Human Bone Marrow Cell Extracts Mitigate Radiation Injury to Salivary Gland
Bibliographic record
Abstract
Mitigation of irradiation injury to salivary glands was previously reported using a cell-free extract from mouse bone marrow. However, to bring this potential therapy a step closer to clinical application, a human bone marrow cell extract (BMCE) needs to be tested. Here, we report that irradiation-induced injury of salivary glands in immunocompetent mice treated with human BMCE secreted 50% more saliva than saline-injected mice, and BMCE did not cause additional acute inflammatory reaction. In addition, to identify the cell fraction in BMCE with the most therapeutic activity, we sorted human bone marrow into 3 cell fractions (mononuclear, granulocyte, and red blood cells) and tested their respective cell extracts. We identified that the mononuclear cell extract (MCE) provided the best therapeutic efficacy. It increased salivary flow 50% to 73% for 16 wk, preserved salivary parenchymal and stromal cells, and doubled cell proliferation rates while producing less inflammatory response. In contrast, the cell extract of granulocytes was of shorter efficacy and induced an acute inflammatory response, while that from red blood cells was not therapeutically effective for salivary function. Several proangiogenic (MMP-8, MMP-9, VEGF, uPA) and antiangiogenic factors (TSP-1, PF4, TIMP-1, PAI-1) were identified in MCE. Added advantages of BMCE and MCE for potential clinical use were that cell extracts from both male and female donors were comparably bioactive and that cell extracts could be stored and transported much more conveniently than cells. These findings suggest human BMCE, specifically the MCE fraction, is a promising therapy against irradiation-induced salivary hypofunction.
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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) | 0.000 | 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) | 0.002 | 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".