Local intragraft humoral immune responses in chronic lung allograft dysfunction
Bibliographic record
Abstract
Background Donor human leukocyte antigen (HLA)-specific antibodies (DSA) and non-HLA antibodies can cause allograft injury, possibly leading to chronic lung allograft dysfunction (CLAD) after lung transplantation. It remains unclear whether these antibodies are produced locally in the graft or derived solely from circulation. We hypothesized that DSA and non-HLA antibodies are produced in CLAD lungs. Methods Lung tissue was prospectively collected from 15 CLAD patients undergoing retransplantation or autopsy. 0.3 g of fresh lung tissue was cultured for 4 days without or with lipopolysaccharide or CD40L: lung culture supernatant (LCS) was sampled. Protein eluate was obtained from 0.3 g of frozen lung tissue. The mean fluorescence intensity (MFI) of DSA and non-HLA antibodies was measured by Luminex and antigen microarray, respectively. Results LCS from all 4 patients who had serum DSA at lung isolation were positive for DSA, with higher levels measured after CD40L stimulation (CD40L + LCS). Of these, only 2 had detectable DSA in lung eluate. MFI of non-HLA antibodies from CD40L + LCS correlated with those from lung eluate but not with those from sera. Flow cytometry showed higher frequencies of activated lung B cells in patients whose CD40L + LCS was positive for DSA ( n = 4) or high non-HLA antibodies ( n = 6) compared to those with low local antibodies ( n = 5). Immunofluorescence staining showed CLAD lung lymphoid aggregates with local antibodies contained larger numbers of IgG + plasma cells and greater IL-21 expression. Conclusions We show that DSA and non-HLA antibodies can be produced within activated B cell-rich lung allografts.
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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) | 0.001 | 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".