Comprehensive Review of Adipose Stem Cells and Their Implication in Distraction Osteogenesis and Bone Regeneration
Bibliographic record
Abstract
Bone is one of the most dynamic tissues in the human body that can heal following injury without leaving a scar. However, in instances of extensive bone loss, this intrinsic capacity of bone to heal may not be sufficient and external intervention becomes necessary. Several techniques are available to address this problem, including autogenous bone grafts and allografts. However, all these techniques have their own limitations. An alternative method is the technique of distraction osteogenesis, where gradual and controlled distraction of two bony segments after osteotomy leads to induction of new bone formation. Although distraction osteogenesis usually gives satisfactory results, its major limitation is the prolonged duration of time required before the external fixator is removed, which may lead to numerous complications. Numerous methods to accelerate bone formation in the context of distraction osteogenesis have been reported. A viable alternative to autogenous bone grafts for a source of osteogenic cells is mesenchymal stem cells from bone marrow. However, there are certain problems with bone marrow aspirate. Hence, scientists have investigated other sources for mesenchymal stem cells, specifically adipose tissue, which has been shown to be an excellent source of mesenchymal stem cells. In this paper, the potential use of adipose stem cells to stimulate bone formation is discussed.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".