Mechanical deformation of elastomer medical devices can enable microbial surface colonization
Bibliographic record
Abstract
Abstract Elastomers such as silicone are common in medical devices (catheters, prosthetic implants, endoscopes), but they remain prone to microbial colonization and biofilm infections. For the first time, our work shows that rates of microbial surface attachment to polydimethylsiloxane (PDMS) silicone can be significantly affected by mechanical deformation. For a section of bent commercial catheter tubing, bacteria (P. aeruginosa) show a strong preference for the ‘convex’ side compared to the ‘concave’ side, by a factor of 4.2. Further testing of cast PDMS materials in bending only showed a significant difference for samples that were manually wiped (damaged) beforehand (1.75 × 104 and 6.02 × 103 cells/mm2 on the convex and concave sides, respectively). We demonstrate that surface microcracks in elastomers are opened under tensile stress (convex bending) to become ‘activated’ as sites for microbial colonization. This work demonstrates that the high elastic limit of elastomers enables these microcracks to reversibly open and close, as ‘dynamic defects’. Commercial catheters have relatively high surface roughness inherent to manufacturing, but we show that even manual wiping of newly-cast PDMS is sufficient to generate surface microcracks. We consider the implication for medical devices that feature sustained, surgical, or cyclic deformation, in which localized tensile conditions may expose these surface defects to opportunistic microbes. As a result, our work showcases serious potential problems in the widespread usage and development of elastomers in medical devices.
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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) | 0.002 | 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".