Bibliographic record
Abstract
Chronic low-grade inflammation contributes to the development of several diseases, including cardiovascular disease. Adequate strategies to target inflammation in cardiovascular disease are in their infancy and remain an avenue of great interest. The purinergic receptor P2X7 is a ubiquitously expressed receptor that predominately mediates inflammation and cellular death. P2X7 is a ligand-gated cation channel that is activated in response to high concentrations of extracellular ATP, triggering the assembly and activation of the NLRP3 (nuclear oligomerization domain like receptor family pyrin domain containing 3) inflammasome and subsequent release of proinflammatory cytokines IL (interleukin)-1β and IL-18. Increased P2X7 activation and IL-1β and IL-18 concentrations have been implicated in the development of many cardiovascular conditions including hypertension, atherosclerosis, ischemia/reperfusion injury, and heart failure. P2X7 receptor KO (knockout) mice exhibit a significant attenuation of the inflammatory response, which corresponds with reduced disease severity. P2X7 antagonism blunts blood pressure elevation in hypertension and progression of atherosclerosis in animal models. IL-1β and IL-18 inhibition has shown efficacy in clinical trials reducing major adverse cardiac events, including myocardial infarction, and heart failure. With several P2X7 antagonists available with proven safety margins, P2X7 antagonism could represent an untapped potential for therapeutic intervention in cardiovascular disorders.
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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.008 |
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".