Disintegration and Machine-Learning-Assisted Identification of Bacteria on Antimicrobial and Plasmonic Ag-CuxO Nanostructures

Developing a High-Throughput Platform for the Discovery of Sustainable Antibacterial Materials

Healthcare-associated infections (HCAIs) pose a significant global health challenge, exacerbated by the rising threat of antimicrobial resistance (AMR). This study introduces a high-throughput platform designed to identify sustainable antibacterial surfaces, exemplified by a copper-silver-zirconium (CuAgZr) alloy library. Utilizing combinatorial synthesis and advanced characterization techniques, material libraries (MatLibs) are generated and evaluated to rapidly screen diverse alloy compositions. The results demonstrate the ability to reproducibly create alloys with significant antimicrobial properties and high hardness, making them suitable for biomedical applications. The study highlights the critical role of compositional precision in developing materials that balance mechanical strength with antibacterial efficacy. Additionally, this approach ensures significant cost-effectiveness, facilitating the identification of economically viable alloy compositions. This research underscores the potential of high-throughput materials science to expedite the discovery of sustainable solutions for reducing HCAIs and addressing AMR, signaling a leap forward in sustainable healthcare material development.

Activation of epidermal growth factor receptors in triple-negative breast cancer cells by morphine; analysis through Raman spectroscopy and machine learning

Triple negative breast cancer (TNBC) is a very aggressive form of breast cancer, and the analgesic drug morphine has been shown to promote the proliferation of TNBC cells. This article investigates whether morphine causes activation of epidermal growth factor receptors (EGFR), the roles of μ-opioid and EGFR receptors on TNBC cell proliferation and migration. While examining the changes with molecular techniques, we also aimed to investigate the analysis ability of Raman spectroscopy and machine learning-based approach. Effects of morphine on the proliferation and migration of MDA.MB.231 cells were evaluated by MTT and scratch wound-healing tests, respectively. Morphine-induced phosphorylation of the EGFR was analyzed by western blotting in the presence and absence of μ-receptor antagonist naltrexone and the EGFR-tyrosine kinase inhibitor gefitinib. Morphine-induced EGFR phosphorylation and cell migration were significantly inhibited by pretreatments with both naltrexone and gefitinib; however, morphine-increased cell proliferation was inhibited only by naltrexone. While morphine-induced changes were observed in the Raman scatterings of the cells, the inhibitory effect of naltrexone was analyzed with similarity to the control group. Principal component analysis (PCA) of the Raman confirmed the epidermal growth factor (EGF)-like effect of morphine and was inhibited by naltrexone and partly by gefitinib pretreatments. Our in vitro results suggest that combining morphine with an EGFR inhibitor or a peripherally acting opioidergic receptor antagonist may be a good strategy for pain relief without triggering cancer proliferation and migration in TNBC patients. In addition, our results demonstrated the feasibility of the Raman spectroscopy and machine learning-based approach as an effective method to investigate the effects of agents in cancer cells without the need for complex and time-consuming sample preparation. The support vector machine (SVM) with linear kernel automatically classified the effects of drugs on cancer cells with ∼95% accuracy.

Sea hedgehog-inspired surface-enhanced Raman scattering biosensor probe for ultrasensitive determination of Staphylococcus aureus in food supplements

Staphylococcus aureus contamination in food supplements poses substantial challenges to public health and large-scale production but the sensitive detection in a timely manner remains a bottleneck. Drawing inspiration from the sea hedgehog, gold nanostars (AuNSs) were leveraged to design an ultrasensitive surface-enhanced Raman scattering (SERS) biosensor for the determination of Staphylococcus aureus in food supplements. Besides the surface enhancement furnished by the AuNSs, Raman reporter molecules and specific aptamers sequentially self-assembled onto these AuNSs to construct the "three-in-one" SERS biosensor probe for label-based quantitation of Staphylococcus aureus. Following incubation with contaminated health product samples, the gold nanostars@Raman reporter-aptamer specifically recognize and assemble around Staphylococcus aureus cells, forming a distinctive sea hedgehog structure. This unique configuration results in an amplified Raman signal at 1338 cm-1 and an enhancement factor of up to 6.71 × 107. The entire quantitative detection process can be completed within 30 min, boasting an exceptional limit of detection as low as 1.0 CFU mL-1. The method exhibits a broad working range for the determination of Staphylococcus aureus, with concentrations spanning 2.15 CFU mL-1 to 2.15 × 105 CFU mL-1. Furthermore, it demonstrates outstanding precision, with relative standard deviation values consistently below 5.0%. As a showcase to validate the practicality of the SERS method, we conducted tests on determining Staphylococcus aureus in a herbal food supplement, i.e., Ginkgo Biloba extract (GBE); the results align closely with those obtained through the conventional lysogeny broth agar plate method, pointing to the potential applicability in real-world scenarios.

