A review of methods for the detection of pathogenic microorganisms

Revolutionizing Escherichia coli detection in real samples with digital SERS aptamer sensor technology

Aptamer sensors based on surface-enhanced Raman scattering (SERS) technology have demonstrated great potential in the ultrasensitive and rapid detection of Escherichia coli (E. coli). Herein, this paper presents a digital SERS aptamer sensor. This sensor integrates ordered nanoscale array synthesis technology and digital analysis technology, enabling highly sensitive and rapid bacterial quantification. The ordered monolayer gold nanosphere arrays (Au NS) can form uniform and dense "hot spots" on the silicon wafer due to their uniform spherical structures and narrow gaps. Moreover, digital SERS is adopted to further optimize the signal uniformity so as to achieve precise quantification. The sensor modules are combined together through base pairing. The aptamers labeled with Raman tags are detached from the complementary DNA due to the competition of the target substance, thus realizing the detection of E. coli. The digital SERS aptamer sensor has been verified to possess excellent selectivity and reproducibility. It has a wide dynamic linear detection range from 1.0 * 101 to 1.0 * 109 CFU/ml and a detection limit of 0.657 CFU/ml, maintaining excellent specificity even in the presence of mixed bacterial interference. The spiked recoveries in actual samples range from 98.80 % to 99.81 %. Leveraging different aptamers and digital analysis, the sensor holds promise for food safety and environmental monitoring applications.

A "signal-on" photoelectrochemical biosensor utilizing in-situ synthesis of SnS2/MgIn2S4 heterostructures and enzyme-assisted target cycling amplification for the sensitive detection of the mecA gene

To address the urgent need for rapid detection of drug resistance biomarkers for methicillin-resistant Staphylococcus aureus (MRSA), particularly the mecA gene that encods the low-affinity penicillin-binding protein PBP2a, this study developed a dual-amplification photoelectrochemical biosensing platform utilizing in-situ synthesized SnS2/MgIn2S4 heterojunction photoanodes. This system synergistically combines the co-sensitization effect of AgInS2 nanoparticles with Exonuclease III-driven target circular amplification: a type II heterojunction structure was constructed through the low-temperature in-situ growth of MgIn2S4 on SnS2 nanosheets, significantly enhanced the separation efficiency of photogenerated carriers, while the band-matched AgInS2 nanoparticles (NPs) improved the directional charge transfer, thereby amplifying the photocurrent response. After recognizing the target mecA gene, the Exo III-triggered hairpin probe initiated a circular cleavage that triggered cascade amplification, resulting in the generation of a substantial amount of output DNA S1 strands. These strands were subsequently assembled into S1/S2-AgInS2 double-strand bridges on the electrode surface, achieving "signal-on" detection through the formation of co-sensitized nanostructures. The biosensor exhibits a wide dynamic range from 100 aM to 1 nM (R2 = 0.999), an exceptionally low detection limit of 12.9 aM (S/N = 3), and a quantitative recovery rate ranging from 98.30 % to 104.70 % for the mecA gene in environmental water matrices, establishing a reliable analytical tool for monitoring antibiotic resistance genes in aquatic ecosystems.

Bactericidal effect of plasma-activated water generated by a novel super-potable plasma device as a novel antibacterial method: an in vitro study

BACKGROUND

A continuous risk from bacterial infection poses a major environmental and public health challenge. As an emerging strategy for inhibiting bacterial infections, plasma-activated water (PAW) has proved highly effective, environment-friendly, and non-drug resistant to many bacteria. However, the plasma device is too big and requires specialists to operate, which inevitably limits its real-life application. It has been confirmed that the bactericidal effect of traditional PAW would be significantly reduced in the organic-rich environments. To better resolve the limitations, a novel super-portable plasma device was applied, and the low concentration of H2O2 was added to enhance the bactericidal effect of PAW in the organ-rich environments.

METHODS

A super-portable plasma device, weighing only 1 kg, was applied to generate PAW. Staphylococcus aureus (S. aureus), Escherichia coli (E. coli) and Porphyromonas gingivalis (P. gingivalis) were selected as the test bacteria. Colony-forming units (CFU) counting and bactericidal rate were employed to evaluate the bactericidal effect of PAW. Optical emission spectroscopy (OES) was used to identify the major reactive species in PAW. Different concentration of H2O2 was added to PAW (H2O2/PAW), and the pH, oxidation-reduction potential (ORP), conductivity, H2O2 and NO content of HP/PAW were measured to explore the bactericidal mechanisms. CCK-8 assay was used to assess the cytotoxicity of 0.1% H2O2/PAW on human periodontal ligament fibroblasts (hPDLFs).

RESULTS

The 20 mL of PAW generated by the novel super-portable plasma device achieved the bactericidal rate of 98.94% against S. aureus (t = 10 min) and 99.66% (t = 20 min). When the PAW was prepared with a volume of 20 mL, activation times of 6 min and 8 min, and the treatment time was 30 min, the bactericidal rates all exceeded 98% against P. gingivalis. The addition of 0.1% H2O2 significantly enhanced the antibacterial effect of PAW in the presence of Bovine Serum Albumin (BSA) (p<0.05). The addition of 0.1% H2O2 lowered the pH, increased the ORP and conductivity, and raised the content of H2O2 and NO in the solution. Cytotoxicity evaluation indicated that 0.1% H2O2/PAW had moderate cytotoxicity only after 20 min of preparation and 48 h of treatment, while other preparation and treatment times showed mild or no cytotoxicity.

CONCLUSIONS

The PAW generated by the novel super-portable plasma device exhibited an excellent bactericidal effect. The addition of low concentrations of H2O2 to PAW, specifically 0.1% H2O2/PAW, maintained an effective antibacterial effect in complex environments in the presence of BSA with non-cytotoxicity.

Biofilm-mediated infections; novel therapeutic approaches and harnessing artificial intelligence for early detection and treatment of biofilm-associated infections

A biofilm is a group of bacteria that have self-produced a matrix and are grouped together in a dense population. By resisting the host's immune system's phagocytosis process and attacking with anti-microbial chemicals such as reactive oxygen and nitrogen species, a biofilm enables pathogenic bacteria to evade elimination. One of the major problems in managing chronic injuries is treating wounds colonized by biofilms. These days, a major issue is the biofilms, which exacerbate infection pathogenesis and severity. Numerous investigators have already discovered cutting-edge methods for biofilm manipulation. Using phytochemicals is a practical tactic to control and prevent the production of biofilms. Numerous studies conducted in the last few years have demonstrated the antibacterial and antibiofilm qualities of nanoparticles (NPs) against bacteria, fungi, and protozoa. Because hydrogel has antibiofilm properties, it has been employed extensively in wound care recently. It may be removed with ease and without causing trauma. Today, artificial intelligence (AI) is being used to improve these tactics by providing customized treatment alternatives and predictive analytics. Artificial intelligence (AI) algorithms have the capability to examine extensive datasets and detect trends in biofilm formation and resistance mechanisms. This can aid in the creation of more potent antimicrobial drugs. AI models analyze complex datasets, predict biofilm formation, and guide the design of personalized treatment strategies by identifying resistance mechanisms and therapeutic targets with exceptional precision. This review provides an integrative perspective on biofilm formation mechanisms and their role in infections, highlighting the innovative applications of AI in this domain. By integrating data from diverse biological systems, AI accelerates drug discovery, optimizes treatment regimens, and enables real-time monitoring of biofilm dynamics. From predictive analytics to personalized care, we explore how AI enhances biofilm diagnostics and introduces precision medicine in biofilm-associated infections. This approach not only addresses the limitations of traditional methods but also paves the way for revolutionary advancements in infection control, antimicrobial resistance management, and improved patient outcomes.

