Proteoglycans in Toll-like receptor responses and innate immunity
Bibliographic record
Abstract
The extracellular matrix (ECM) is an active and dynamic feature of tissues that not only provides gross structure but also plays key roles in cellular responses. The ever-changing microenvironment responds dynamically to cellular and external signals, and in turn influences cell fate, tissue development, and response to environmental injury or microbial invasion. It is therefore paramount to understand how the ECM components interact with each other, the environment and cells, and how they mediate their effects. Among the ECM components that have recently garnered increased attention, proteoglycans (PGs) deserve special note. Recent evidence strongly suggests that they play a crucial role both in health maintenance and disease development. In particular, proteoglycans dictate whether homeostasis or cell death will result from a given injury, by triggering and modulating activation of the innate immune system, via a conserved array of receptors that recognize exogenous (infectious) or endogenous (tissue damage) molecular patterns. Innate immune activation by proteoglycans has important implications for the understanding of cell-matrix interactions in health and disease. In this review, we will summarize the current state of knowledge of innate immune signaling by proteoglycans, discuss the implications, and explore future directions to define progress in this area of extracellular matrix biology.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".