Recreating the Human Alveolar Macrophage Niche to Study Bacterial Pneumonia
Bibliographic record
Abstract
Staphylococcus aureus (SA) pneumonia infections are widespread in Canada, particularly in hospitalized patients, and are associated with high mortality rates and significant financial costs to the healthcare system. A major limitation in our ability to design treatments to alleviate disease burden is a lack of understanding regarding how the lung’s frontline defenders, such as alveolar macrophages, interact with SA bacteria in humans. Unfortunately, there are a lack of models which accurately model human alveolar macrophages (AMs). To address this, we have designed a human lung on chip (LoC) device to improve human AM phenotypic differentiation in culture to investigate the interactions of SA with AMs. First, we used Mass Cytometry (CyTOF) to characterize the surface marker expression of PBMC-derived AMs cultured in the LoC device for three days. Results demonstrated PBMC-AMs acquired expression of numerous AM markers (eg. CD11b, CD169, CD206, and CD163) to similar levels as primary AMs derived from bronchoscopy samples. Next, we characterized PBMC-derived AM behavior in the LoC and their influence on LoC tissue morphology and barrier function. Results showed PBMC-AMs had similar migratory behaviors and morphology to what has been previously reported in vivo further validating our model. Subsequently, we examined the impact of bacterial infection using a 4-hour incubation with SA and found dramatically altered AM phenotype including altered marker expression and reduced migration. Finally, we incubated various strains and knockouts of SA in the LoC +/- PBMCs and found differing rates of bacterial abundance. Overall, we anticipate our model will offer an avenue for future studies seeking to understand human lung disease, particularly as it relates to SA lung infection.
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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 | 0.001 | 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".