Conditioned media from mouse osteosarcoma cells promote MC3T3‐E1 cell proliferation using JAKs and PI3‐K/Akt signal crosstalk
Bibliographic record
Abstract
The maintenance of bone mass requires a strict balance between bone formation by osteoblasts and bone resorption by osteoclasts. In tumoral bone environment, tumor cells frequently disturb this balance by interaction with bone cells to create a favorable site for tumor growth, and promote pathological bone changes. Thus, elucidation of the mechanisms underlying interaction between tumor cells and bone cells might eventually lead to a more rational strategy for therapeutic intervention for bone tumors and better understanding of bone biology. In the present study, the effects of mouse osteosarcoma cells on mouse preosteoblastic cells were determined by assessment of cell viability, osteoblastic differentiation and signal transduction pathways. MOS-J/POS-1 conditioned media (CM) significantly induced MC3T3-E1 cell proliferation in a dose-dependent manner and reduced both alkaline phosphatase activity and mineralized nodule formation. Piceatannol, AG490, LY294002 and rapamycin significantly abrogated this up-regulated cell proliferation; however, UO126 and STAT3 inhibitor peptide did not affect this up-regulated cell proliferation. MOS-J/POS-1 CM activated ERK 1/2, STAT3 and Akt signal transduction pathways; however, pro-proliferating signal induced by MOS-J/POS-1 CM was transmitted via Akt not ERK 1/2 and STAT3 pathways. Furthermore, Western blot analyses clearly revealed novel signal crosstalk between JAKs and PI3-K/Akt in osteoblastic cells. The specific factor(s) involved in MOS-J/POS-1 CM-induced MC3T3-E1 cell proliferation that use JAKs/PI3-K/Akt/mTOR pathway remain(s) to be determined. Determination of the specific factor(s) responsible for JAKs and PI3-K/Akt signal crosstalk that results in up-regulated preosteoblast proliferation will offer new insight into the pathology of osteosarcoma as well as other bone-related diseases.
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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) | 0.000 | 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) | 0.002 | 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".