Effects of Tissue-engineered Bone by Coculture of Adipose-derived Stem Cells and Vascular Endothelial Cells on Host Immune Status
Bibliographic record
Abstract
AIM: The study aimed to explore the effects of tissue-engineered bone constructed with partially deproteinized biologic bone (PDPBB) and coculture of adipose-derived stem cells (ADSCs) and vascular endothelial cells (VECs) on host immune status, providing a very useful clue for the future development of bone engineering. METHODS: Tissue-engineered bones constructed by PDPBB and ADSCs, VECs or coculture of them were implanted into the muscle bag of bilateral femurs of Sprague-Dawley rats. Partially deproteinized biologic bone alone and blank control were also implanted. After transplantation, the proliferation of implanted seed cells in tissue-engineered bones was labeled by bromodeoxyuridine staining. Moreover, the changes of T-lymphocyte subpopulations, including CD3 + CD4+ and CD3 + CD8+ in peripheral blood were then detected using flow cytometry to analyze the immune rejection of tissue-engineered bone implantation based on peripheral blood CD4/CD8 ratios. RESULTS: After transplantation, the proliferation of implanted seed cells was observed in tissue-engineered bones of different groups. At different time points after transplantation, the CD4+/CD8+ ratio in peripheral blood of PDPBB + ADSCs, PDPBB + coculture, and blank control groups did not exhibit significant change. Although the CD4+/CD8+ ratio in peripheral blood of PDPBB + VECs group was significantly higher than other group at 1 week after transplantation, that of PDPBB + VECs and PDPBB + coculture group was significantly decreased at 8 week after transplantation compared with that of blank control group. CONCLUSIONS: Our results indicated that there was no significant immune rejection after transplantation of tissue-engineered bone constructed with PDPBB and coculture of ADSCs and VECs as seed cells.
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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".