A novel multifunctional SERS microfluidic sensor based on ZnO/Ag nanoflower arrays for label-free ultrasensitive detection of bacteria

This study proposes a microfluidic platform for rapid enrichment and ultrasensitive SERS detection of bacteria. The platform comprises ZnO nanoflower arrays decorated with silver nanoparticles to enhance the SERS sensitivity. The ZnO nanoflower array substrate with a 3D reticular columnar structure is prepared using the hydrothermal method. SEM analysis depicts the 3.05 μm gap distribution of the substrate array to intercept the most bacteria in the particle sizes range of 0.5 to 3 μm. Then, silver nanoparticles are deposited on the ZnO nano-array surface by liquid evaporation self-assembly. TEM and SEM analysis indicate nanosize of Ag particles, evenly distributed on the substrate, enhancing the SERS efficiency and improving sensing reproducibility. The probe molecules (R6G) are tested to demonstrate the high SERS activity of the proposed microfluidic sensor. Then, Escherichia coli, Staphylococcus aureus, Enterococcus faecalis, and Bacillus subtilis are selected, demonstrating the sensor's excellent bacterial capture and sensitive recognition capabilities, with a detection limit as low as 102 CFU mL-1. Additionally, the antibacterial properties of ZnO/Ag heterojunction nanostructures are studied, suggesting their ability to inactivate bacteria. Compared with the traditional Au-enhanced chip, the sensor preparation is easy, safe, reliable, and low-cost. Moreover, the ZnO nano-array exhibits a large specific surface area, high interception ability, stronger and uniform SERS performance, and effective and reliable detection of trace pathogens. This work provides potential future ZnO/Ag microfluidic SERS sensor applications for rapid, unlabeled, and trace pathogens detection in clinical and environmental applications, potentially achieving breakthroughs in early detection, prevention, and treatment.

ZnO@Ag-Functionalized Paper-Based Microarray Chip for SERS Detection of Bacteria and Antibacterial and Photocatalytic Inactivation

Bacterial infections caused by pathogenic microorganisms have become a serious, widespread health concern. Thus, it is essential and required to develop a multifunctional platform that can rapidly and accurately determine bacteria and effectively inhibit or inactivate pathogens. Herein, a microarray SERS chip was successfully synthesized using novel metal/semiconductor composites (ZnO@Ag)-ZnO nanoflowers (ZnO NFs) decorated with Ag nanoparticles (Ag NPs) arrayed on a paper-based chip as a supporting substrate for in situ monitoring and photocatalytic inactivation of pathogenic bacteria. Typical Gram-positive Staphylococcus aureus and Gram-negative Escherichia coli and Vibrio parahemolyticus were selected as models. Partial least-squares discriminant analysis (PLS-DA) was performed to minimize the dimensionality of SERS spectra data sets and to develop a cost-effective identification model. The classification accuracy was 100, 97.2, and 100% for S. aureus, E. coli, and V. parahemolyticus, respectively. The antimicrobial activity of ZnO@Ag was proved by the microbroth dilution method, and the minimum inhibitory concentrations (MICs) of S. aureus, E. coli, and V. parahemolyticus were 40, 50, and 55 μg/mL, respectively. Meanwhile, it demonstrated remarkable photocatalytic performance under natural sunlight for the inactivation of pathogenic bacteria, and the inactivation rates for S. aureus, E. coli, and V. parahemolyticus were 100, 97.03 and 97.56%, respectively. As a result, the microarray chip not only detected the bacteria with high sensitivity but also confirmed the antibacterial and photocatalytic sterilization properties. Consequently, it offers highly prospective strategies for foodborne diseases caused by pathogenic bacteria.

