Microenvironment Modulates Osteogenic Cell Lineage Commitment in Differentiated Embryonic Stem Cells
Bibliographic record
Abstract
BACKGROUND: Due to their self-renewal, embryonic stem cells (ESCs) are attractive cells for applications in regenerative medicine and tissue engineering. Although ESC differentiation has been used as a platform for generating bone in vitro and in vivo, the results have been unsatisfactory at best. It is possible that the traditional culture methods, which have been used, are not optimal and that other approaches must be explored. METHODOLOGY/PRINCIPAL FINDINGS: ESCs were differentiated into osteoblast lineage using a micro-mass approach. In response to osteogenic differentiation medium, many cells underwent apoptosis, while others left the micro-mass, forming small aggregates in suspension. These aggregates were cultured in three different culture conditions (adhesion, static suspension, and stirred suspension), then examined for osteogenic potential in vitro and in vivo. In adhesion culture, ESCs primed to become osteoblasts recommitted to the adipocyte lineage in vitro. In a static suspension culture, resulting porous aggregates expressed osteoblasts markers and formed bone in vivo via intermembranous ossification. In a stirred suspension culture, resulting non-porous aggregates suppressed osteoblast differentiation in favor of expanding progenitor cells. CONCLUSIONS/SIGNIFICANCE: We demonstrate that microenvironment modulates cell fate and subsequent tissue formation during ESC differentiation. For effective tissue engineering using ESCs, it is important to develop optimized cell culture/differentiation conditions based upon the influence of microenvironment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".