The role of inflammasomes in human diseases and their potential as therapeutic targets
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it