Cells from bone marrow that evolve into oral tissues and their clinical applications
Bibliographic record
Abstract
There are two major well-characterized populations of post-natal (adult) stem cells in bone marrow: hematopoietic stem cells which give rise to blood cells of all lineages, and mesenchymal stem cells which give rise to osteoblasts, adipocytes, and fibroblasts. For the past 50 years, strict rules were taught governing developmental biology. However, recently, numerous studies have emerged from researchers in different fields suggesting the unthinkable--that stem cells isolated from a variety of organs are capable of ignoring their cell lineage boundaries and exhibiting more plasticity in their fates. Plasticity is defined as the ability of post-natal (tissue-specific adult) stem cells to differentiate into mature and functional cells of the same or of a different germ layer of origin. There are reports that bone marrow stem cells can evolve into cells of all dermal lineages, such as hepatocytes, skeletal myocytes, cardiomyocytes, neural, endothelial, epithelial, and even endocrine cells. These findings promise significant therapeutic implications for regenerative medicine. This article will review recent reports of bone marrow cells that have the ability to evolve or differentiate into oral and craniofacial tissues, such as the periodontal ligament, alveolar bone, condyle, tooth, bone around dental and facial implants, and oral mucosa.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".