underlying low surface expression of BAFF receptor (BAFF-R) on B cells in cGVHD. Flow cytometry analysis was performed in an adult cohort of post-HCT patients with cGVHD onset (n = 44) and time-matched recipients without cGVHD (n = 63). We confirmed that the onset of cGVHD was associated with higher soluble BAFF (sBAFF) levels, elevated CD27 − CD10 − CD21 low CD19 + B cell and classical switched memory B cell counts, and reduced transitional and naïve B cell counts. The highest single B cell population area under the receiver operating characteristic (ROC) curve (AUC) was .72 for transitional type 1 CD21 low B cells. We also showed a significant inverse relationship between sBAFF and surface BAFF-R expression caused by sBAFF modulation of BAFF-R. Steroid use at sample collection influenced the significance of the sBAFF:B cell ratio, naïve and marginal zone-like B cells. The optimal combination of B cell subsets most significantly associated with cGVHD onset with or without concurrent corticosteroid use resulted in ROC AUCs of .87 and .84, respectively. Transitional and CD21 low B cells were the only populations present in both panels; however, analyzing only these populations resulted in ROC AUCs of .79 and .78, respectively. This suggests that the inclusion of other populations and use of different panels depending on steroid use is necessary to achieve better accuracy. sBAFF was not a component of either panel. These novel B cell profiles could be tested prospectively in patients post-HSCT and could lead to focused mechanistic studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.001 | 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".