Role of Apoptotic Cell Clearance in Pneumonia and Inflammatory Lung Disease
Bibliographic record
Abstract
Pneumonia and inflammatory diseases of the pulmonary system such as chronic obstructive pulmonary disease and asthma continue to cause significant morbidity and mortality globally. While the etiology of these diseases is highly different, they share a number of similarities in the underlying inflammatory processes driving disease pathology. Multiple recent studies have identified failures in efferocytosis-the phagocytic clearance of apoptotic cells-as a common driver of inflammation and tissue destruction in these diseases. Effective efferocytosis has been shown to be important for resolving inflammatory diseases of the lung and the subsequent restoration of normal lung function, while many pneumonia-causing pathogens manipulate the efferocytic system to enhance their growth and avoid immunity. Moreover, some treatments used to manage these patients, such as inhaled corticosteroids for chronic obstructive pulmonary disease and the prevalent use of statins for cardiovascular disease, have been found to beneficially alter efferocytic activity in these patients. In this review, we provide an overview of the efferocytic process and its role in the pathophysiology and resolution of pneumonia and other inflammatory diseases of the lungs, and discuss the utility of existing and emerging therapies for modulating efferocytosis as potential treatments for these diseases.
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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) | 0.003 | 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".