CD11c+ antigen presenting cells from the alveolar space, lung parenchyma and spleen differ in their phenotype and capabilities to activate naïve and antigen-primed T cells
Bibliographic record
Abstract
BACKGROUND: The lung is divided into two major compartments: the alveolar space and the parenchyma. The alveolar macrophages are the first line of leukocytes in the lung taking up incoming microbes or microbial antigens whereas the parenchymal dendritic cells (DCs) are believed to be the sole potent antigen presenting cells (APCs) in the lung. Both resting alveolar macrophages and parenchymal DCs express CD11c. Several important questions remain to be elucidated: 1] to which extent the alveolar space and lung parenchymal CD11c+ APCs differ in their phenotype and ability to activate naïve T cells; 2] whether they differ in their ability to activate antigen-experienced or -primed T cells; and 3] whether these lung CD11c+ APC populations differ from the splenic CD11c+ APCs which have been commonly used for understanding APC biology. RESULTS: CD11c+ APCs from the alveolar space, lung parenchyma, and the spleen display differential co-stimulatory molecule expression and cytokine responsiveness upon stimulation. Alveolar space APCs are weak activators of naïve T cells compared to lung parenchymal and splenic CD11c+ APC populations. However, alveolar space APCs are able to potently activate the in vivo microbial antigen-primed T cells to a similar extent as lung parenchymal and splenic APCs. CONCLUSION: Together our findings indicate that alveolar CD11c+ APCs have a specialized T cell-activating function, capable of activating antigen-primed, but not naïve, T cells whereas lung CD11c+ APCs are capable of activating both the naïve and antigen-primed T cell populations.
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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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".