Polyacrylamide-based Hydrogel Coatings Improve Biocompatibility of Implanted Pump Devices
Bibliographic record
Abstract
Abstract The introduction of transcutaneous and subcutaneous implants and devices into the human body instigates fouling and foreign body responses that limit their functional lifetimes. Polymer coatings are a promising solution to improve the biocompatibility of such implants, with potential to enhance in vivo device performance and prolong device lifetime. In this work we sought to develop novel materials that can be used as coatings on devices. We first synthesized a library of polyacrylamide-based copolymer hydrogels and implanted them into the subcutaneous space of mice to evaluate their biocompatibility. We sought to discover copolymer hydrogel formulations eliciting less fibrous capsule formation and lower overall inflammation than gold standard materials such as PEG and polyzwitterions. The top performing copolymer hydrogel materials were then shown to improve the biocompatibility of a commonly used medical material, PDMS. Lastly, we applied the leading hydrogel coating to the catheter tips of insulin pumps. In a rat model of insulin deficient diabetes, the functional lifetime of hydrogel-coated devices was extended over uncoated pumps. These polyacrylamide-based copolymer hydrogel coatings have the potential to improve device function and lifetime, thereby reducing the burden of disease management for people regularly using implanted devices, such as those with diabetes.
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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".