Enhanced Osteogenic Differentiation of Pluripotent Stem Cells via γ-Secretase Inhibition
Bibliographic record
Abstract
Bone healing is a complex, well-organized process. Multiple factors regulate this process, including growth factors, hormones, cytokines, mechanical stimulation, and aging. One of the most important signaling pathways that affect bone healing is the Notch signaling pathway. It has a significant role in controlling the differentiation of bone mesenchymal stem cells and forming new bone. Interventions to enhance the healing of critical-sized bone defects are of great importance, and stem cell transplantations are eminent candidates for treating such defects. Understanding how Notch signaling impacts pluripotent stem cell differentiation can significantly enhance osteogenesis and improve the overall healing process upon transplantation. In Rancourt's lab, mouse embryonic stem cells (ESC) have been successfully differentiated to the osteogenic cell lineage. This study investigates the role of Notch signaling inhibition in the osteogenic differentiation of mouse embryonic and induced pluripotent stem cells (iPS). Our data showed that Notch inhibition greatly enhanced the differentiation of both mouse embryonic and induced pluripotent stem cells.
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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".