Polynuclear transition metal complexes: emerging agents for bacterial imaging and antimicrobial therapy

Polynuclear transition metal complexes (PTMCs) represent a promising class of compounds with significant potential for advancing microbial diagnostics and treatment due to their multifunctional properties. This perspective highlights recent progress in the design of PTMCs for detecting and combating microbial infections. Complexes with multiple metal centers, such as silver(I), rhenium(I), iron(II), cobalt(II), nickel(II), copper(II), zinc(II), cadmium(II), ruthenium(II), iridium(III), gold(I), and gold(III), exhibit a wide range of structural motifs and are effective against a broad spectrum of multidrug-resistant bacterial infections. PTMCs show their antimicrobial effects through several mechanisms that include the generation of reactive oxygen species, which cause oxidative stress and damage bacterial cells, disrupt bacterial membranes, bind selectively to bacterial biomolecules, and interfere with critical cellular functions. Additionally, luminescent PTMCs are ideal for real-time imaging and tracking of bacterial cells during infection. In this perspective, we discuss their various applications, safety concerns, and emerging trends in the clinical use of PTMCs due to their enormous possibilities for future medical applications.

Innovations in aptamer-based biosensors for detection of pathogenic bacteria: Recent advances and perspective

The rapid and accurate detection of pathogenic bacteria is a pressing concern in the fields of public health, food safety, and environmental monitoring. However, traditional methods often prove to be slow and difficult to quantify accurately. Thus, there is a pressing need to develop advanced methods that enable rapid detection which is sensitive and inexpensive. Aptamers, which are short nucleic acid sequences derived through a process called systematic evolution of ligands by exponential enrichment (SELEX), offer a promising alternative due to their unique binding characteristics. These properties confer several advantages over traditional antibodies, making aptamers effective and versatile bioreceptors for pathogen detection. Recent advancements have led to the development of various aptamer-based biosensors utilizing diverse signaling strategies, including optical, electrochemical, mass-based, paper-based and microchip capillary electrophoresis (MCE) methods. The integration of nanomaterials with aptamer technology has further enhanced biosensor performance by improving sensitivity and enabling real-time monitoring of bacterial contamination. In this review, the focus is on current developments in aptamer-based biosensors and their potential applications in clinical diagnostics, food safety and environmental monitoring. As research progresses, the customization of aptamer sequences for specific targets is expected to yield tailored diagnostic solutions, ultimately improving patient outcomes and public health responses. The continued exploration of aptamer technology marks a significant advancement in methodologies for detecting pathogenic bacteria, highlighting not only the promise of aptamers as effective detection tools but also the critical need for multidisciplinary collaboration, integrating molecular biology, materials science, and microfluidics, to overcome challenges in this field.

Optimisation and clinical validation of a metagenomic third-generation sequencing approach for aetiological diagnosis in bronchoalveolar lavage fluid of patients with pneumonia

BACKGROUND

Metagenomic Third Generation Sequencing (mTGS), based on nanopore technology, has emerged as a promising tool for the rapid diagnosis of pneumonia pathogens. However, this technology currently lacks standardised technical protocols, quality control measures, and comprehensive performance evaluations for the simultaneous detection of bacteria, fungi, and viruses in clinical settings.

METHODS

We optimised the mTGS workflow by refining key parameters (cell wall lysis, fragment size selection, host DNA depletion, and sequencing depth) using reference samples and bronchoalveolar lavage fluid (BALF) from eight patients with pneumonia. These optimisations formed the basis for a standardised mTGS protocol. To assess the clinical diagnostic value of the optimised mTGS, a multicentre prospective cohort study involving 313 pneumonia-suspected patients was conducted. Each BALF sample was tested using conventional microbiological testing (CMTs), metagenomic next-generation sequencing (mNGS), pre-optimised mTGS, and optimised mTGS.

FINDINGS

The optimised mTGS protocol, based on the refined cell wall lysis, fragment size selection, no host DNA depletion, and 800 MB sequencing depth, achieved a tenfold increase in sensitivity compared with pre-optimised mTGS for detecting the species of Bacillus subtilis, Mycobacterium tuberculosis, Mycobacterium avium, Cryptococcus neoformans, and Human papillomavirus in reference samples. In the prospective cohort, 274 patients with a confirmed diagnosis of pneumonia were identified, yielding 376 distinct microbes. The mTGS identified more microbes than CMTs (314 vs. 115), with a 45.30% increase in sensitivity (84.70% vs. 39.40%, P < 0.01, Chi-square test/Fisher's exact test). Compared with pre-optimised mTGS, the sensitivity of optimised mTGS increased by 32.51% (84.70% vs. 52.19%, P < 0.01, Chi-square test/Fisher's exact test). mTGS showed comparable performance to mNGS (84.70% vs. 79.90%, P = 0.14,Chi-square test/Fisher's exact test), both significantly outperforming CMTs. mNGS was more sensitive for detecting Non-tuberculous mycobacteria, Pneumocystis jirovecii, and Aspergillus spp., while mTGS demonstrated higher sensitivity for M. tuberculosis, Chlamydia psittaci, and Streptococcus pneumoniae. The overall diagnostic agreement between mTGS and clinical diagnosis was 81.80%.

INTERPRETATION

We optimised and validated a standardised mTGS protocol that significantly improved the ability to detect pathogens in the BALF of patients with pneumonia. Optimised mTGS demonstrated comparable performance to mNGS, making it a promising tool for the aetiological diagnosis of pneumonia.

FUNDING

The Research and Development Programme of Zhejiang Province (2023C03068, 2024C03187), the National Natural Science Foundation of China (82272338), the Key R&D Plan of the Ministry of Science and Technology (China) of China (2022YFC2504502).

Protocol for sampling multidrug-resistant bacteria using artificial plastic substrates in aquatic ecosystems

Artificial plastic substrates (APSs) consist of four plastic polymers that work as traps for pathogenic bacteria and facilitate their sampling and detection. Here, we present a protocol for sampling antibiotic-resistant pathogenic bacteria in aquatic ecosystems using APSs. We describe steps for selecting plastic polymers, followed by their assembly, submergence in aquatic environments, and collection for analysis. This protocol can also help explore plastic's role as a new route for disease exposure for humans and animals. For complete details on the use and execution of this protocol, please refer to Ferheen et al.1.

Nanoparticle-assisted PCR: fundamentals, mechanisms, and forensic implications

Polymerase Chain Reaction (PCR) has transformed forensic DNA analysis but is still limited when dealing with compromised trace or inhibitor-containing samples. Nanotechnology has been integrated into nanoPCR (nanoparticle-assisted PCR) to overcome these obstacles. Nanomaterials improve PCR sensitivity, selectivity, and efficiency. Examples of these materials are semiconductor quantum dots and metal nanoparticles. They enhance DNA binding to primers, stabilize enzymes, and function as effective heat conductors, making accurate amplification possible even with tainted samples. The developments in nanoPCR have potential uses in forensics, as they allow for the more sensitive analysis of smaller, polluted, or deteriorated samples. Nevertheless, there are methodological and ethical issues. To provide credible and legitimate forensic evidence, rigorous validation and standardization of NanoPCR techniques are vital. The article addresses the relevant ethical and methodological aspects in forensic casework while examining the integration of nanotechnology into PCR.

