Polyaniline-based 3D network structure promotes entrapment and detection of drug-resistant bacteria

Click chemistry-enabled gold nanorods for sensitive detection and viability evaluation of copper(II)-reducing bacteria

The rise of antibiotic resistance poses a significant and ongoing challenge to public health, with pathogenic bacteria remaining a persistent threat. Traditional culture methods, while considered the gold standard for bacterial detection and viability assessment, are time-consuming and labor-intensive. To address this limitation, we developed a novel point-of-care (POC) detection method leveraging citrate- and alkyne-modified gold nanorods (AuNRs) synthesized with click chemistry properties. These AuNRs exhibit superior biocompatibility and enhanced quantitative performance compared to conventional surfactant-modified AuNRs. Our method, termed AuNRs-bacteria-initiated click chemistry (AuNRs-BICC), detects CuII-reducing bacteria by quantifying AuNRs bound to a biosensing interface via bacteria-mediated CuII reduction to CuI and subsequent click chemistry with biosensing interface of azide modifications. Using dark-field microscopy (DFM), we demonstrated a strong linear correlation between AuNR counts and the logarithm of bacterial concentration for both Gram-negative Escherichia coli (including KPC-2-expressing antibiotic-resistant strains) and Gram-positive Staphylococcus aureus across a range of 101 to 107 cells, achieving a remarkable detection limit of 101 cells. The AuNRs-BICC biosensor exhibits high selectivity for target bacterial strains and provides rapid detection within 3 h. Furthermore, it can assess bacterial viability in the presence of various antibiotics, including meropenem, ceftriaxone and tetracycline, suggesting its potential for rapid antibiotic susceptibility testing and facilitating timely clinical intervention for infectious diseases.

Thermo-responsive 3D nanostructures for enhanced performance in food-poisoning bacterial analysis

The growing risk of bacterial food poisoning due to global warming has necessitated the development of methods for accurate detection of food-poisoning bacteria. Despite extensive efforts to develop enhanced bacterial-capture methods, challenges associated with the release of the captured bacteria have limited the sensitivity of bacterial detection. In this study, thermo-responsive intelligent 3D nanostructures to improve food-poisoning bacterial analysis performance were fabricated by introducing a thermo-responsive polymer onto an urchin-like 3D nanopillar substrate (URCHANO). A co-polymer of methacryloyl glycinamide and benzyl acrylate (MNAGA-Bn 5%) was introduced as a thermo-responsive co-polymer onto URCHANO using an electron-transfer atom-transfer radical-polymerization method to fabricate Thermo-URCHANO. A temperature-related analysis of the surface properties of Thermo-URCHANO revealed a hydrophobic-to-hydrophilic transition at 37 °C, which facilitated the release of bacteria captured within the nanostructure. In a one-pot analysis to capture and analyze various food-poisoning bacteria in kitchenware (gloves and aprons) and food items (eggs and sausages), mimicking real-life environments, specimens collected using Thermo-URCHANO showed lower Ct values than those collected with uncoated URCHANO, indicating greater bacterial detection. This method could effectively release captured bacteria through temperature changes, improving extraction efficiency during swab collection. While Thermo-URCHANO needs further optimization, it is expected to enhance bacterial analysis performance and sensitivity.

Hypervirulent and carbapenem-resistant Klebsiella pneumoniae: A global public health threat

The evolution of hypervirulent and carbapenem-resistant Klebsiella pneumoniae can be categorized into three main patterns: the evolution of KL1/KL2-hvKp strains into CR-hvKp, the evolution of carbapenem-resistant K. pneumoniae (CRKp) strains into hv-CRKp, and the acquisition of hybrid plasmids carrying carbapenem resistance and virulence genes by classical K. pneumoniae (cKp). These strains are characterized by multi-drug resistance, high virulence, and high infectivity. Currently, there are no effective methods for treating and surveillance this pathogen. In addition, the continuous horizontal transfer and clonal spread of these bacteria under the pressure of hospital antibiotics have led to the emergence of more drug-resistant strains. This review discusses the evolution and distribution characteristics of hypervirulent and carbapenem-resistant K. pneumoniae, the mechanisms of carbapenem resistance and hypervirulence, risk factors for susceptibility, infection syndromes, treatment regimens, real-time surveillance and preventive control measures. It also outlines the resistance mechanisms of antimicrobial drugs used to treat this pathogen, providing insights for developing new drugs, combination therapies, and a "One Health" approach. Narrowing the scope of surveillance but intensifying implementation efforts is a viable solution. Monitoring of strains can be focused primarily on hospitals and urban wastewater treatment plants.

