Sequential broncho-alveolar lavages reflect distinct pulmonary compartments: clinical and research implications in lung transplantation
Bibliographic record
Abstract
BACKGROUND: Bronchoalveolar lavage (BAL) has proven to be very useful to monitor the lung allograft after transplantation. In addition to allowing detection of infections, multiple BAL analytes have been proposed as potential biomarkers of lung allograft rejection or dysfunction. However, BAL collection is not well standardized and differences in BAL collection represent an important source of variation. We hypothesized that there are systematic differences between sequential BALs that are relevant to BAL analysis. METHODS: As part of 126 consecutive bronchoscopies in lung transplant recipients, two sequential BALs (BAL1 and BAL2) were performed in one location during each bronchoscopy by instilling and suctioning 50 ml of normal saline twice into separate containers. Cell concentration, viability and differentials, Surfactant Protein-D (SP-D), Club Cell Secretory Protein (CCSP), and levels of CXCL10, IL-10, CCL2, CCL5, VEGF-C, RAGE, CXCL9, CXCL1, IL-17A, IL-21, PDGF, and GCSF were compared between BAL1 and BAL2. RESULTS: Total cell concentration did not differ between BAL1 and BAL2; however, compared to BAL2, BAL1 had more dead cells, epithelial cells, neutrophils, and higher concentrations of airway epithelium-derived CCSP and inflammatory markers. BAL2 had a higher concentration of SP-D compared to BAL1. CONCLUSION: In this study performed in lung transplant recipients, we show that sequential BALs represent different lung compartments and have distinct compositions. BAL1 represents the airway compartment with more epithelial cells, neutrophils, and epithelium-derived CCSP. Conversely, BAL2 samples preferentially the distal bronchoalveolar space with greater cell viability and higher SP-D. Our findings illustrate how the method of BAL collection can influence analyte concentrations and further emphasize the need for a standardized approach in translational research involving BAL samples.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".