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
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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