Immuno-PCR in the Analysis of Food Contaminants

Food safety is a significant issue of global concern. Consumer safety and government regulations drive the need for the accurate analysis of food contaminants, residues and other chemical constituents of concern. Traditional methods for the detection of food contaminants often present challenges, including lengthy processing times and food matrix interference; they often require expensive equipment, skilled personnel or have limitations in sensitivity or specificity. Developing novel analytical methods that are sensitive, specific, accurate and rapid is therefore crucial for ensuring food safety and the protection of consumers. The immuno-polymerase chain reaction (IPCR) method offers a promising solution in the analysis of food contaminants by combining the specificity of conventional immunological methods with the exponential sensitivity of PCR amplification. This review evaluates the current state of IPCR methods, describes a variety of existing IPCR formats and explores their application in the analysis of food contaminants, including pathogenic bacteria and their toxins, viruses, mycotoxins, allergens, polycyclic aromatic hydrocarbons, polychlorinated biphenyls, phthalic acid esters, pesticides, antibiotics and other food contaminants. Depending on the type of analyte, either sandwich or competitive format IPCR methods are predominantly used. This review also examines limitations of current IPCR methods and explores potential advancements for future implementation in the field of food safety.

FEN1-Aided RPA (FARPA) Coupled with Autosampling Microfluidic Chip Enables Highly Multiplexed On-Site Pathogen Screening

A simple, rapid, low-cost, and multiplex detection platform is crucial for the diagnosis of infectious diseases, especially for on-site pathogen screening. However, current methods are difficult to satisfy the requirements for minimal instrument and multiplexed point-of-care testing (POCT). Herein, we propose a versatile and easy-to-use platform (FARPA-chip) by combining multiplex FARPA with an autosampling microfluidic chip. A pair of universal recombinase polymerase amplification (RPA) primers introduced during double-stranded cDNA (ds-cDNA) preparation are employed to amplify multiple targets, followed by amplicon-decoding with the chip, indicating no bias in amplifying different targets due to the universal RPA primers. FARPA-chip exhibits that as low as 10 copies of each target RNA in the starting sample can be sensitively detected by 12-plex detection of vector-borne viruses within 45 min and no cross-talk is observed between different targets. The feasibility of this platform is confirmed by designing a 9-plex FARPA-chip to detect 6 kinds of clinically common respiratory viruses from 16 clinical samples of nasopharyngeal swabs, and the results are completely consistent with RT-qPCR. Furthermore, by integrating quick extraction reagent, the turnaround time can be significantly decreased to <50 min, highlighting that our FARPA-chip enables a cost-effective on-site pathogen screening with a relatively high level of multiplexing. Depending on the number of chambers in the chip, the current design is theoretically capable of detecting up to 24 different pathogens, which should fulfill most clinical purposes. We believe that the proposed platform could provide an effective way for a series of healthcare-related applications in resource-limited settings.

An ultrasensitive fluorescence nano-biosensor based on RBP 41-quantum dot microspheres for rapid detection of Salmonella in the food matrices

Swift screening of Salmonella-contaminated food is crucial for timely prevention and control of foodborne illness outbreaks. A novel phage receptor binding protein (RBP 41) was previously identified and characterized from phage T102. This study functionalized RBP 41 onto magnetic beads (MBs) and quantum dot microspheres (QDMs) to form magnetic separation and fluorescent probes, respectively. The bacteria were captured by RBP 41-MBs and labelled with RBP 41-QDMs to form MBs-RBP 41-bacteria-RBP 41-QDMs complexes, then the fluorescence intensity of complexes was detected for determination of Salmonella. This proposed biosensor was demonstrated to detect Salmonella as low as 0.1245 Log10 CFU/mL (∼2 CFU/mL) within ∼1.5 h. The recovery yield of Salmonella in the spiked food samples ranged from 87 % to 119 %, indicating that it could detect Salmonella in real samples. This novel magnetic fluorescence nano-biosensor has potential to detect the bacteria in the different sample to reduce the detection time and increase sensitivity.

Fluorescent aptasensor for detection of Salmonella typhimurium using boric acid-functionalized terbium metal-organic framework and magnetic nanoparticles

A fluorescent detection platform was designed using boric acid-functionalized terbium metal-organic framework (BA-Tb-MOF) and carboxyl-modified magnetic nanoparticles (MNPs) to identify Salmonella typhimurium (S. typhimurium) bacteria. Firstly, carboxyl-modified Fe3O4MNPs were coated with specific aptamer (Apt-MNPs) as the capture probe for S. typhimurium. Then, the Apt-MNPs were added to the bacterial suspension to facilitate the targeted binding. Subsequently, the fluorescent probe (BA-Tb-MOF) was introduced into this solution. The BA-Tb-MOF was strongly attached to the bacterial surface through interactions between BA and glycolipids on the bacterial cell walls, forming a stable complex. As the bacterial concentration increased, the fluorescence intensity of the solution progressively decreased due to the binding and removal of bacteria-Apt-MNPs/BA-Tb-MOF complexes through magnetic separation. Under optimum conditions, the concentration of S. typhimurium and the fluorescence intensity showed an inverse linear relationship within the range of 101-109 CFU/mL, and the detection limit was 4 CFU/mL. The developed sensor showed high specificity against several other pathogenic bacteria such as E. coli, S. aureus, and P. aeruginosa. The developed fluorescence platform also successfully detected the S. typhimurium in drinking water and egg samples with satisfactory recoveries (83-98%). This strategy can be investigated further for the detection of S. typhimurium and other pathogens in food and clinical samples.

Nutritional, functional, and microbial qualities of legume-based flour blends processed by SMEs in Zambia and Malawi compared to standard Corn-Soy Blend Plus (CSB +): a cross-sectional study

BACKGROUND

Legumes enhance food security in developing countries, necessitating an understanding of their properties. This study examined the nutritional, functional, and microbial qualities of legume-based flour blends from Small and Medium Enterprises (SMEs) in Malawi and Zambia. SMEs were chosen for their key role in local food production, distribution, and complementary food supply.

METHOD

A total of 36 legume-based flour blend samples were collected using snowball sampling, consisting of 21 samples (7 sets of 3 similar samples) from SMEs in Zambia and 15 samples (5 sets of 3 similar samples) from SMEs in Malawi. Samples were analyzed for proximate composition, energy, iron, and zinc content. The nutritional contributions to the Recommended Dietary Allowances (RDA) for children aged 1-3 years were assessed. Additionally, functional properties such as water-holding and oil-holding capacities were measured. Microbial analysis was performed, and the data were statistically analyzed to determine significance (p ≤ 0.05).

RESULTS

Our findings revealed substantial variability in the nutritional content of these flour blends. Protein content ranged from 9.4% to 41.5%, carbohydrates from 8.1% to 71.3%, crude fat from 2.3% to 26.8%, and crude fiber from 6.2% to 35.2%. Iron and zinc levels also varied significantly, from 2.9 to 21.9 mg/100 g and 2.2 to 5.2 mg/100 g, respectively. These inconsistencies highlight a lack of standardization in nutrient content for blends intended for infant feeding. When prepared as 96 g porridge servings for children aged 1-3 years, the blends provided notable contributions to the Recommended Dietary Allowance (RDA). However, their nutrient levels were generally lower compared to the standard Corn-Soy Blend Plus (CSB +). The flour blends also showed variations in physico-functional properties, and some had microbial loads exceeding 250 cfu/g, reflecting inadequate hygiene practices during processing.

CONCLUSION

To enhance their products, SMEs should ensure that their flour blends meet both nutritional and safety standards while striving to match or surpass the nutrient content of CSB + to remain competitive in the market.

