The Molecular Legacy of Apoptosis in Transplantation
Bibliographic record
Abstract
Transplanted organs have to cope with diverse immunologic and metabolic stressors that augment the percentage of stressed and dying cells. Cell death, whether apoptotic or necrotic, is crucial in various transplantation-associated conditions. Necrosis, a proinflammatory type of cell death classically considered as accidental, is increasingly recognized as a highly controlled death program. Apoptosis, the classical programmed cell death mode program, is tightly orchestrated and culminates in the activation of caspases. Apoptosis was classically regarded as a silent form of cell death, but mounting evidence indicates that apoptotic cells "don't go silently" and leave a heritage to the local microenvironment. This apoptotic legacy, embedded within the effector phase of apoptosis, is aimed, at least in part, at controlling leukocyte trafficking and fostering tissue remodeling at sites of apoptotic cell deletion and can promote maladaptive remodeling pathways of importance for obliterative vascular remodeling. Moreover, apoptotic cells can transfer bioactive molecules by the release of apoptotic membrane vesicles that, in turn, shapes the phenotype and functions of immune cells. In this review, we summarize recent data highlighting the importance of apoptosis-associated intercellular communication networks in the regulation of allograft remodeling and immune responses in transplantation.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".