Mechanisms underlying dental-derived stem cell-mediated neurorestoration in neurodegenerative disorders
Bibliographic record
Abstract
BACKGROUND: Neurodegenerative disorders have a complex pathology and are characterized by a progressive loss of neuronal architecture in the brain or spinal cord. Neuroprotective agents have demonstrated promising results at the preclinical stage, but this has not been confirmed at the clinical stage. Thus far, no neuroprotective drug that can prevent neuronal degeneration in patients with neurodegenerative disorders is available. MAIN BODY: Recent studies have focused on neurorestorative measures, such as cell-based therapy, rather than neuroprotective treatment. The utility of cell-based approaches for the treatment of neurodegenerative disorders has been explored extensively, and the results have been somewhat promising with regard to reversing the outcome. Because of their neural crest origin, ease of harvest, accessibility, ethical suitability, and potential to differentiate into the neurogenic lineage, dental-derived stem cells (DSCs) have become an attractive source for cell-based neurorestoration therapies. In the present review, we summarize the possible use of DSC-based neurorestoration therapy as an alternative treatment for neurodegenerative disorders, with a particular emphasis on the mechanism underlying recovery in neurodegenerative disorders. CONCLUSION: Transplantation research in neurodegenerative diseases should aim to understand the mechanism providing benefits both at the molecular and functional level. Due to their ease of accessibility, plasticity, and ethical suitability, DSCs hold promise to overcome the existing challenges in the field of neurodegeneration through multiple mechanisms, such as cell replacement, bystander effect, vasculogenesis, synaptogenesis, immunomodulation, and by inhibiting apoptosis.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".