Spectroscopic analysis of bacterial photoreactivation

With the rise of bacterial infections and antibiotic resistance, spectroscopic devices originally developed for bacterial detection have shown promise to rapidly identify bacterial strains and determine the ratio of live to dead bacteria. However, the detection of the photoreactivated pathogens remains a critical concern. This study utilizes fluorescence and Raman spectroscopy to analyze bacterial responses to UV irradiation and subsequent photoreactivation. Our experimental results reveal limitations in fluorescence spectroscopy for detecting photoreactivated bacteria, as the intense fluorescence of tryptophan and tyrosine amino acids masks the fluorescence emitted by thymine molecules. Conversely, Raman spectroscopy proves more effective, showing a detectable decrease in band intensities of E. coli bacteria at 1248 and 1665 cm-1 after exposure to UVC radiation. Subsequent UVA irradiation results in the partial restoration of these band intensities, indicating DNA repair and bacterial photoreactivation. This enhanced understanding aims to improve the accuracy and effectiveness of these spectroscopic tools in clinical and environmental settings.

Nucleic Acid Aptamer-Based Sensors for Bacteria Detection: A Review

Bacteria have a significant impact on human production and life, endangering human life and health, so rapid detection of infectious agents is essential to improve human health. Aptamers, which are pieces of oligonucleotides (DNA or RNA) have been applied to biosensors for bacteria detection due to their high affinity, selectivity, robust chemical stability, and their compatibility with various signal amplification and signal transduction mechanisms. In this review, we summarize the different bacterial aptamers selected in recent years using SELEX technology and discuss the differences in optical and electrochemical bacterial aptamer sensors. In addition the technological developments and innovations in bacterial aptamer sensor technology are introduced. Combining new materials and methods, the efficiency and stability of the sensors have also been improved. This review summarizes the progress of current bacterial aptamer sensors based on their practical application status and provides an outlook on their future development.

Detection of antimicrobial resistance via state-of-the-art technologies versus conventional methods

Antimicrobial resistance (AMR) is recognized as one of the foremost global health challenges, complicating the treatment of infectious diseases and contributing to increased morbidity and mortality rates. Traditionally, microbiological culture and susceptibility testing methods, such as disk diffusion and minimum inhibitory concentration (MIC) assays, have been employed to identify AMR bacteria. However, these conventional techniques are often labor intensive and time consuming and lack the requisite sensitivity for the early detection of resistance. Recent advancements in molecular and genomic technologies-such as next-generation sequencing (NGS), matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS), lateral flow immunoassays (LFIAs), PCR-based diagnostic methods, and CRISPR-based diagnostics-have revolutionized the diagnosis of AMR. These innovative approaches provide increased sensitivity, reduced turnaround times, and the ability to identify genetic resistance mechanisms. This review seeks to examine the advantages and disadvantages of both emerging technologies and traditional methods for detecting AMR, emphasizing the potential benefits and limitations inherent to each. By understanding the strengths and limitations of these technologies, stakeholders, including researchers, healthcare professionals, regulatory agencies, health authorities, financial managers, and patients, can make informed decisions aimed at preventing the emergence and dissemination of antibiotic-resistant strains, thereby ultimately increasing patient safety.

Sensing Microorganisms Using Rapid Detection Methods: Supramolecular Approaches

Supramolecular chemistry relies on the dynamic association/dissociation of molecules through non-covalent interactions. These interactions of a self-assembled system can be strategically exploited for sensing several microorganisms. Moreover, supramolecular systems can also be combined with other functional components like nanoparticles, self-assembled monolayers, and microarray systems to produce multicomponent sensors with higher sensitivity and lower detection time. In this review, we will discuss how cutting-edge supramolecular chemistry has enabled scientists to develop microbial biosensors with high reliability and rapid detection time. Moreover, they produce high-throughput operations, real-time monitoring, extensive operation platforms, and cost-effective production. This review can serve as a conceptual background for understanding state-of-the-art rapid detection methods of microbial biosensing.

Spectroscopic characterization of bacterial colonies through UV hyperspectral imaging techniques

Introduction

Plate culturing and visual inspection are the gold standard methods for bacterial identification. Despite the growing attention on molecular biology techniques, colony identification using agar plates remains manual, interpretative, and heavily reliant on human experience, making it prone to errors. Advanced imaging techniques, like hyperspectral imaging, offer potential alternatives. However, the use of hyperspectral imaging in the VIS-NIR region has been hindered by sensitivity to various components and culture medium changes, leading to inaccurate results. The application of hyperspectral imaging in the ultraviolet (UV) region has not been explored, despite the presence of specific absorption and emission peaks in bacterial components.

Methods

To address this gap, we developed a predictive model for bacterial colony detection and identification using UV hyperspectral imaging. The model utilizes hyperspectral images acquired in the UV wavelength range of 225-400 nm, processed with principal component analysis (PCA) and discriminant analysis (DA). The measurement setup includes a hyperspectral imager, a PC for automated data analysis, and a conveyor belt system to transport agar plates for automated analysis. Four bacterial species (Escherichia coli, Staphylococcus, Pseudomonas, and Shewanella) were cultured on two different media, Luria Bertani and Tryptic Soy, to train and validate the model.

Results

The PCA-DA-based model demonstrated high accuracy (90%) in differentiating bacterial species based on the first three principal components, highlighting the potential of UV hyperspectral imaging for bacterial identification.

Discussion

This study shows that UV hyperspectral imaging, coupled with advanced data analysis techniques, offers a robust and automated alternative to traditional methods for bacterial identification. The model's high accuracy emphasizes the untapped potential of UV hyperspectral imaging in microbiological analysis, reducing human error and improving reliability in bacterial species differentiation.

Eradication of Antibiotic-Resistant Gram-Positive Bacteria and Biofilms by Rationally Designed AIE-Active Iridium(III) Complexes Derived from Cyclometalating 2-Phenylquinoline and Ancillary Bipyridyl Ligands

Antibiotic resistance caused by Gram-positive bacteria is a growing global human health threat. Selective discrimination and eradication of Gram-positive bacteria and their biofilms is challenging. Therapeutic strategies with multiple modes of action are urgently needed to address the increase in Gram-positive bacteria-resistant nosocomial infections. In this work, we have presented rationally designed aggregation-induced emission (AIE)-active cationic cyclometalated iridium(III) complexes derived from 2-phenylquinoline and 2,2'-bipyridine ligands for Gram-positive antibacterial studies. The AIE properties of these complexes were exploited for selective discrimination between Gram-positive and Gram-negative bacteria. These complexes displayed good antimicrobial activity against critical Gram-positive ESKAPE pathogens with minimum inhibitory concentrations in the low micromolar range but were inactive against Gram-negative pathogens. Importantly, the complexes can inhibit biofilm formation and eradicate bacteria from mature biofilms, which are major causes of persistent infections and antibiotic resistance and are more difficult to eliminate. In addition, these complexes showed low hemolytic activity against mammalian cells and a high therapeutic index, indicating good selectivity. Interestingly, the complexes kill bacteria through a variety of modes of mechanism, including ROS generation, cell membrane disruption, and depolarization and the loss of bacterial membrane integrity. These findings offer opportunities for designing metal AIEgens to treat Gram-positive bacterial infections effectively.

Tuning Ion Current Rectifying Nanopipettes for Sensitive Detection of Methicillin-Resistant Staphylococcus aureus

Infectious diseases pose a growing challenge in healthcare, with the increasing rate of antimicrobial resistance limiting therapeutic options available for treatment. Rapid detection of infections at the earliest opportunity can significantly improve patient outcomes. In this report, ion current rectifying quartz nanopipettes with ca. 109 nm orifices were utilized for the label-free detection of DNA indicative of methicillin-resistant Staphylococcus aureus (MRSA). By immobilizing probe DNA complementary to the mecA gene on the internal walls of the nanopipette, the detection was achieved by monitoring changes in ion current rectification (ICR) following probe-target hybridization. We demonstrate enhanced sensitivity by controlling the surface probe density, resulting in a tunable, sensitive sensor technology with a detection limit as low as 0.35 pM. Finite element simulations are used to support our experimental findings, revealing that to maximize target-induced changes to ICR, the probe surface density must be minimized. This sensitive and label-free methodology was integrated with the polymerase chain amplification reaction to achieve selective identification of this pathogen from laboratory-grown cultures, highlighting that ion current rectifying nanopipette sensors offer the potential to be a cost-effective and rapid tools for infectious disease detection.

