IL‐1α and IL‐1β have different effects on formation and activity of large osteoclasts
Bibliographic record
Abstract
Interleukin 1 (IL-1) is a proinflammatory cytokine upregulated in conditions such as rheumatoid arthritis and periodontal disease. Both isoforms, IL-1alpha and IL-1beta, have been shown to activate osteoclasts (OCs), the cells responsible for resorbing bone. Inflammatory conditions are also characterized by increased bone loss and by the presence of large OCs (10+ nuclei). We and others have previously shown that large OCs are more likely to be resorbing compared to small OCs (2-5 nuclei). Moreover, large OCs express higher levels of the IL-1 activating receptor IL-1RI, integrins alphav and beta3, RANK, and TNFR1, while small OCs have higher levels of the decoy receptor IL-1RII. We hypothesized that IL-1 would have different effects on large and small OCs due to these distinct receptor expression patterns. To test this hypothesis, RAW 264.7 cells were differentiated into populations of small and large OCs and treated with IL-1alpha or IL-1beta (1 and 10 ng/ml). In the presence of sRANKL, both IL-1alpha and IL-1beta increased total OC number and resorptive activity of large OCs. IL-1alpha stimulated formation of large OCs and increased the number of resorption pits, while IL-1beta changed the morphology of large OCs and integrin-beta3 phosphorylation. No effects were seen in small OCs in response to either IL-1 isoform. These results demonstrate that IL-1 predominantly affects large OCs. The dissimilarity of responses to IL-1alpha and IL-1beta suggests that these isoforms activate different signaling pathways within the two OC populations.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| 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".