Hierarchical Regulation of Wound Healing by NOD-Like Receptors in Cardiovascular Disease
Bibliographic record
Abstract
SIGNIFICANCE: Persistent nonmicrobial tissue injury leads to the nonlinear activation of integrated wound-healing pathways. In chronic cardiovascular diseases, local tissue undergoes dynamic remodeling involving both structural cells and professional innate immune cells in attempts to limit burden of injury. While the final effector mechanisms by which these different cellular populations participate in wound healing are functionally distinct, their upstream molecular signaling pathways can often be shared. RECENT ADVANCES: The NOD-like receptors (NLRs) are intracellular pattern recognition receptors that have been well characterized as key regulators of pro-inflammatory cytokine production in innate immune cells. However, recent evidence has shown that some NLR proteins are additionally expressed by resident structural cells despite negligible cytokine production. These results indicate the potential for noncanonical routes of innate immune signaling by NLRs within solid organ systems. CRITICAL ISSUES: Here, we review the emerging functions of NLR proteins in professional immune and tissue-resident cells, and discuss the implications in wound healing during chronic cardiovascular diseases. Emphasis is placed on NLRP3 and its regulation of cardiac structure and function in response to injury. Specific cellular and subcellular signaling paradigms are also discussed. FUTURE DIRECTIONS: The characterization of how NLRs participate in homeostasis during cellular injury is essential to develop their potential utility for therapeutic intervention in cardiovascular disease.
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 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) | 0.002 | 0.002 |
| 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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".