Prevention and detection of bacterial pathogens on medical device materials
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,002 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».