Uncoupling bacterial attachment on and detachment from polydimethylsiloxane surfaces through empirical and simulation studies

A rapid and specific antimicrobial resistance detection of Escherichia coli via magnetic nanoclusters

Drinking water contamination, often caused by bacteria, leads to substantial numbers of diarrhea deaths each year, especially in developing regions. Human urine as a source of fertilizer, when handled improperly, can contaminate drinking water. One dominant bacterial pathogen in urine is Escherichia coli, which can trigger serious waterborne/foodborne diseases. Considering the prevalence of the multi-drug resistant extended-spectrum beta-lactamase (ESBL) producing E. coli, a rapid detection method for resistance is highly desired. In this work, we developed a method for quick identification of E. coli and, at the same time, capable of removal of general bacterial pathogens from human urine. A specific peptide GRHIFWRRGGGHKVAPR, reported to have a strong affinity to E. coli, was utilized to modify the PEGylated magnetic nanoclusters, resulting in a specific capture and enrichment of E. coli from the bacteria-spiked artificial urine. Subsequently, a novel luminescent probe was applied to rapidly identify the antimicrobial resistance of the collected E. coli within 30 min. These functionalized magnetic nanoclusters demonstrate a promising prospect to rapidly detect ESBL E. coli in urine and contribute to reducing drinking water contamination.

Specific capture of Pseudomonas aeruginosa for rapid detection of antimicrobial resistance in urinary tract infections

Urinary tract infections (UTIs) are among the most predominant microbial diseases, leading to substantial healthcare burdens and threatening human well-being. UTIs can become more critical when caused by Pseudomonas aeruginosa, particularly by antimicrobial-resistant types. Thereby a rapid diagnosis and identification of the antimicrobial-resistant P. aeruginosa can support and guide an efficient medication and an effective treatment toward UTIs. Herein, we designed a platform for prompt purification, and effective identification of P. aeruginosa to combat the notorious P. aeruginosa associated UTIs. A peptide (QRKLAAKLT), specifically binding to P. aeruginosa, was grafted onto PEGylated magnetic nanoclusters and enabled a successful capture and enrichment of P. aeruginosa from artificial human urine. Rapid identification of antimicrobial resistance of the enriched P. aeruginosa can be moreover accomplished within 30 min. These functionalized magnetic nanoclusters demonstrate a prominent diagnostic potential to combat P. aeruginosa associated UTIs, which can be extended to other P. aeruginosa involved infections.

Ultrafast Determination of Antimicrobial Resistant Staphylococcus aureus Specifically Captured by Functionalized Magnetic Nanoclusters

Sepsis, the systemic response to infection, is a life-threatening situation for patients and leads to high mortality, especially when caused by antimicrobial resistant pathogens. Prompt diagnosis and identification of the pathogenic bacteria, including their antibiotic resistance, are highly desired to yield a timely decision for treatment. Here, we aim to develop a platform for rapid isolation and efficient identification of Staphylococcus aureus, the most frequently occurring pathogen in sepsis. A peptide (VPHNPGLISLQG, SA5-1), specifically binding to S. aureus, was conjugated to the PEGylated magnetic nanoclusters, successfully enabling the specific capture and enrichment of S. aureus from blood serum. Consequently, fast detection of the antimicrobial resistance of the collected S. aureus was achieved within 30 min using a novel luminescent probe. These magnetic nanoclusters manifest a promising diagnostic prospect to combat sepsis.

Robust Antibacterial Activity of Xanthan-Gum-Stabilized and Patterned CeO2-x-TiO2 Antifog Films

Increased occurrence of antimicrobial resistance leads to a huge burden on patients, the healthcare system, and society worldwide. Developing antimicrobial materials through doping rare-earth elements is a new strategy to overcome this challenge. To this end, we design antibacterial films containing CeO_2-x-TiO₂, xanthan gum, poly(acrylic acid), and hyaluronic acid. CeO_2-x-TiO₂ inks are additionally integrated into a hexagonal grid for prominent transparency. Such design yields not only an antibacterial efficacy of ∼100% toward Staphylococcus aureus and Escherichia coli but also excellent antifog performance for 72 h in a 100% humidity atmosphere. Moreover, FluidFM is employed to understand the interaction in-depth between bacteria and materials. We further reveal that reactive oxygen species (ROS) are crucial for the bactericidal activity of E. coli through fluorescent spectroscopic analysis and SEM imaging. We meanwhile confirm that Ce³⁺ ions are involved in the stripping phosphate groups, damaging the cell membrane of S. aureus. Therefore, the hexagonal mesh and xanthan-gum cross-linking chains act as a reservoir for ROS and Ce³⁺ ions, realizing a long-lasting antibacterial function. We hence develop an antibacterial and antifog dual-functional material that has the potential for a broad application in display devices, medical devices, food packaging, and wearable electronics.

Machine learning ensures rapid and precise selection of gold sea-urchin-like nanoparticles for desired light-to-plasmon resonance

Sustainable energy strategies, particularly solar-to-hydrogen production, are anticipated to overcome the global reliance on fossil fuels. Thereby, materials enabling the production of green hydrogen from water and sunlight are continuously designed, e.g., ZnO nanostructures coated by gold sea-urchin-like nanoparticles, which employ the light-to-plasmon resonance to realize photoelectrochemical water splitting. But such light-to-plasmon resonance is strongly impacted by the size, the species, and the concentration of the metal nanoparticles coating on the ZnO nanoflower surfaces. Therefore, a precise prediction of the surface plasmon resonance is crucial to achieving an optimized nanoparticle fabrication of the desired light-to-plasmon resonance. To this end, we synthesized a substantial amount of metal (gold) nanoparticles of different sizes and species, which are further coated on ZnO nanoflowers. Subsequently, we utilized a genetic algorithm neural network (GANN) to obtain the synergistically trained model by considering the light-to-plasmon conversion efficiencies and fabrication parameters, such as multiple metal species, precursor concentrations, surfactant concentrations, linker concentrations, and coating times. In addition, we integrated into the model's training the data of nanoparticles due to their inherent complexity, which manifests the light-to-plasmon conversion efficiency far from the coupling state. Therefore, the trained model can guide us to obtain a rapid and automatic selection of fabrication parameters of the nanoparticles with the anticipated light-to-plasmon resonance, which is more efficient than an empirical selection. The capability of the method achieved in this work furthermore demonstrates a successful projection of the light-to-plasmon conversion efficiency and contributes to an efficient selection of the fabrication parameters leading to the anticipated properties.