Achievements and Challenges in Transplantation of Mesenchymal Stem Cells in Otorhinolaryngology
Bibliographic record
Abstract
Otorhinolaryngology enrolls head and neck surgery in various tissues such as ear, nose, and throat (ENT) that govern different activities such as hearing, breathing, smelling, production of vocal sounds, the balance, deglutition, facial animation, air filtration and humidification, and articulation during speech, while absence of these functions can lead to high morbidity and even mortality. Conventional therapies for head and neck damaged tissues include grafts, transplants, and artificial materials, but grafts have limited availability and cause morbidity in the donor site. To improve these limitations, regenerative medicine, as a novel and rapidly growing field, has opened a new therapeutic window in otorhinolaryngology by using cell transplantation to target the healing and replacement of injured tissues. There is a high risk of rejection and tumor formation for transplantation of embryonic stem cells (ESCs) and induced pluripotent stem cells (iPSCs); mesenchymal stem cells (MSCs) lack these drawbacks. They have easy expansion and antiapoptotic properties with a wide range of healing and aesthetic functions that make them a novel candidate in otorhinolaryngology for craniofacial defects and diseases and hold immense promise for bone tissue healing; even the tissue sources and types of MSCs, the method of cell introduction and their preparation quality can influence the final outcome in the injured tissue. In this review, we demonstrated the anti-inflammatory and immunomodulatory properties of MSCs, from different sources, to be safely used for cell-based therapies in otorhinolaryngology, while their achievements and challenges have been described too.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".