A Contracted Channel Droplet Reinjection Chip-Based Simple Integrated ddpcr System for SARS-CoV-2 and H1N1 Detection

Droplet microfluidics is a powerful method for digital droplet polymerase chain reaction (ddPCR) applications. However, precise droplet control, bulky peripherals, and multistep operation usually required in droplet detection process hinder the broad application of ddPCR. Here, a contracted channel droplet reinjection chip is presented, where droplets can be self-separated and detected one by one at intervals. Based on that, a Simple Integrated ddPCR (SI-ddPCR) system is established, including surface-wetting-based droplet generation, tube heating, and droplet signal detection. To assess the system's performance, we quantified SARS-CoV-2 and H1N1 simultaneously using duplex-ddPCR. The results exhibited a good linearity (R2 = 0.999) at concentrations ranging from 101 to 104 copies/μL. By employing the SI-ddPCR system, we detected SARS-CoV-2 and H1N1 in clinical samples isolated from 20 swab specimens with an accuracy of 97.5%. Thus, the developed SI-ddPCR system offers simple droplet detection, eliminates complicated peripherals and multistep operations, and promises to be a portable, low-cost, and easy-to-deploy toolbox for high-accuracy ddPCR.

Aptamer and DNAzyme Based Colorimetric Biosensors for Pathogen Detection

The detection of pathogens is critical for preventing and controlling health hazards across clinical, environmental, and food safety sectors. Functional nucleic acids (FNAs), such as aptamers and DNAzymes, have emerged as versatile molecular tools for pathogen detection due to their high specificity and affinity. This review focuses on the in vitro selection of FNAs for pathogens, with emphasis on the selection of aptamers for specific biomarkers and intact pathogens, including bacteria and viruses. Additionally, the selection of DNAzymes for bacterial detection is discussed. The integration of these FNAs into colorimetric biosensors has enabled the development of simple, cost-effective diagnostic platforms. Both non-catalytic and catalytic colorimetric biosensors are explored, including those based on gold nanoparticles, polydiacetylenes, protein enzymes, G-quadruplexes, and nanozymes. These biosensors offer visible detection through color changes, making them ideal for point-of-care diagnostics. The review concludes by highlighting current challenges and future perspectives for advancing FNA-based colorimetric biosensing technologies for pathogen detection.

Utilizing Targeted Next-Generation Sequencing for Rapid, Accurate, and Cost-Effective Pathogen Detection in Lower Respiratory Tract Infections

Objective

To evaluate the diagnostic performance and clinical impact of targeted next-generation sequencing (tNGS) in patients with suspected lower respiratory tract infections.

Methods

Following propensity score matching, we compared the diagnostic performances of tNGS and metagenomic next-generation sequencing (mNGS). Furthermore, the diagnostic performance of tNGS was compared with that of culture, and its clinical impact was assessed.

Results

After propensity score matching, the coincidence rate of tNGS was comparable to that of mNGS (82.9% vs 73.9%, P=0.079). The detection rates for bacterial, viral, fungal, and mixed infections were not significantly different (P>0.05). Bacterial-viral co-infection (16.7%) was the most common mixed infection detected by tNGS. tNGS showed a higher detection rate than culture (75.2% vs 19.0%, P<0.01). The positive detection rate by tNGS was not significantly different between immunocompromised and immunocompetent patients (88.6% vs 80.5%, P=0.202), but was significantly higher than that by culture (P<0.001). Moreover, 65 patients (44.5%) had their medications modified based on the tNGS results, and the majority exhibited notable improvement regardless of treatment adjustment.

Conclusion

tNGS performs comparably to mNGS and surpasses culture in detecting lower respiratory tract infections. Nevertheless, tNGS is faster and more cost-effective than mNGS, making it highly significant for guiding rational treatment.

Open-set deep learning-enabled single-cell Raman spectroscopy for rapid identification of airborne pathogens in real-world environments

Pathogenic bioaerosols are critical for outbreaks of airborne disease; however, rapidly and accurately identifying pathogens directly from complex air environments remains highly challenging. We present an advanced method that combines open-set deep learning (OSDL) with single-cell Raman spectroscopy to identify pathogens in real-world air containing diverse unknown indigenous bacteria that cannot be fully included in training sets. To test and further enhance identification, we constructed the Raman datasets of aerosolized bacteria. Through optimizing OSDL algorithms and training strategies, Raman-OSDL achieves 93% accuracy for five target airborne pathogens, 84% accuracy for untrained air bacteria, and 36% reduction in false positive rates compared to conventional close-set algorithms. It offers a high detection sensitivity down to 1:1000. When applied to real air containing >4600 bacterial species, our method accurately identifies single or multiple pathogens simultaneously within an hour. This single-cell tool advances rapidly surveilling pathogens in complex environments to prevent infection transmission.

Development of a His-Tag-mediated pull-down and quantification assay for G-quadruplex containing DNA sequences

In this study, we developed a simple pull-down assay using peptide nucleic acids (PNAs) equipped with a His-Tag and a G-quadruplex (G4) ligand for the selective recognition and quantification of G4-forming DNA sequences. Efficient and specific target recovery was achieved using optimized buffer conditions and magnetic Ni-NTA beads, while quantification was realized by employing the enzyme-like properties of the G4/hemin complex. The assay was validated through HPLC analysis and adapted for a 96-well plate format. The results show that higher recovery can be achieved using His-Tag with Ni-NTA magnetic beads as compared to the more common biotin-streptavidin purification. The inclusion of the G4-ligand as an additional selectivity handle was shown to be beneficial for both recovery and selectivity.

Universal, untargeted detection of bacteria in tissues using metabolomics workflows

Fast and reliable identification of bacteria directly in clinical samples is a critical factor in clinical microbiological diagnostics. Current approaches require time-consuming bacterial isolation and enrichment procedures, delaying stratified treatment. Here, we describe a biomarker-based strategy that utilises bacterial small molecular metabolites and lipids for direct detection of bacteria in complex samples using mass spectrometry (MS). A spectral metabolic library of 233 bacterial species is mined for markers showing specificity at different phylogenetic levels. Using a univariate statistical analysis method, we determine 359 so-called taxon-specific markers (TSMs). We apply these TSMs to the in situ detection of bacteria using healthy and cancerous gastrointestinal tissues as well as faecal samples. To demonstrate the MS method-agnostic nature, samples are analysed using spatial metabolomics and traditional bulk-based metabolomics approaches. In this work, TSMs are found in >90% of samples, suggesting the general applicability of this workflow to detect bacterial presence with standard MS-based analytical methods.

Prevalence of Bacterial Pathogens Isolated from Canines with Pyoderma and Otitis Externa in Korea: A Systematic Review and Meta-Analysis

Bacterial skin infections, particularly pyoderma and otitis externa, are widespread in dogs, primarily caused by Staphylococcus and Pseudomonas species. This study evaluates the prevalence and types of bacterial pathogens in affected dogs in South Korea using a meta-analytical approach. Following the PRISMA guidelines, five electronic databases were searched for relevant studies published between 1990 and 2024. Three researchers independently performed data extraction and quality assessment. A subgroup analysis explored the variability in pathogen prevalence across studies based on bacterial genus, bacterial species, publication year, sampling year, sampling location, infection type, diagnostic method, and sample size. Publication bias was evaluated using funnel plots and Egger's regression test, with all analyses conducted using the R program. Of the 944 articles, 29 met the eligibility criteria. The pooled bacterial prevalence among infected dogs was 99.95% (95%CI: 99.85-100). Staphylococcus was the most prevalent genus (95.93%), followed by Pseudomonas (48.43%), Enterococcus (20.32%), and Escherichia (17.63%). The most common species were Staphylococcus pseudintermedius (78.89%), Staphylococcus intermedius (71.43%), and Pseudomonas aeruginosa (46.13%). This study underscores the need for comprehensive treatment strategies targeting Gram-negative and Gram-positive bacteria, emphasizing further research on antimicrobial resistance patterns and treatment efficacy to enhance canine health outcomes in South Korea.

