An investigation of functionalized chitosan and alginate multilayer conformal nanocoating on mouse beta cell spheroids as a model for pancreatic islet transplantation
Bibliographic record
Abstract
Multilayer conformal coatings have been shown to provide a nanoscale barrier between cells and their environment with adequate stability, while regulating the diffusion of nutrition and waste across the cell membrane. The coating method aims to minimize capsule thickness and implant volume while reducing the need for immunosuppressive drugs, making it a promising approach for islet cell encapsulation in clinical islet transplantation for the treatment of Type 1 diabetes. This study introduces an immunoprotective nanocoating obtained through electrostatic interaction between quaternized phosphocholine-chitosan (PC-QCH) and tetrahydropyran triazole phenyl-alginate (TZ-AL) onto mouse β-cell spheroids. First, successful synthesis of the proposed polyelectrolytes was confirmed with physico-chemical characterization. A coating with an average thickness of 540 nm was obtained with self-assembly of 4-bilayers of PC-QCH/TZ-AL onto MIN6 β-cell spheroids. Surface coating of spheroids did not affect cell viability, metabolic activity, or insulin secretion, when compared to non-coated spheroids. The exposure of the polyelectrolytes to THP-1 monocyte-derived macrophages lead to a reduced level of TNF-α secretion and exposure of coated spheroids to RAW264.7 macrophages showed a decreasing trend in the secretion of TNF-α and IL-6. In addition, coated spheroids were able to establish normoglycemia when implanted into diabetic NOD-SCID mice, demonstrating in vivo biocompatibility and cellular function. These results demonstrate the ability of the PC-QCH/TZ-AL conformal coating to mitigate pro-inflammatory responses from macrophages, and thus can be a promising candidate towards nanoencapsulation for cell-based therapy, particularly in type 1 diabetes, where the insulin secreting β-cells are subjected to inflammation and immune cell attack.
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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) | 0.000 | 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 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".