Lung Macrophage Phenotypes and Functional Responses: Role in the Pathogenesis of COPD
Bibliographic record
Abstract
Lung macrophages (LMs) are essential immune effector cells that are pivotal in both innate and adaptive immune responses to inhaled foreign matter. They either reside within the airways and lung tissues (from early life) or are derived from blood monocytes. Similar to macrophages in other organs and tissues, LMs have natural plasticity and can change phenotype and function depending largely on the microenvironment they reside in. Phenotype changes in lung tissue macrophages have been implicated in chronic inflammatory responses and disease progression of various chronic lung diseases, including Chronic Obstructive Pulmonary Disease (COPD). LMs have a wide variety of functional properties that include phagocytosis (inorganic particulate matter and organic particles, such as viruses/bacteria/fungi), the processing of phagocytosed material, and the production of signaling mediators. Functioning as janitors of the airways, they also play a key role in removing dead and dying cells, as well as cell debris (efferocytic functions). We herein review changes in LM phenotypes during chronic lung disease, focusing on COPD, as well as changes in their functional properties as a result of such shifts. Targeting molecular pathways involved in LM phenotypic shifts could potentially allow for future targeted therapeutic interventions in several diseases, such as COPD.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".