Intelligent Microcontroller-Based Infrared Attenuated Total Reflection Spectroscopy for High-Throughput Screening and Discrimination of Foodborne Fungi

Food safety became one of the most critical issues owing to the large expansion of international trading and emission of various pollutants in air, water and soil. Fungal contamination of food and feed has attracted most of the attention in the last decade because of the emerging analytical tools that facilitate the detection and discrimination of fungal species in imported foodstuff, seeds, grains, plants, meats …etc. In this work, we give an insight on the application of integrated attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy and artificial-intelligence algorithms to the determination and discrimination of fungal species/strains which potentially infect plants, seeds and grains. The proposed method is based on a microcontroller which allows the PC to analyze a large number of samples via serial connection with an UART module. Penicillium chrysogenum, Aspergillus niger, Aspergillus fumigatus, Aspergillus solani, Aspergillus flavus and two different strains of Fusarium oxysporum were used as model microorganisms. The use of artificial-intelligence algorithms herein provides the advantage of automation enabling high throughput screening of large numbers of food samples in less than 5 s. In addition, the classification accuracy is enhanced by applying these machine-learning classification techniques. Principle component analysis (PCA) was used in order to extract the spectral discriminative features from the recorded fungal FTIR spectra. Three intelligent methods of classification; namely, artificial neural network (ANN), support-vector machine (SVM) and k-nearest neighbor (KNN), were used in this study in order to prove that integration of spectroscopic measurements with varying machine-learning methods give a simple analytical tool for detection and classification of foodborne pathogens. All the utilized classifiers gave an accuracy of 100 % and were able to discriminate different species and/or strains of the investigated fungi in few milliseconds.

Engineering the bacteriophage 80 alpha endolysin as a fast and ultrasensitive detection toolbox against Staphylococcus aureus

The isolation and identification of pathogenic bacteria from a variety of samples are critical for controlling bacterial infection-related health problems. The conventional methods, such as plate counting and polymerase chain reaction-based approaches, tend to be time-consuming and reliant on specific instruments, severely limiting the effective identification of these pathogens. In this study, we employed the specificity of the cell wall-binding (CBD) domain of the Staphylococcus aureus bacteriophage 80 alpha (80α) endolysin towards the host bacteria for isolation. Amidase 3-CBD conjugated magnetic beads successfully isolated as few as 1 × 102 CFU/mL of S. aureus cells from milk, blood, and saliva. The cell wall hydrolyzing activity of 80α endolysin promoted the genomic DNA extraction efficiency by 12.7 folds on average, compared to the commercial bacterial genomic DNA extraction kit. Then, recombinase polymerase amplification (RPA) was exploited to amplify the nuc gene of S. aureus from the extracted DNA at 37 °C for 30 min. The RPA product activated Cas12a endonuclease activity to cleave fluorescently labeled ssDNA probes. We then converted the generated signal into a fluorescent readout, detectable by either the naked eye or a portable, self-assembled instrument with ultrasensitivity. The entire procedure, from isolation to identification, can be completed within 2 h. The simplicity and sensitivity of the method developed in this study make it of great application value in S. aureus detection, especially in areas with limited resource supply.

Advancements of paper-based sensors for antibiotic-resistant bacterial species identification

Evolution of antimicrobial-resistant bacterial species is on a rise. This review aims to explore the diverse range of paper-based platforms designed to identify antimicrobial-resistant bacterial species. It highlights the most important targets used for sensor development and examines the applications of nanosized particles used in paper-based sensors. This review also discusses the advantages, limitations, and applicability of various targets and detection techniques for sensing drug-resistant bacterial species using paper-based platforms.

qPCR-based phytoplankton abundance and chlorophyll a: A multi-year study in twelve large freshwater rivers across the United States

Phytoplankton overgrowth, which characterizes the eutrophication or trophic status of surface water bodies, threatens ecosystems and public health. Quantitative polymerase chain reaction (qPCR) is promising for assessing the abundance and community composition of phytoplankton. However, applications of qPCR to indicate eutrophication and trophic status, especially in lotic systems, have yet to be comprehensively evaluated. For the first time, this study correlates qPCR-based phytoplankton abundance with chlorophyll a (the most widely used indicator of eutrophication and trophic status) in multiple freshwater rivers. From early summer to late fall in 2017, 2018, and 2019, we evaluated phytoplankton, chlorophyll a, pheophytin a, and the Trophic Level Index (TLI) in twelve large freshwater rivers in three regions (western, midcontinent, and eastern) in the United States. qPCR-based phytoplankton abundance had positive allometric correlations with chlorophyll a concentration (adjusted R2 = 0.5437, p-value < 0.001), pheophytin a concentration (adjusted R2 = 0.3378, p-value <0.001), and TLI (adjusted R2 = 0.4789, p-value < 0.001). Thus, a greater phytoplankton abundance suggests a higher trophic status. This work also presents the numerical values of qPCR-based phytoplankton abundance defining the boundaries among trophic statuses (e.g., oligotrophic, mesotrophic, and eutrophic) of freshwater rivers. The sampling sites in the midcontinent rivers were more eutrophic because they had significantly higher chlorophyll a concentrations, pheophytin a concentrations, and TLI values than the sites in the western and eastern rivers. The higher phytoplankton abundance at the midcontinent sites confirmed their higher trophic status. By linking qPCR-based phytoplankton abundance to chlorophyll a, this study demonstrates that qPCR is a promising avenue to investigate the population dynamics of phytoplankton and the trophic status (or eutrophication) of freshwater rivers.

Highly sensitive SERS nanoplatform based on aptamer and vancomycin for detection of S. aureus and its clinical application

Staphylococcus aureus (S. aureus) is the most common pathogen in human purulent infections, which can cause local purulent infections, as well as pneumonia, pseudomembranous enteritis, pericarditis, and even systemic infections. The conventional methods including bacteria colony counting, polymerase chain reaction and enzyme-linked immunosorbent assay can't fully meet the requirement of highly sensitive detection of S. aureus due to their own disadvantages. Therefore, it's an urgent need to develop new platform to detect S. aureus in the early infection stage. In this study, a new surface-enhanced Raman scattering (SERS)-based nanoplatform based on dual-recognition of aptamer (Apt) and vancomycin (Van) was developed for the highly sensitive detection of S. aureus. The SERS nanoplatform consisted of two functional parts: aptamer-conjugated Fe3O4 magnetic nanoparticles (Fe3O4-Apt MNPs) for bacteria enrichment and vancomycin modified-Au nanoparticles (Van-Au NPs) as the SERS probes for S. aureus quantitative detection. Upon the target bacteria enrichment, the SERS signals of the supernatant after magnetic separation could be obtained and analyzed under different concentrations of S. aureus. The limit of detection of the proposed assay was found to be 3.27 CFU/mL. We believe that the proposed SERS-based nanoplatform has great potential as a powerful tool in the early detection of specific bacteria.

