B Cell–Activating Transcription Factor Plays a Critical Role in the Pathogenesis of Anti–Major Histocompatibility Complex–Induced Obliterative Airway Disease
Bibliographic record
Abstract
Antibodies (Abs) against major histocompatibility complex (MHC) results in T helper-17 (Th17)-mediated immunity against lung self-antigens (SAgs), K-α1 tubulin and collagen V and obliterative airway disease (OAD). Because B cell-activating transcription factor (BATF) controls Th17 and autoimmunity, we proposed that BATF may play a critical role in OAD. Anti-H2K(b) was administered intrabronchially into Batf (-/-) and C57BL/6 mice. Histopathology of the lungs on days 30 and 45 after Ab administration to Batf (-/-) mice resulted in decreased cellular infiltration, epithelial metaplasia, fibrosis, and obstruction. There was lack of Abs to SAgs, reduction of Sag-specific interleukin (IL)-17 T cells, IL-6, IL-23, IL-17, IL-1β, fibroblast growth factor-6, and CXCL12 and decreased Janus kinase 2, signal transducer and activator of transcription 3 (STAT3), and retinoid-related orphan receptor γT. Further, micro-RNA (miR)-301a, a regulator of Th17, was reduced in Batf (-/-) mice in contrast to upregulation of miR-301a and downregulation of protein inhibitor of activated STAT3 (PIAS3) in anti-MHC-induced OAD animals. We also demonstrate an increase in miR-301a in the bronchoalveolar lavage cells from lung transplant recipients with Abs to human leukocyte antigen. This was accompanied by reduction in PIAS3 mRNA. Therefore, we conclude that BATF plays a critical role in the immune responses to SAgs and pathogenesis of anti-MHC-induced rejection. Targeting BATF should be considered for preventing chronic rejection after human lung transplantation.
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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 | 0.001 |
| 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".