Cell extracts from spleen and adipose tissues restore function to irradiation‐injured salivary glands
Bibliographic record
Abstract
A cell extract from whole bone marrow (BM), which we named "BM Soup," has the property to restore saliva secretion to irradiation (IR)-injured salivary glands (SGs). However, BM cell harvesting remains an invasive procedure for the donor. The main objective of this study was to test the therapeutic effect of "Cell Soups" obtained from alternate tissues, such as adipose-derived stromal cells (ADSCs) and spleen cells to repair SGs. BM Soup, Spleen Soup, ADSC Soup, or saline (vehicle control) was injected intravenously into mice with IR-injured SGs (13Gy). Results demonstrated that all three cell soups restored 65-70% of saliva secretion, protected acinar cells, blood vessels, and parasympathetic nerves, and increased cell proliferation. Although protein array assays identified more angiogenesis-related growth factors in ADSC Soup, the length of its therapeutic efficiency on saliva flow was less than that of the BM Soup and Spleen Soup. Another objective of this study was to compare "Fresh" versus "Cryopreserved (-80 °C)" BM Soup. It was found that the therapeutic effect of 12-month "Cryopreserved BM Soup" was comparable to that of "Fresh BM Soup" on the functional restoration of IR-injured SGs. In conclusion, both Spleen Soup and ADSC Soup can be used to mitigate IR-damaged SGs.
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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".