Polyacrylamide‐based hydrogel coatings improve biocompatibility of implanted pump devices
Bibliographic record
Abstract
The introduction of transcutaneous and subcutaneous implants and devices into the human body instigates fouling and foreign body responses (FBRs) 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. Here we sought to develop novel materials for use as coatings on subcutaneously implanted devices to reduce the FBR and local tissue inflammation in comparison to gold standard materials such as poly(ethylene glycol) and polyzwitterions. We prepared a library of polyacrylamide-based copolymer hydrogels, which were selected from materials previously shown to exhibit remarkable antifouling properties with blood and plasma, and implanted them into the subcutaneous space of mice to evaluate their biocompatibility over the course of 1 month. The top performing polyacrylamide-based copolymer hydrogel material, comprising a 50:50 mixture of N-(2-hydroxyethyl)acrylamide (HEAm) and N-(3-methoxypropyl)acrylamide (MPAm), exhibited significantly better biocompatibility and lower tissue inflammation than gold standard materials. Moreover, when applied to polydimethylsiloxane disks or silicon catheters as a thin coating (45 ± 1 μm), this leading copolymer hydrogel coating significantly improved implant biocompatibility. Using a rat model of insulin-deficient diabetes, we showed that insulin pumps fitted with HEAm-co-MPAm hydrogel-coated insulin infusion catheters exhibited improved biocompatibility and extended functional lifetime over pumps fitted with industry standard catheters. 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.
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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 | 0.004 | 0.001 |
| 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 | 0.000 |
| 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 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".