Harnessing CD36 to Rein in Inflammation
Bibliographic record
Abstract
Maintaining health requires a dynamic balance between the influence of pro-inflammatory and anti-inflammatory mediators. While inflammation serves an important protective role against infection, unrestrained inflammation is acutely lethal and unresolved inflammation contributes to a broad range of chronic disorders. Immunotherapy with cytokines themselves or cytokine antagonists faces strict limitations due to efficacy, safety and cost. More successful treatment of the pro-inflammatory component of chronic disorders may emerge from strategies designed to reset the balance between pro and anti-inflammatory cytokines through physiological regulatory pathways. One emerging avenue for this approach is exploitation of the link between the cell surface protein CD36 and the anti-inflammatory cytokine interleukin-10 (IL-10). Agents that increase CD36 expression and agents that directly bind to CD36 have anti-inflammatory properties that may directly relate to induction of IL-10. The immunosuppressive effects of apoptotic cells were first reported more than a decade ago and have since been tested in animal models and several clinical trials. A recent publication demonstrates that induction of IL-10 by apoptotic cells is largely dependent upon the interaction between apoptotic cells and CD36, the receptor on monocytes and macrophages for apoptotic cells. This provides a direct mechanistic link between CD36 engagement and IL-10 induction, opening up new possibilities for using CD36 ligands, agents that increase CD36 expression or a combination of both to modulate inflammation and treat, or even prevent, an important set of chronic disorders.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".