Combined Strategy of Endothelial Cells Coating, Sertoli Cells Coculture and Infusion Improves Vascularization and Rejection Protection of Islet Graft
Bibliographic record
Abstract
Improving islet graft revascularization and inhibiting rejection become crucial tasks for prolonging islet graft survival. Endothelial cells (ECs) are the basis of islet vascularization and Sertoli cells (SCs) have the talent to provide nutritional support and exert immunosuppressive effects. We construct a combined strategy of ECs coating in the presence of nutritious and immune factors supplied by SCs in a co-culture system to investigate the effect of vascularization and rejection inhibition for islet graft. In vivo, the combined strategy improved the survival and vascularization as well as inhibited lymphocytes and inflammatory cytokines. In vitro, we found the combinatorial strategy improved the function of islets and the effect of ECs-coating on islets. Combined strategy treated islets revealed higher levels of anti-apoptotic signal molecules (Bcl-2 and HSP-32), survival and function related molecules (PDX-1, Ki-67, ERK1/2 and Akt) and demonstrated increased vascular endothelial growth factor receptor 2 (KDR) and angiogenesis signal molecules (FAk and PLC-γ). SCs effectively inhibited the activation of lymphocyte stimulated by islets and ECs. Predominantly immunosuppressive cytokines could be detected in culture supernatants of the SCs coculture group. These results suggest that ECs-coating and Sertoli cells co-culture or infusion synergistically enhance islet survival and function after transplantation.
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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".