Human Fibroblast Sheet Promotes Human Pancreatic Islet Survival and Function in Vitro
Bibliographic record
Abstract
In previous work, we engineered functional cell sheets using bone marrow-derived mesenchymal stem cells (BM-MSCs) to promote islet graft survival. In the present study, we hypothesized that a cell sheet using dermal fibroblasts could be an alternative to MSCs, and then we aimed to evaluate the effects of this cell sheet on the functional viability of human islets. Fibroblast sheets were fabricated using temperature-responsive culture dishes. Human islets were seeded onto fibroblast sheets. The efficacy of the fibroblast sheets was evaluated by dividing islets into three groups: the islets-alone group, the coculture with fibroblasts group, and the islet culture on fibroblast sheet group. The ultrastructure of the islets cultured on each fibroblast sheet was examined by electron microscopy. The fibroblast sheet expression of fibronectin (as a component of the extracellular matrix) was quantified by Western blotting. After 3 days of culture, islet viabilities were 70.2 ± 9.8%, 87.4 ± 5.8%, and 88.6 ± 4.5%, and survival rates were 60.3 ± 6.8%, 65.3 ± 3.0%, and 75.8 ± 5.6%, respectively. Insulin secretions in response to high-glucose stimulation were 5.1 ± 1.6, 9.4 ± 3.8, and 23.5 ± 12.4 µIU/islet, and interleukin-6 (IL-6) secretions were 3.0 ± 0.7, 5.1 ± 1.2, and 7.3 ± 1.0 ng/day, respectively. Islets were found to incorporate into the fibroblast sheets while maintaining a three-dimensional structure and well-preserved extracellular matrix. The fibroblast sheets exhibited a higher expression of fibronectin compared to fibroblasts alone. In conclusion, human dermal fibroblast sheets fabricated by tissue-engineering techniques could provide an optimal substrate for human islets, as a source of cytokines and extracellular matrix.
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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 | 0.001 |
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".