Bone Marrow–derived Cell Therapy for Oral Mucosal Repair after Irradiation
Bibliographic record
Abstract
Oral mucositis (ulcer) is a serious and painful side effect for patients with head and neck cancer following radiation therapy. However, current clinical strategies cannot efficiently prevent the occurrence of oral mucositis. In this study, we investigated whether bone marrow-derived cells (BMDCs) prevented the occurrence and/or decreased the severity of radiation-induced oral mucositis. Fresh concentrated BMDCs from male C3H mice were transplanted intravenously into female mice after tongue irradiation. For 14 days postirradiation, the changes of body weight and the time courses of ulceration were observed. Until the ulcer reached maximum size (7 days postirradiation), macroscopic and histologic analyses of harvested tongues were performed to detect the behavior of donor BMDCs. Between 2 and 5 days postirradiation, BMDCs-transplanted mice showed more expression of stem cell markers (c-Kit, Sca-1) and EGFR and fewer apoptotic cells when compared with nontransplanted control mice (irradiation group). On day 7, there were fewer and smaller ulcers observed in the BMDCs-transplanted group. Tongues of these mice had preserved their epithelial thickness, and regenerative activities (blood vessels formation, cell proliferation) were higher than they were in the irradiation group. Fluorescently labeled BMDCs were not detected in tongue epithelium but rather in connective tissue (dermis) just below the basal cell layer. These findings suggest that exogenous BMDCs behave to reduce radiogenic oral mucositis in a paracrine manner.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".