Investigation of Three-Dimensional Bacterial Adhesion Manner on Model Organic Surfaces Using Quartz Crystal Microbalance with Energy Dissipation Monitoring

Bacterial biofilms reduce the performance and efficiency of biomedical and industrial devices. The initial step in forming bacterial biofilms is the weak and reversible attachment of the bacterial cells onto the surface. This is followed by bond maturation and secretion of polymeric substances, which initiate irreversible biofilm formation, resulting in stable biofilms. This implies that understanding the initial reversible stage of the adhesion process is crucial to prevent bacterial biofilm formation. In this study, we analyzed the adhesion processes of E. coli on self-assembled monolayers (SAMs) with different terminal groups using optical microscopy and quartz crystal microbalance with energy dissipation (QCM-D) monitoring. We found that a considerable number of bacterial cells adhere to hydrophobic (methyl-terminated) and hydrophilic protein-adsorbing (amine- and carboxy-terminated) SAMs forming dense bacterial adlayers while attaching weakly to hydrophilic protein-resisting SAMs [oligo(ethylene glycol) (OEG) and sulfobetaine (SB)], forming sparse but dissipative bacterial adlayers. Moreover, we observed positive shifts in the resonant frequency for the hydrophilic protein-resisting SAMs at high overtone numbers, suggesting how bacterial cells cling to the surface using their appendages as explained by the coupled-resonator model. By exploiting the differences in the acoustic wave penetration depths at each overtone, we estimated the distance of the bacterial cell body from different surfaces. The estimated distances provide a possible explanation for why bacterial cells tend to attach firmly to some surfaces and weakly to others. This result is correlated to the strength of the bacterium-substratum bonds at the interface. Elucidating how the bacterial cells adhere to different surface chemistries can be a suitable guide in identifying surfaces with a more significant probability of contamination by bacterial biofilms and designing bacteria-resistant surfaces and coatings with excellent bacterial antifouling characteristics.

Prediction of Serum Adsorption onto Polymer Brush Films by Machine Learning

Using machine learning based on a random forest (RF) regression algorithm, we attempted to predict the amount of adsorbed serum protein on polymer brush films from the films' physicochemical information and the monomers' chemical structures constituting the films using a RF model. After the training of the RF model using the data of polymer brush films synthesized from five different types of monomers, the model became capable of predicting the amount of adsorbed protein from the chemical structure, physicochemical properties of monomer molecules, and structural parameters (density and thickness of the films). The analysis of the trained RF quantitatively provided the importance of each structural parameter and physicochemical properties of monomers toward serum protein adsorption (SPA). The ranking for the significance of the parameters agrees with our general understanding and perception. Based on the results, we discuss the correlation between brush film's physical properties (such as thickness and density) and SPA and attempt to provide a guideline for the design of antibiofouling polymer brush films.

Analysis of Vicinal Water in Soft Contact Lenses Using a Combination of Infrared Absorption Spectroscopy and Multivariate Curve Resolution

In this paper, we propose a new spectroscopic method to explore the behavior of molecules near polymeric molecular networks of water-containing soft materials such as hydrogels. We demonstrate the analysis of hydrogen bonding states of water in the vicinity of hydrogels (soft contact lenses). In this method, we apply force to hydrated contact lenses to deform them and to modulate the ratio between the signals from bulk and vicinal regions. We then collect spectra at different forces. Finally, we extracted the spectra of the vicinal region using the multivariate curve resolution-alternating least square (MCR-ALS) method. We report the hydration states depending on the chemical structures of hydrogels constituting the contact lenses.