Imatinib mesylate and nilotinib decrease synthesis of bone matrix in�vitro
Bibliographic record
Abstract
Tyrosine kinase inhibitors (TKIs), such as imatinib (IMA) and nilotinib (NIL), are the cornerstone of chronic myeloid leukemia (CML) treatment via the blockade of the oncogenic BCR‑ABL1 fusion protein. However, skeletal side effects are commonly observed in pediatric patients receiving long‑term treatment with IMA. Additionally, in vitro studies have shown that IMA and NIL alter vitamin D metabolism, which may further impair bone metabolism. To determine whether TKIs directly affect bone cell function, the present study treated the human osteoblastic cell line SaOS‑2 with IMA or NIL and assessed effects on their mineralization capacity as well as mRNA expression of receptor activator of nuclear factor κB ligand (RANKL) and osteoprotegerin (OPG), two cytokines that regulate osteoclastogenesis. Both TKIs significantly inhibited mineralization and downregulated osteoblast marker genes, including alkaline phosphatase, osteocalcin, osterix, as well as genes associated with the pro‑osteogenic Wnt signaling pathway; NIL was more potent than IMA. In addition, both TKIs increased the RANKL/OPG ratio, which is known to stimulate osteoclastogenesis. The present results suggested that the TKIs IMA and NIL directly inhibited osteoblast differentiation and directly promoted a pro‑osteoclastogenic environment through the RANKL‑OPG signaling axis. Thus, we propose that future work is required to determine whether the bone health of CML patients undergoing TKI‑treatment should be routinely monitored.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".