Surface-Enhanced Raman Scattering-Based Surface Chemotaxonomy: Combining Bacteria Extracellular Matrices and Machine Learning for Rapid and Universal Species Identification

Rapid, universal, and accurate identification of bacteria in their natural states is necessary for on-site environmental monitoring and fundamental microbial research. Surface-enhanced Raman scattering (SERS) spectroscopy emerges as an attractive tool due to its molecule-specific spectral fingerprinting and multiplexing capabilities, as well as portability and speed of readout. Here, we develop a SERS-based surface chemotaxonomy that uses bacterial extracellular matrices (ECMs) as proxy biosignatures to hierarchically classify bacteria based on their shared surface biochemical characteristics to eventually identify six distinct bacterial species at >98% classification accuracy. Corroborating with in silico simulations, we establish a three-way inter-relation between the bacteria identity, their ECM surface characteristics, and their SERS spectral fingerprints. The SERS spectra effectively capture multitiered surface biochemical insights including ensemble surface characteristics, e.g., charge and biochemical profiles, and molecular-level information, e.g., types and numbers of functional groups. Our surface chemotaxonomy thus offers an orthogonal taxonomic definition to traditional classification methods and is achieved without gene amplification, biochemical testing, or specific biomarker recognition, which holds great promise for point-of-need applications and microbial research.

Mechanochemical Activation of Silicone for Large-Scale Fabrication of Anti-Biofouling Liquid-like Surfaces

Large-scale preparation of liquid-like coatings with perfect transparency via solventless and room-temperature processes using low-cost and biocompatible materials is of tremendous interest for a broad range of applications. Here, we present a mechanochemical activation strategy for solventless grafting of poly(dimethylsiloxane) (PDMS) onto glass, silicon wafers, and ceramics. Activation is achieved via ball milling PDMS without using any solvents or additives prior to application. Ball milling results in chain scission and generation of free radicals, allowing room-temperature grafting at durations ≤1 h. The deposition of ball-milled PDMS can be facilitated by brushing or drop-casting, enabling large-scale applications. The resulting surfaces facilitate the sliding of droplets at angles <20° for liquids with surface tension ranging from 22 to 73 mN/m. An important application for public health is generating anti-biofouling coatings on sanitary ware. For example, PDMS-grafted surfaces prepared on a regular-size toilet bowl exhibit a 105-fold decrease in the attachment of bacteria from urine. These findings highlight the significant potential of mechanochemical processes for the practical preparation of liquid-like surfaces.

Biogenic Nanoparticles Silver and Copper and Their Composites Derived from Marine Alga Ulva lactuca: Insight into the Characterizations, Antibacterial Activity, and Anti-Biofilm Formation

Bacterial pathogens cause pain and death, add significantly to the expense of healthcare globally, and pose a serious concern in many aspects of daily life. Additionally, they raise significant issues in other industries, including pharmaceuticals, clothing, and food packaging. Due to their unique properties, a great deal of attention has been given to biogenic metal nanoparticles, nanocomposites, and their applications against pathogenic bacteria. This study is focused on biogenic silver and copper nanoparticles and their composites (UL/Ag2 O-NPS, Ul/CuO-NPs, and Ul/Ag/Cu-NCMs) produced by the marine green alga Ulva lactuca. The characterization of biogenic nanoparticles UL/Ag2 O-NPS and Ul/CuO-NPs and their composites Ul/Ag/Cu-NCMs has been accomplished by FT-IR, SEM, TEM, EDS, XRD, and the zeta potential. Minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) experiments were conducted to prove antibacterial activity against both Gram-positive and Gram-negative bacteria and anti-biofilm. The FTIR spectroscopy results indicate the exiting band at 1633 cm-1, which represents N-H stretching in nanocomposites, with a small shift in both copper and silver nanoparticles, which is responsible for the bio-reduction of nanoparticles. The TEM image reveals that the Ul/Ag/Cu-NCMs were hexagonal, and the size distribution ranged from 10 to 35 nm. Meanwhile, Ul/CuO-NPs are rod-shaped, whereas UL/Ag2 O-NPS are spherical. The EDX analysis shows that Cu metal was present in a high weight percentage over Ag in the case of bio-Ag/Cu-NCMs. The X-ray diffraction denotes that Ul/Ag/Cu-NCMs, UL/CuO-NPs, and UL/Ag2 O-NPS were crystalline. The results predicted by the zeta potential demonstrate that Ul/Ag/Cu-NCMs were more stable than Ul/CuO-NPs. The antibacterial activity of UL/Ag2 O-NPS, Ul/Ag/Cu-NCMs, and UL/CuO-NPs was studied against eleven Gram-negative and Gram-positive multidrug-resistant bacterial species. The maximum inhibition zones were obtained with UL/Ag2 O-NPS, followed by Ul/Ag/Cu-NCMs and Ul/CuO-NPs in all the tested bacteria. The maximum anti-biofilm percentage formed by E. coli KY856933 was obtained with UL/Ag2 O-NPS. These findings suggest that the synthesized nanoparticles might be a great alternative for use as an antibacterial agent against different multidrug-resistant bacterial strains.