Longitudinal Lower Airway Microbial Signatures of Acute Cellular Rejection in Lung Transplantation
Bibliographic record
Abstract
Abstract Rationale Acute cellular rejection (ACR) after lung transplant is a leading risk factor for chronic lung allograft dysfunction. Prior studies have demonstrated dynamic microbial changes occurring within the allograft and gut that influence local adaptive and innate immune responses. However, the lung microbiome’s overall impact on ACR risk remains poorly understood. Objectives To evaluate whether temporal changes in microbial signatures were associated with the development of ACR. Methods We performed cross-sectional and longitudinal analyses (joint modeling of longitudinal and time-to-event data and trajectory comparisons) of 16S rRNA gene sequencing results derived from lung transplant recipient lower airway samples collected at multiple time points. Measurements and Main Results Among 103 lung transplant recipients, 25 (24.3%) developed ACR. In comparing samples acquired 1 month after transplant, subjects who never developed ACR demonstrated lower airway enrichment with several oral commensals (e.g., Prevotella and Veillonella spp.) than those with current or future (beyond 1 mo) ACR. However, a subgroup analysis of those who developed ACR beyond 1 month revealed delayed enrichment with oral commensals occurring at the time of ACR diagnosis compared with baseline, when enrichment with more traditionally pathogenic taxa was present. In longitudinal models, dynamic changes in α-diversity (characterized by an initial decrease and a subsequent increase) and in the taxonomic trajectories of numerous oral commensals were more commonly observed in subjects with ACR. Conclusions Dynamic changes in the lower airway microbiota are associated with the development of ACR, supporting its potential role as a useful biomarker or in ACR pathogenesis.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".