Acute lung injury and cell death: how many ways can cells die?
Bibliographic record
Abstract
Apoptosis has been considered as an underlying mechanism in acute lung injury/acute respiratory distress syndrome and multiorgan dysfunction syndrome. Recently, several alternative pathways for cell death (such as caspase-independent cell death, oncosis, and autophagy) have been discovered. Evidence of these pathways in the pathogenesis of acute lung injury has also come into light. In this article, we briefly introduce cell death pathways and then focus on studies related to lung injury. The different types of cell death that occur and the underlying mechanisms utilized depend on both experimental and clinical conditions. Lipopolysaccharide-induced acute lung injury is associated with apoptosis via Fas/Fas ligand mechanisms. Hyperoxia and ischemia-reperfusion injury generate reactive oxidative species, which induce complex cell death patterns composed of apoptosis, oncosis, and necrosis. Prolonged overexpression of inflammatory mediators results in increased production and activation of proteases, especially cathepsins. Activation and resistance to death of neutrophils also plays an important role in promoting parenchymal cell death. Knowledge of the coexisting multiple cell death pathways and awareness of the pharmacological inhibitors targeting different proteases critical to cell death may lead to the development of novel therapies for acute lung injury.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".