Dedifferentiated Fat (<scp>DFAT</scp>) cells: A cell source for oral and maxillofacial tissue engineering
Bibliographic record
Abstract
Tissue engineering is a promising method for the regeneration of oral and maxillofacial tissues. Proper selection of a cell source is important for the desired application. This review describes the discovery and usefulness of dedifferentiated fat (DFAT) cells as a cell source for tissue engineering. Dedifferentiated Fat cells are a highly homogeneous cell population (high purity), highly proliferative, and possess a multilineage potential for differentiation into various cell types under proper in vitro inducing conditions and in vivo. Moreover, DFAT cells have a higher differentiation capability of becoming osteoblasts, chondrocytes, and adipocytes than do bone marrow-derived mesenchymal stem cells and/or adipose tissue-derived stem cells. The usefulness of DFAT cells in vivo for periodontal tissue, bone, peripheral nerve, muscle, cartilage, and fat tissue regeneration was reported. Dedifferentiated Fat cells obtained from the human buccal fat pad (BFP) are a minimally invasive procedure with limited esthetic complications for patients. The BFP is a convenient and accessible anatomical site to harvest DFAT cells for dentists and oral surgeons, and thus is a promising cell source for oral and maxillofacial tissue engineering.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".