Lung microbiome quantification and bacterial density as prognostic markers in lung transplantation
Bibliographic record
Abstract
BACKGROUND: Chronic lung allograft dysfunction (CLAD) is a barrier to long-term survival following lung transplantation. Composition of the lung allograft microbiome has been explored as a prognostic marker for allograft survival, but most studies have measured bacterial relative, not absolute, abundances. Absolute quantitation of bacterial abundances improves the assessment of host-microbe interactions in diverse disease states and physiologic compartments but has not been compared to compositional indices alone for performance in identifying CLAD-associated allograft microbiomes. METHODS: We performed a case-control study to compare the composition of the lung allograft microbiome between lung transplant recipients who developed CLAD within the first 4 years of transplant (cases) versus those who remained CLAD-free for the first 5 years post-transplant (controls). Within 12 months post-transplantation, bronchoalveolar lavage fluid was collected from 53 cases and 55 controls to compare compositional features of the microbiome and bacterial absolute abundance. RESULTS: We identified 5 overlapping compositional subtypes, termed community state types (CST), of the lung allograft microbiome that were dominated by either Prevotella species, Streptococcus and Neisseria species, Staphylococcus or Pseudomonas species, or a diverse bacterial population. Lower bacterial density was observed in the taxonomically diverse community and was lower in controls than cases. Normalizing the relative abundance of CST-defining bacteria to total bacterial density strengthened microbiome-composition/chronic lung allograft dysfunction (CLAD) associations. CONCLUSIONS: Absolute bacterial quantification strengthens associations between the lung microbiome and CLAD following lung transplantation and may improve our understanding of how the lung microbiome affects lung transplant outcomes.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".