Bibliographic record
Abstract
Renal inflammation is a universal response to infectious and noninfectious triggers. Sensors of the innate immune system, such as Toll-like receptors or RIG-like receptors, provide danger recognition platforms on renal cells that integrate and translate the diverse triggers of renal inflammation by inducing cell activation and the secretion of proinflammatory cytokines and chemokines. As a new entry, the inflammasome-forming NLR genes integrate various danger signals into caspase-1-activating platforms that regulate the processing and secretion of pro-IL-1β and pro-IL-18 into the mature and active cytokines. Accumulating data now document a role for the NLRP3 inflammasome and IL-1β/IL-18 in many diseases, including atherosclerosis, diabetes, amyloidosis, malaria, crystal-related diseases, and other autoinflammatory disorders, identifying this innate immune pathway as an attractive therapeutic target. Here we review the current knowledge regarding inflammasome signaling and outline existing evidence on the expression and functional role of the inflammasome-caspase-1-IL-1β/IL-18 axis in kidney disease. We further provide a perspective on the potential roles of the inflammasomes in the pathogenesis of acute and chronic kidney diseases.
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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) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".