Glucose‐Responsive Bioinorganic Nanohybrid Membrane for Self‐Regulated Insulin Release
Bibliographic record
Abstract
Abstract A bioinorganic nanohybrid glucose‐responsive membrane is developed for self‐regulated insulin delivery analogous to a healthy human pancreas. The application of MnO 2 nanoparticles as a multifunctional component in a glucose‐responsive, protein‐based membrane with embedded pH‐responsive hydrogel nanoparticles is proposed. The bio‐nanohybrid membrane is prepared by crosslinking bovine serum albumin (BSA)–MnO 2 nanoparticle conjugates with glucose oxidase and catalase in the presence of poly( N ‐isopropyl acrylamide‐ co ‐methacrylic acid) nanoparticles. The preparation and performance of this new nanocomposite material for a glucose‐responsive insulin release system is presented. The activity and stability of immobilized glucose oxidase and the morphology and mechanical properties of the membrane are investigated. The enzymatic activity is well preserved in the membranes. The use of MnO 2 nanoparticles not only reinforces the mechanical strength and the porous structure of the BSA‐based membrane, but enhances the long‐term stability of the enzymes. The in vitro release of insulin across the membrane is modulated by changes in glucose concentration mimicking possible fluctuations of blood‐glucose level in diabetic patients. A four‐fold increase in insulin permeation is observed when the glucose concentration is increased from normal to hyperglycemic levels, which returns to the baseline level when the glucose concentration is reduced to a normal level.
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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 | 0.001 | 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".