Inflammasome activation in acute lung injury
Bibliographic record
Abstract
Inflammasomes are multiprotein complexes tasked with sensing endogenous or exogenous inflammatory signals and integrating this signal into a downstream response. Inflammasome activation has been implicated in a variety of pulmonary diseases, including pulmonary hypertension, bacterial pneumonia, COPD, and asthma. Of increasing interest is the contribution of inflammasome activation in the context of acute lung injury/acute respiratory distress syndrome (ALI/ARDS). Inflammasome activation in both the lung parenchyma and resident immune cells generates intereukin-1β (IL-1β) and IL-18, both of which drive the cascade of lung inflammation forward. Blockade of these responses has been shown to be beneficial in animal models and is a focus of translational research in the field. In this review, we will discuss the assembly and regulation of inflammasomes during lung inflammation, highlighting therapeutically viable effector steps. We will examine the importance of IL-1β and IL-18, two key products of inflammasome activation, in ALI, as well as the contribution of the pulmonary endothelial cell to this process. Finally, we will explore translational research moving toward anti-inflammasome therapies for ALI/ARDS and speculate toward future directions for the field.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".