The effect of allogenic human Wharton's jelly stem cells seeded onto acellular dermal matrix in healing of rat burn wounds
Bibliographic record
Abstract
Abstract Background Various methods were introduced to overcome the autograft shortage in burn wound care, including cell transplantation and tissue engineering. Aims To evaluate the healing effect of allogenic human Wharton's jelly stem cells (hWJSCs) seeded onto acellular dermal matrix (ADM) in rat burn injuries. Patients and Methods Human Wharton's jelly stem cells provided from umbilical cord tissue were characterized before transplantation, and the growth kinetic was determined. Skin samples from cosmetic surgeries were used for preparation of ADM. Forty male Sprague Dawley rats were randomly divided into 4 equal groups. Third‐degree burn was induced for all animals by exposing to hot water using a 2 cm ring for 10 seconds. Group 1 was burned rats that did not receive any treatment. After burn injury, the second group received silver sulfadiazine (SSD), the third group was treated just by using ADM, and the fourth group received 2 × 106 hWJSCs seeded onto ADM. The animals were euthanized for histologic evaluation after 7, 14, and 21 days. Results Human Wharton's jelly stem cells were characterized to be spindle shape and positive for osteogenic and adipogenic induction and for mesenchymal markers but lacked hematopoietic markers. Population doubling time (PDT) was 40.1 hours with an increasing growth trend until day 6th. Macro‐ and microscopically, the healing was mild in ADM group and moderate in ADM + hWJSCs group after 21 days. Conclusion Allogenic hWJSCs seeded onto ADM improved the healing process in burn wounds denoting to their therapeutic and anti‐inflammatory effects in burn wounds that can be added to the literature.
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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 | 0.000 |
| 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".