Next-generation sequencing guides the treatment of severe community-acquired pneumonia with empiric antimicrobial therapy failure: A propensity-score-matched study

BACKGROUND

Next-generation sequencing (NGS) is a promising diagnostic tool for pathogens diagnosis. The aim of this study is to evaluate the application of NGS-based antimicrobial therapy on clinical outcomes in severe community-acquired pneumonia (SCAP) patients with empiric antimicrobial therapy failure.

METHODOLOGY

We performed a multi-center, retrospective cohort of SCAP patients with initial empiric therapy failure. Propensity score (PS) matching was used to obtain balance among the baseline variables in NGS group (n = 82) and conventional group (n = 82). We compared the diagnostic performance of NGS with conventional microbial culture. We also compared the impact of NGS-based antimicrobial therapy on the prognosis of patients with empirical antimicrobial therapy failure.

RESULTS

The positive rate of NGS was higher than that of conventional pathogen detection methods (92.6% vs. 74.7%, P = 0.001). Compared to the conventional group, the NGS group has a considerably higher modifications rate of antibiotic treatment (73.2% vs. 54.9%, P = 0.015). The mortality of NGS group was significantly lower than that of conventional group (28.0% vs. 43.9%, P = 0.034). Moreover, the detection of NGS can significantly shorten the ventilation time (P = 0.046), and reduce the antibiotic cost (P = 0.026).

CONCLUSIONS

NGS is a valuable tool for microbial identification of SCAP patient with initial empiric therapy failure.

Swift and precise detection of unlabeled pathogens using a nanogap electrode impedimetric sensor facilitated by electrokinetics

For the protection of human health and environment, there is a growing demand for high-performance, user-friendly biosensors for the prompt detection of pathogenic bacteria in samples containing various substances. We present a nanogap electrode-based purely electrical impedimetric sensor that utilizes the dielectrophoresis (DEP) mechanism. Our nanogap sensor can directly and sensitively detect pathogens present at concentrations as low as 1-10 cells/assay in buffers and drinking milk without the need for separation, purification, or specific ligand binding. This is achieved by minimizing the electrical double-layer effect and electrode polarization in nanogap impedance sensors, reducing signal loss. In addition, even at low DEP voltages, nanogap sensors can quickly establish strong DEP forces between the nanogap electrodes to control the spatial concentration of pathogens around the electrodes. This activates and stabilizes inter-electrode signal transmission along the nanogap-aligned pathogens, increasing sensitivity and reducing errors during repeated measurements. The DEP-enabled nanogap impedance sensor developed in this study is valuable for a variety of pathogen detection and monitoring systems including point-of-care testing (POCT) as it can detect pathogens in diverse samples containing multiple substances quickly and with high sensitivity, is compatible with complex solutions such as food and beverages, and provides highly reproducible results without the need for separate binding and separation processes.

Fluorescent Antimicrobial Peptides Based on Nile Red: Effect of Conjugation Site and Chemistry on Wash-Free Staining of Bacteria

Fluorescent probes for bacterial detection can be obtained by conjugating antimicrobial peptides with fluorescent dyes. However, little is known about the effect of the conjugation site and linker chemistry on staining efficiency. We synthesized three conjugates of the antimicrobial peptide ubiquicidin with the environmentally sensitive fluorophore Nile Red that differed by the attachment site and the chemical composition of the linker. We showed that incorporating fluorophore as a minimalistic non-natural amino acid resulted in a superior probe compared with the typically used bioconjugation approaches. The new peptide-based probe named UNR-1 displayed red fluorescence and enabled robust wash-free staining of Gram-positive and Gram-negative bacteria. The probe exhibited selectivity over mammalian cells and enabled rapid fluorescence detection of bacteria by fluorescence microscopy and flow cytometry in an add-and-read format. Our results may foster the development of next-generation fluorescent AMPs for clinical laboratory diagnostics and medical imaging.

A Propidium Monoazide-Assisted Digital CRISPR/Cas12a Assay for Selective Detection of Live Bacteria in Sample

Escherichia coli O157:H7 (E. coli O157:H7) is a prominent pathogenic bacterium that poses serious risks to food safety and public health. Rapid and accurate detection of live E. coli O157:H7 is of great importance in food quality monitoring and clinical diagnosis. Here, we report a propidium monoazide-assisted nonamplification digital CRISPR/Cas12a assay for sensitive and rapid detection of live E. coli O157:H7. The incorporation of propidium monoazide into the method enables the selective detection of live bacteria by eliminating 98% of interference from the dead bacterial nucleic acid. Implemented on microfluidic digital chips, this method can achieve absolute quantification of nonamplified nucleic acid. The entire detection process of live bacteria can be completed within 120 min without the need for establishing a standard curve, and the sensitivity of the method reaches 1.2 × 103 CFU/mL. The method was validated using various samples, yielding results consistent with the plate counting method (Pearson's r = 0.9490). Consequently, this method holds significant potential for applications in fields requiring live bacterial detection.

New insights into the roles of fungi and bacteria in the development of medicinal plant

BACKGROUND

The interaction between microorganisms and medicinal plants is a popular topic. Previous studies consistently reported that microorganisms were mainly considered pathogens or contaminants. However, with the development of microbial detection technology, it has been demonstrated that fungi and bacteria affect beneficially the medicinal plant production chain.

AIM OF REVIEW

Microorganisms greatly affect medicinal plants, with microbial biosynthesis a high regarded topic in medicinal plant-microbial interactions. However, it lacks a systematic review discussing this relationship. Current microbial detection technologies also have certain advantages and disadvantages, it is essential to compare the characteristics of various technologies.

KEY SCIENTIFIC CONCEPTS OF REVIEW

This review first illustrates the role of fungi and bacteria in various medicinal plant production procedures, discusses the development of microbial detection and identification technologies in recent years, and concludes with microbial biosynthesis of natural products. The relationship between fungi, bacteria, and medicinal plants is discussed comprehensively. We also propose a future research model and direction for further studies.

Artificial neural network-based shelf life prediction approach in the food storage process: A review

The prediction of food shelf life has become a vital tool for distributors and consumers, enabling them to determine storage and optimal edible time, thus avoiding unexpected food waste. Artificial neural network (ANN) have emerged as an effective, fast and accurate method for modeling, simulating and predicting shelf life in food. ANNs are capable of tackling nonlinear, complex and ill-defined problems between the variables without prior knowledge. ANN model exhibited excellent fit performance evidenced by low root mean squared error and high correlation coefficient. The low relative error between actual values and predicted values from the ANN model demonstrates its high accuracy. This paper describes the modeling of ANN in food quality prediction, encompassing commonly used ANN architectures, ANN simulation techniques, and criteria for evaluating ANN model performance. The review focuses on the application of ANN for modeling nonlinear food quality during storage, including dairy, meat, aquatic, fruits, and vegetables products. The future prospects of ANN development mainly focus on optimal models and learning algorithm selection, multiple model fusion, self-learning and self-correcting shelf-life prediction model development, and the potential utilization of deep learning techniques.

Clinical Application of Metagenomic Next-Generation Sequencing in Sepsis Patients with Early Antibiotic Treatment

Purpose

This study aimed to evaluate the clinical utility of metagenomic next-generation sequencing (mNGS) in sepsis patients who received early empirical antibiotic treatment.

Patients and Methods

A retrospective analysis was conducted on clinical data from sepsis patients diagnosed in the Emergency Intensive Care Unit (EICU) between April 2019 and May 2023. All patients underwent standard conventional microbiological testing. Patients were categorized into either the mNGS group or the control group based on whether they underwent mNGS tests. Baseline variables were matched using propensity scores.

