Winner of the student award in the undergraduate category, 10th World Biomaterials Congress, May 17–22, 2016, Montreal QC, Canada: Evaluation of the tissue response to alginate encapsulated islets in an omentum pouch model
Bibliographic record
Abstract
Islet transplantation is currently in clinical use as a treatment for type I diabetes, but donor shortages and long-term immunosuppression limit broad application. Alginate microcapsules coated with poly-l-ornithine can be used to encapsulate islets in an environment that allows diffusion of glucose, insulin, nutrients, and waste products while inhibiting cells and antibodies. While clinical trials are ongoing using islets encapsulated in alginate microbeads, there are concerns in regards to long-term stability. Evaluation of the local tissue response following implantation provides insight into the underlying mechanisms contributing to biomaterial failure, which can be used to the design of new material strategies. Macrophages play an important role in driving the response. In this study, the stability of alginate microbeads coated with PLO containing islets transplanted in the omentum pouch model was investigated. Biomaterial structure and the inflammatory response were characterized by X-ray phase contrast (XPC) μCT imaging, histology, and immunostaining. XPC allowed evaluation of microbead 3D structure and identification of failed and stable microbeads. A robust inflammatory response characterized by high cell density and the presence of pro-inflammatory macrophages was found around the failed grafts. The results obtained provide insight into the local tissue response and possible failure mechanisms for alginate microbeads. © 2016 Wiley Periodicals, Inc. J Biomed Mater Res Part A: 104A: 1581-1590, 2016.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".