Multiplex Detection of Foodborne Pathogens using 3D Nanostructure Swab and Deep Learning-Based Classification of Raman Spectra

Proactive management of foodborne illness requires routine surveillance of foodborne pathogens, which requires developing simple, rapid, and sensitive detection methods. Here, a strategy is presented that enables the detection of multiple foodborne bacteria using a 3D nanostructure swab and deep learning-based Raman signal classification. The nanostructure swab efficiently captures foodborne pathogens, and the portable Raman instrument directly collects the Raman signals of captured bacteria. a deep learning algorithm has been demonstrated, 1D convolutional neural network with binary labeling, achieves superior performance in classifying individual bacterial species. This methodology has been extended to mixed bacterial populations, maintaining accuracy close to 100%. In addition, the gradient-weighted class activation mapping method is used to provide an investigation of the Raman bands for foodborne pathogens. For practical application, blind tests are conducted on contaminated kitchen utensils and foods. The proposed technique is validated by the successful detection of bacterial species from the contaminated surfaces. The use of a 3D nanostructure swab, portable Raman device, and deep learning-based classification provides a powerful tool for rapid identification (≈5 min) of foodborne bacterial species. The detection strategy shows significant potential for reliable food safety monitoring, making a meaningful contribution to public health and the food industry.

Nanotopology-Enabled On-Site Pathogen Detection for Managing Atopic Dermatitis

Atopic dermatitis (AD), a prevalent skin condition often complicated by microbial infection, poses a significant challenge in identifying the responsible pathogen for its effective management. However, a reliable, safe tool for pinpointing the source of these infections remains elusive. In this study, a novel on-site pathogen detection that combines chemically functionalized nanotopology with genetic analysis is proposed to capture and analyze pathogens closely associated with severe atopic dermatitis. The chemically functionalized nanotopology features a 3D hierarchical nanopillar array (HNA) with a functional polymer coating, tailored to isolate target pathogens from infected skin. This innovative nanotopology demonstrates superior pathogenic capture efficiency, favorable entrapment patterns, and non-cytotoxicity. An HNA-assembled stick is utilized to directly retrieve bacteria from infected skin samples, followed by extraction-free quantitative loop-mediated isothermal amplification (direct qLAMP) for validation. To mimic human skin conditions, porcine skin is employed to successfully capture Staphylococcus aureus, a common bacterium exacerbating AD cases. The on-site detection method exhibits an impressive detection limit of 103 cells mL-1. The HNA-assembled stick represents a promising tool for on-site detection of bacteria associated with atopic dermatitis. This innovative approach enables to deepen the understanding of AD pathogenesis and open avenues for more effective management strategies for chronic skin conditions.

ANCA: artificial nucleic acid circuit with argonaute protein for one-step isothermal detection of antibiotic-resistant bacteria

Endonucleases have recently widely used in molecular diagnostics. Here, we report a strategy to exploit the properties of Argonaute (Ago) proteins for molecular diagnostics by introducing an artificial nucleic acid circuit with Ago protein (ANCA) method. The ANCA is designed to perform a continuous autocatalytic reaction through cross-catalytic cleavage of the Ago protein, enabling one-step, amplification-free, and isothermal DNA detection. Using the ANCA method, carbapenemase-producing Klebsiella pneumoniae (CPKP) are successfully detected without DNA extraction and amplification steps. In addition, we demonstrate the detection of carbapenem-resistant bacteria in human urine and blood samples using the method. We also demonstrate the direct identification of CPKP swabbed from surfaces using the ANCA method in conjunction with a three-dimensional nanopillar structure. Finally, the ANCA method is applied to detect CPKP in rectal swab specimens from infected patients, achieving sensitivity and specificity of 100% and 100%, respectively. The developed method can contribute to simple, rapid and accurate diagnosis of CPKP, which can help prevent nosocomial infections.