Inflammasomes in Tissue Damages and Immune Disorders After Trauma
Bibliographic record
Abstract
Trauma remains a leading cause of death worldwide. Hemorrhagic shock and direct injury to vital organs are responsible for early mortality whereas most delayed deaths are secondary to complex pathophysiological processes. These processes result from imbalanced systemic reactions to the multiple aggressions associated with trauma. Trauma results in the uncontrolled local and systemic release of endogenous mediators acting as danger signals [damage-associated molecular patterns (DAMPs)]. Their recognition by the innate immune system triggers a pro-inflammatory immune response paradoxically associated with concomitant immunosuppression. These responses, ranging in intensity from inappropriate to overwhelming, promote the propagation of injuries to remote organs, leading to multiple organ failure and death. Some of the numerous DAMPs released after trauma trigger the assembly of intracellular multiprotein complexes named inflammasomes. Once activated by a ligand, inflammasomes lead to the activation of a caspase. Activated caspases allow the release of mature forms of interleukin-1β and interleukin-18 and trigger a specific pro-inflammatory cell death termed pyroptosis. Accumulating data suggest that inflammasomes, mainly NLRP3, NLRP1, and AIM2, are involved in the generation of tissue damage and immune dysfunction after trauma. Following trauma-induced DAMP(s) recognition, inflammasomes participate in multiple ways in the development of exaggerated systemic and organ-specific inflammatory response, contributing to organ damage. Inflammasomes are involved in the innate responses to traumatic brain injury and contribute to the development of acute respiratory distress syndrome. Inflammasomes may also play a role in post-trauma immunosuppression mediated by dysregulated monocyte functions. Characterizing the involvement of inflammasomes in the pathogenesis of post-trauma syndrome is a key issue as they may be potential therapeutic targets. This review summarizes the current knowledge on the roles of inflammasomes in trauma.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".