METAL AND ESSENTIAL OILS-BASED ANTIBACTERIAL SOLUTIONS: TOWARDS PATIENT-SPECIFIC IMPLANTABLE DEVICES
Bibliographic record
Abstract
Infection is among the main unmet challenges in orthopedics, causing severe societal and economical burden. Available options to prevent infection are limited and the raising incidence of microbial resistance is progressively reducing the applicability of antibiotic-eluting devices. Here we show two different approaches to functionalize implantable devices, without using antibiotics. One route exploits the deposition, by plasma-assisted techniques (Ionized Jet Deposition and Femto-Second Pulsed Laser Deposition) of metal-based coatings (silver, copper and zinc-based) on the prostheses surface. The second route is based on essential oils. Both approaches are also used to obtain antibacterial patches, manufactured by electrospinning, to be used alone or in combination with prostheses. Metal coatings composition (XRD), morphology and thickness (FEG-SEM, AFM), and biocompatibility (Alamar Blue test, fibroblasts) are measured, depending on deposition parameters. Then, antibacterial efficacy is measured on S. Aureus, E. Coli, E. Faecalis, and P. Aeruginosa, in terms of planktonic growth, adhesion and biofilm formation, exploiting the Calgary Biofilm Device. Our results show that coatings have tunable characteristics (thickness, surface roughness), and can be applied to a variety of substrates (ceramic, metallic and polymeric), including custom-made prostheses, with high reproducibility, adhesion to substrate and no defects. They are biocompatible and can inhibit planktonic and biofilm growth of gram + and gram – strains. Antimicrobial efficacy is strain-dependent, but complete inhibition can be achieved by tuning the films characteristics. These results suggest that selecting a metal based on microbial contamination would be a more promising approach compared to the sole use of silver, moving towards a personalized medicine in infection. For essential oils-based patches, we started from a screening of the antibacterial oils used in the literature, including the non-medical field. The selected oils (thymol, lavender, turmeric, ginger, lemon, cinnamon, citronella, tea tree, black pepper) are screened and those having the lowest MIC are selected and used for electrospinning (using PCL and PEO as a matrix). After electrospinning the patches, we measured their stability, morphology, antibacterial activity (against S. Aureus and E. Coli) and biocompatibility. Our results show that all oils can be effectively spun and are retained in the patch, thus providing high antibacterial action. The antibacterial activity depends on the oil and the polymeric matrix, with tea tree oil and black pepper being the most effective for the strains under examination. As for the metals, higher efficacy can be achieved by a proper selection of single/combined oils to address specific bacterial strains.
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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 | 0.000 | 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) | 0.000 | 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".