The role of inflammasomes in human diseases and their potential as therapeutic targets
Bibliographic record
Abstract
Inflammasomes are large protein complexes that play a major role in sensing inflammatory signals and triggering the innate immune response. Each inflammasome complex has three major components: an upstream sensor molecule that is connected to a downstream effector protein such as caspase-1 through the adapter protein ASC. Inflammasome formation typically occurs in response to infectious agents or cellular damage. The active inflammasome then triggers caspase-1 activation, followed by the secretion of pro-inflammatory cytokines and pyroptotic cell death. Aberrant inflammasome activation and activity contribute to the development of diabetes, cancer, and several cardiovascular and neurodegenerative disorders. As a result, recent research has increasingly focused on investigating the mechanisms that regulate inflammasome assembly and activation, as well as the potential of targeting inflammasomes to treat various diseases. Multiple clinical trials are currently underway to evaluate the therapeutic potential of several distinct inflammasome-targeting therapies. Therefore, understanding how different inflammasomes contribute to disease pathology may have significant implications for developing novel therapeutic strategies. In this article, we provide a summary of the biological and pathological roles of inflammasomes in health and disease. We also highlight key evidence that suggests targeting inflammasomes could be a novel strategy for developing new disease-modifying therapies that may be effective in several conditions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".