Gastroesophageal reflux disease is associated with differences in the allograft microbiome, microbial density and inflammation in lung transplantation
Bibliographic record
Abstract
Abstract Rationale Gastroesophageal reflux disease (GERD) may affect lung allograft inflammation and function through its effects on allograft microbial community composition in lung transplant recipients. Objectives Our objective was to compare the allograft microbiota in lung transplant recipients with or without clinically diagnosed GERD in the first post-transplant year, and assess associations between GERD, allograft microbiota, inflammation and acute and chronic lung allograft dysfunction (ALAD/CLAD). Methods 268 bronchoalveolar lavage samples were collected from 75 lung transplant recipients at a single transplant centre every 3 months post-transplant for 1 year. Ten transplant recipients from a separate transplant centre provided samples pre/post-anti-reflux Nissen fundoplication surgery. Microbial community composition and density were measured using 16S rRNA gene sequencing and qPCR, respectively and inflammatory markers and bile acids were quantified. Measurements and Main Results We observed three community composition profiles (labelled community state types, CSTs 1-3). Transplant recipients with GERD were more likely to have CST1, characterized by high bacterial density and relative abundance of the oropharyngeal colonizing genera Prevotella and Veillonella . GERD was associated with more frequent transition to CST1. CST1 was associated with lower per-bacteria inflammatory cytokine levels than the pathogen-dominated CST3. Time-dependant models revealed associations between CST3 and development of ALAD/CLAD. Nissen fundoplication decreased bacterial load and pro-inflammatory cytokines. Conclusion GERD was associated with a high bacterial density, Prevotella/Veillonella dominated CST1. CST3, but not CST1 or GERD, was associated with inflammation and early development of ALAD/CLAD. Nissen fundoplication was associated with decreases in microbial density in BALF samples, especially the CST1-specific genus, Prevotella .
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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 | 0.001 |
| 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 | 0.001 | 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".