Gingival mesenchymal stem cells: Biological properties and therapeutic applications
Bibliographic record
Abstract
Our understanding of mesenchymal stem cells (MSCs) and their biological properties is steadily increasing, with more studies focusing on their therapeutic effects in the domains of immunology, tissue engineering and regenerative medicine. MSCs may be derived from tissues such as bone marrow, adipose, the umbilical cord, as well as from dental tissues (e.g., tooth germ, dental follicle, pulp tissue of exfoliated deciduous and permanent teeth, apical papilla, periodontal ligament, gingiva, and alveolar bone). Gingival mesenchymal stem cells (GMSCs) are non-hematopoietic adult stem cells isolated from the gingival lamina propria. When compared to MSCs purified from various dental and non-dental tissues, GMSCs are more abundant in source, relatively non-invasive to obtain, and genetically stable. In recent years, many studies have found that GMSCs possess the ability of self-renewal, multi-directional differentiation, and chemotaxis to inflammatory sites for immunity regulation. Their molecular and stem-cell properties make them highly suitable for both preclinical and clinical research. Extracellular vesicles (EVs) secreted by GMSCs are of key interest due to their ability to emulate the biological and therapeutic activity of GMSCs themselves. This paper will therefore review the current consensus on GMSCs, surveying their sources and isolation methods, their biological properties, and their therapeutic applications on inflammatory and immune-related diseases.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".