Biphasic effects of leukemia inhibitory factor on osteoblastic differentiation
Bibliographic record
Abstract
Leukemia inhibitory factor (LIF) is a cytokine produced by multiple cell types including osteoblasts and which is active on bone metabolism. We have previously shown that in a bone nodule forming in vitro model of osteogenesis, the fetal rat calvaria (RC) cell model, LIF inhibits osteoblast differentiation, acting on late osteoprogenitors and/or early osteoblasts. These results are in contrast to in vivo experiments, in which LIF has been found to increase bone formation. To resolve this discrepancy, we have tested the effect of LIF on rat bone marrow (RBM) stromal cell cultures, an in vitro model encompassing earlier osteoprogenitor stages. LIF inhibited cell growth in early, proliferating RBM cultures, but increased the culture saturation density. The effect of LIF on bone nodule formation in this model was cell density dependent and biphasic. Continuous treatment with LIF reduced the number of bone nodules present in confluent, more mature cultures, and the inhibitory effect was strongest when cells were plated at higher cell density than lower. In contrast, during the early stages of RBM culture, nodule numbers were higher in LIF-treated dishes than in controls, and this effect was greater in lower density cultures. Acute LIF treatment restricted to early time points increased the final number of bone nodules formed in mature RBM cell cultures, but not in RC cell cultures. Our results indicate that LIF exerts complex, stage-specific effects on osteoprogenitor recruitment, differentiation, and bone formation, and that the effects are cell nonautonomous, in the rat bone marrow stromal cell model. J. Cell. Biochem. Suppl. 36: 63-70, 2001.
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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) | 0.000 | 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) | 0.001 | 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 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".