Effect of radiation and cell implantation on wound healing in a rat model
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Having shown that intra-dermal injection of fibroblasts decreases the effect of radiation on healing of superficial wounds, we now test the effect of fibroblasts and syngeneic marrow stromal cells on irradiated deep and superficial wounds. METHODS: Wistar rats received bilateral buttock irradiation followed by partial excision of the gluteus muscle bilaterally. In Protocol 1, one irradiated wound was treated with 1.2 x 10(7) autologous cells injected intra-dermally. In Protocol 2, the experimental side was treated with a fibrin and autologous cell implant (1.2 x 10(7) cells). Twenty-one days later, wound mechanical characteristics were tested. In Protocol 3, the effect of pooled marrow stromal cells on healing of superficial irradiated wounds in Lewis rats was similarly tested. RESULTS: The fibrin-fibroblast implant (Protocol 2) had no effect on wound mechanics. Superficial injection of fibroblasts (Protocol 1) significantly improved wound breaking strength when compared to the control group (mean +/- SEM, breaking strength: treated 504.6 +/- 37.0 g vs. control 353.4 +/- 35.2 g, P = 0.005). The dermal injection of marrow stromal cells also resulted in marked increases in breaking strength (mean +/- SEM, breaking strength: treated 338.5 +/- 39.9 g vs. control 187.1 +/- 12.0 g, P < 0.01). In both Protocols 1 and 3, ultimate tensile strength and toughness were increased in the side receiving cell transplantation. CONCLUSIONS: Cell implantation holds promise for decreasing the effect of radiation on healing of irradiated wounds.
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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".