The Antibacterial Activity of Silicone-Polyether Surfactants
Notice bibliographique
Résumé
The increase in microbial resistance to antibiotics underscores the need for novel antibacterial surfaces, particularly for silicone-based implants, because the hydrophobicity of silicones has been linked to undesirable microbial adhesion and biofilm formation. Unfortunately, current strategies for mitigation, such as pretreatment of surfaces with antiseptics/antibiotics, are not consistently effective. In fact, they can facilitate the prevalence of resistant pathogens by exposing bacteria to sublethal concentrations of biocides. Therefore, scientific interest has shifted to preventing initial adhesion (prior to surface colonization) by using surfactants as surface modifiers. Accordingly, Chapter 2 studied the bioactivity of ACR-008 UP (an acrylic-terminated superwetting silicone surfactant) after it was copolymerized in increasing weight percentages with butyl methacrylate (BMA) and/or methyl methacrylate (MMA). Interestingly, copolymers of 20 wt % ACR showed at least 3x less adhesion by Escherichia coli BL21 (E. coli) than any other formulation. This was not a consequence of wettability, which followed a parabolic function with ACR concentration: high contact angles (CA) with sessile water drops were observed at both low (< 20 wt %) and high (> 80 wt %) concentrations of ACR in materials. The CA at 20 wt % ACR was 66°. The lack of E. coli adhesion was ascribed to surfactant-membrane interactions; hence, the antibacterial potential of compounds related to ACR was further probed. Chapter 3, therefore, examines the structure-activity relationships of nonionic silicone polyether surfactants in solution. Azide/alkyne click chemistry was used to prepare a series of eight compounds with consistent hydrophilic tails (8- 44 poly(ethylene glycol) units), but variable hydrophobic heads (branched silicones with 3-10 siloxane linkages, and in two cases phenyl substitutions). The compounds were tested for toxicity at 0.001 w/v %, 2.5 w/v % and their critical micelle concentrations (CMCs), against different concentrations of E. coli in a 3-step assay. Surfactants with smaller head groups had as much as 4x the bioactivity of larger analogues, with the smallest hydrophobe exhibiting potency equivalent to SDS. Smaller PEG chains were similarly associated with higher potency. This data suggests that lower micelle stability, and the theoretically enhanced permeability of smaller silicone head groups in membranes, is linked to antibacterial activity. The results further demonstrate that the simple manipulation of nonionic silicone polyether structures, leads to significant changes in antibacterial action. To ensure similar results were achievable when such surfactants are immobilized on surfaces, 8 compounds with shorter, ethoxysilylpropyl-terminated PEG chains, and branched or linear hydrophobes, were incorporated into a homemade, room temperature vulcanization (RTV) silicone (Chapter 4). The materials, containing 0- 20 wt% surfactants) were then tested for contact killing and cytophobicity against the same E. coli strain. Elastomers modified with 0.5- 1 wt% of (EtO)3Si-PEG- laurate, and separately (EtO)3Si-PEG-tBS, were on average 2x more hydrophilic relative to controls (103°) and differed in their wettability by ~40°, yet both were anti-adhesive; a ~30-fold reduction in adhesion was seen on modified surfaces relative to the control PDMS. Additionally, the (EtO)3Si-PEG-tBS surface demonstrated biocidal behavior, which further highlighted the importance of surfactant chemistry- not just wettability- in observing a specific antibacterial response (if any). Based on the data collated from each Chapter, silicone surfactants seem to have great potential as bioactive agents and warrant further systematic investigations into their mechanisms of action. In so doing, their chemistry may be optimized against different microbes for a variety of applications. In particular, their potential to create non-toxic, cytophobic silicones is particularly encouraging, given the need for anti-adhesive, biofilm preventing material surfaces.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».