Prevention and detection of bacterial pathogens on medical device materials
Bibliographic record
Abstract
With a predicted ten million deaths per year estimated by the year 2050, alternative strategies in combating microbial infection are required. Current techniques to combat these infections rely on the heavy use of antibiotics, however, an increase in antimicrobial resistance has led to these predicted numbers. The leading cause of antimicrobial resistance is the biofilm various bacteria produce to increase their survivability, however, this production of a natural polysaccharide barrier only occurs when bacterial cells adhere to a surface, such as medical devices. By preventing this adhesion, it may be possible to slow down the rate of antimicrobial resistance and ensure this prediction is never realised. The work presented in this thesis covers three aspects in preventing and detecting bacterial pathogens on medical devices. Firstly, copper oxide nanoparticles were synthesised and characterised both uncoated and coated in glutamic acid. These glutamic acid-coated nanoparticles (Glu-CuO NPs) were coated onto various medical materials, using 3-mercaptopropyltrimethoxysilane (MPTMS) to create an antimicrobial coating. The coating was applied using a dip coating and spray coating on various medical-grade materials. Leaching of the coatings was evaluated and found (8 ± 35 mg L-1) and (2.7 ± 1.1 mg L-1) for dip coating and spray coatings respectively. The coating materials were also tested against HaCaT epithelial cells to determine the toxicity of MPTMS (>0.1 mol L-1) and the Glu-CuO NPs (325 mg L-1). Various pathogens, including ESKAPE pathogens, had their phenotypic and genotypic antibiotic resistance profiles evaluated and compared with slight concordance (53.7 %). The Glu-CuO NPs were tested on these species finding the minimum bactericidal concentration depending on species (325 mg L-1). The nanoparticle coating was evaluated using a modified Minimum Biofilm Eradication Concentration (MBEC) assay and the reduction in viable counts was measured at both the MBC concentration and at 20 times MIC concentration (1-2 log reduction and 3-4 log reduction respectively), with the same results seen using a CDC bioreactor. Finally, non-invasive measurements and imaging of different bacterial species were made using NMR and MRI to determine when a biofilm infection has occurred. T1 and T2 relaxation values were measured using a 1.5 T Siemens Avanto scanner, between 2863 ms for T1 measurements and 1100 ms for T2eff). Additional measurements of the apparent diffusion coefficient (ADC) and self-diffusion coefficient (SDC) were taken, with changes in ADC and SDC could be observed by the decrease in diffusion coefficient, during the first two days, then an increase afterwards. This indicated property changes of the media and could be used to evaluate contamination indirectly. T1 and T2 measurements of the porcine tracheal wall were taken to ensure enough contrast was observable. The measured values between the tracheal wall and biofilm were significantly different, such that both T1-weighted and T2eff weighted imaging sequences could be used, to visualise the biofilm on the silicone tube. These results showed that copper oxide nanoparticles functionalised with an amino acid can be used as an antimicrobial coating when adhered at non-toxic concentrations to human epithelial cells. The coating can be applied to different medical materials and by different coating techniques allowing for reapplication of an antimicrobial coating on ad-hoc modified medical devices prior to surgery and offering an alternative to submerging a device in antibiotics. Finally, MRI can be used to non-invasively detect biofilm formation in an intubated porcine pluck, where a week-old biofilm is visible on silicone tube. This offers an alternative to invasive techniques and should be adopted to reduce the use of invasive techniques when determining biofilm infection, whilst also enabling earlier detect biofilms earlier. Collectively, this work shows new methods for preventing and detecting biofilm formation on medical devices, with a particular focus on endotracheal tubes.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".