Bibliographic record
Abstract
The rapid and efficient phagocytic clearance of apoptotic cells, termed efferocytosis, is a critical mechanism in the maintenance of tissue homeostasis. Removal of apoptotic cells through efferocytosis prevents secondary necrosis and the resultant inflammation caused by the release of intracellular contents. The importance of efferocytosis in homeostasis is underscored by the large number of inflammatory and autoimmune disorders, including atherosclerosis and systemic lupus erythematosus, that are characterized by defective apoptotic cell clearance. Although mechanistically similar to the phagocytic clearance of pathogens, efferocytosis differs from phagocytosis in that it is immunologically silent and induces a tissue repair response. Efferocytes face unique challenges resulting from the internalization of apoptotic cells, including degradation of the apoptotic cell, dealing with the extra metabolic load imposed by the processing of apoptotic cell contents, and the coordination of an anti-inflammatory, pro-tissue repair response. This review will discuss recent advances in our understanding of the cellular response to apoptotic cell uptake, including trafficking of apoptotic cell cargo and antigen presentation, signaling and transcriptional events initiated by efferocytosis, the coordination of an anti-inflammatory response and tissue repair, unique cellular metabolic responses and the role of efferocytosis in host defense. A better understanding of how efferocytic cells respond to apoptotic cell uptake will be critical in unraveling the complex connections between apoptotic cell removal and inflammation resolution and maintenance of tissue homeostasis.
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 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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".