Engineering surgical stitches to prevent bacterial infection
Bibliographic record
Abstract
Abstract Surgical site infections (SSIs) account for a massive economic, physiological, and psychological burden on patients and health care providers. Sutures provide a surface to which bacteria can adhere, proliferate, and promote SSIs. Current methods for fighting SSIs involve the use of sutures coated with common antibiotics (triclosan). Unfortunately, these antibiotics have been rendered ineffective due to the increasing rate of antibiotic resistance. A promising new avenue involves the use of metallic nanoparticles (MNPs). MNPs exhibit low cytotoxicity and a strong propensity for killing bacteria while evading the typical antibiotic resistance mechanisms. In this work, we developed a novel MNPs dip-coating method for PDS-II sutures and explored the capabilities of a variety of MNPs in killing bacteria while retaining the cytocompatibility. Our findings indicated that our technique provided a homogeneous coating for PDS-II sutures, maintaining the strength, structural integrity, and degradability. The MNP coatings possess strong in vitro antibacterial properties against P aeruginosa and S. aureus —varying the %of dead bacteria from ~ 40% (for MgO NPs) to ~ 90% (for Fe 2 O 3 ) compared to ~ 15% for uncoated PDS-II suture, after 7 days. All sutures demonstrated minimal cytotoxicity (cell viability > 70%) reinforcing the movement towards the use MNPs as a viable antibacterial technology.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
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".