Results

Out of 461 sepsis patients screened, 130 were included after propensity matching, with 65 patients in each group. Despite prior antibiotic treatment, 57 cases (87.69%) in the mNGS group had positive mNGS results, exceeding the culture detection rate (52.31%). Besides, a higher proportion of patients in the mNGS group experienced antibiotic adjustments compared to the control group (72.31% vs 53.85%). Mortality rates were also compared based on the duration of antibiotic exposure before mNGS sampling. Patients exposed to antibiotics for less than 24 hours had a lower mortality rate compared to those exposed for over 8 days (22.22% vs 42.86%). COX multivariate analysis identified mNGS testing, underlying diseases, lymphocyte percentage, infection site (respiratory and bloodstream) as independent risk factors for mortality in sepsis patients.

Conclusion

With increased antibiotic exposure time, the positive rate of culture testing significantly decreased (44.44% vs 59.52% vs 35.71%, P = 0.031), whereas the positive rate of mNGS remained stable (77.78% vs 88.10% vs 92.86%, P = 0.557). mNGS demonstrated less susceptibility to antibiotic exposure. Early mNGS detection positively impacted the prognosis of sepsis patients.

Fluorometric determination of mecA gene in MRSA with a graphene-oxide based bioassay using flap endonuclease 1-assisted target recycling and Klenow fragment-triggered signal amplification

Methicillin-resistant Staphylococcus aureus (MRSA) is a multidrug-resistant bacterium that causes a wide range of illnesses, necessitating the development of new technologies for its detection. Herein, we propose a graphene oxide (GO)-based sensing platform for the detection of mecA gene in MRSA using flap endonuclease 1 (FEN1)-assisted target recycling and Klenow fragment (KF)-triggered signal amplification. Without the target, all the DNA probes were adsorbed onto GO, resulting in fluorescence quenching of the dye. Upon the addition of the target, a triple complex was formed that triggered FEN1-assisted target recycling and initiated two polymerization reactions with the assistance of KF polymerase, generating numerous dsDNA that were repelled by GO. These dsDNAs triggered fluorescence enhancement when SYBR Green I was added. Therefore, the target DNA was quantified by measuring the fluorescence at excitation and emission wavelengths of 480/526 nm. This mecA gene assay showed a good linear range from 1 to 50 nM with a lower limit of detection of 0.26 nM, and displayed good applicability to the analysis of real samples. Thus, a new method for monitoring MRSA has been developed that has great potential for early clinical diagnosis and treatment.

Exonuclease-III Assisted the Target Recycling Coupling with Hybridization Chain Reaction for Sensitive mecA Gene Analysis by Using PGM

In the field of neonatal infections nursing, methicillin-resistant Staphylococcus aureus (MRSA) is a major bacterial pathogen. Here, we present a portable biosensor for MRSA detection that is both highly sensitive and portable, owing to its implementation on the personal glucose meter (PGM) platform. The H probe was fixed on the magnetic bead for mecA gene analysis. A blunt 3' terminus appeared in the MBs-H probe when the mecA gene was present. Exonuclease-III (Exo-III) recognized the blunt terminus and cleaved it, freeing the mecA gene and so facilitating target recycling. In the meantime, the remaining H probe-initiated hybridization chain reaction (HCR) led to the desired signal amplification. Portable quantitative detection of mecA gene is possible because PGM can read the quantity of invertase tagged on HCR product. After optimizing several experimental parameters, such as the concentration of Exo-III and incubation time, the constructed sensor is extremely sensitive, with a detection limit of 2 CFU/mL. The results from this sensitive PGM-based sensor are in agreement with those obtained from plate counting methods, suggesting that it can be used to accurately assess the MRSA content in artificial clinical samples. In addition, the PGM sensor can significantly cut down on time spent compared to plate counting techniques. The manufactured sensor provides a promising option for accurate identification of pathogenic bacteria.

Prussian-Blue-Nanozyme-Enhanced Simultaneous Immunochromatographic Control of Two Relevant Bacterial Pathogens in Milk

Salmonella typhimurium and Listeria monocytogenes are relevant foodborne bacterial pathogens which may cause serious intoxications and infectious diseases in humans. In this study, a sensitive immunochromatographic analysis (ICA) for the simultaneous detection of these two pathogens was developed. For this, test strips containing two test zones with specific monoclonal antibodies (MAb) against lipopolysaccharides of S. typhimurium and L. monocytogenes and one control zone with secondary antibodies were designed, and the double-assay conditions were optimized to ensure high analytical parameters. Prussian blue nanoparticles (PBNPs) were used as nanozyme labels and were conjugated with specific MAbs to perform a sandwich format of the ICA. Peroxidase-mimic properties of PBNPs allowed for the catalytic amplification of the colorimetric signal on test strips, enhancing the assay sensitivity. The limits of detection (LODs) of Salmonella and Listeria cells were 2 × 102 and 7 × 103 cells/mL, respectively. LODs were 100-fold less than those achieved due to the ICA based on the traditional gold label. The developed double ICA was approbated for the detection of bacteria in cow milk samples, which were processed by simple dilution by buffer before the assay. For S. typhimurium and L. monocytogenes, the recoveries from milk were 86.3 ± 9.8 and 118.2 ± 10.5% and correlated well with those estimated by the enzyme-linked immunosorbent assay as a reference method. The proposed approach was characterized by high specificity: no cross-reactivity with other bacteria strains was observed. The assay satisfies the requirements for rapid tests: a full cycle from sample acquisition to result assessment in less than half an hour. The developed ICA has a high application potential for the multiplex detection of other foodborne pathogens.

Research Progress of Machine Learning in Extending and Regulating the Shelf Life of Fruits and Vegetables

Fruits and vegetables are valued for their flavor and high nutritional content, but their perishability and seasonality present challenges for storage and marketing. To address these, it is essential to accurately monitor their quality and predict shelf life. Unlike traditional methods, machine learning efficiently handles large datasets, identifies complex patterns, and builds predictive models to estimate food shelf life. These models can be continuously refined with new data, improving accuracy and robustness over time. This article discusses key machine learning methods for predicting shelf life and quality control of fruits and vegetables, with a focus on storage conditions, physicochemical properties, and non-destructive testing. It emphasizes advances such as dataset expansion, model optimization, multi-model fusion, and integration of deep learning and non-destructive testing. These developments aim to reduce resource waste, provide theoretical basis and technical guidance for the formation of modern intelligent agricultural supply chains, promote sustainable green development of the food industry, and foster interdisciplinary integration in the field of artificial intelligence.

Fast and sensitive detection of viable Escherichia coli O157:H7 using a microwell-confined and propidium monoazide-assisted digital CRISPR microfluidic platform

Escherichia coli O157:H7 is a major foodborne pathogen that poses a significant threat to food safety and human health. Rapid and sensitive detection of viable Escherichia coli O157:H7 can effectively prevent food poisoning. Here, we developed a microwell-confined and propidium monoazide-assisted digital CRISPR microfluidic platform for rapid and sensitive detection of viable Escherichia coli O157:H7 in food samples. The reaction time is significantly reduced by minimizing the microwell volume, yielding qualitative results in 5 min and absolute quantitative results in 15 min. With the assistance of propidium monoazide, this platform can eliminate the interference from 99% of dead Escherichia coli O157:H7. The direct lysis method obviates the need for a complex nucleic acid extraction process, offering a limit of detection of 3.6 × 101 CFU mL-1 within 30 min. Our results demonstrated that the platform provides a powerful tool for rapid detection of Escherichia coli O157:H7 and provides reliable guidance for food safety testing.

Salmonella Detection in Food Using a HEK-hTLR5 Reporter Cell-Based Sensor

The development of a rapid, sensitive, specific method for detecting foodborne pathogens is paramount for supplying safe food to enhance public health safety. Despite the significant improvement in pathogen detection methods, key issues are still associated with rapid methods, such as distinguishing living cells from dead, the pathogenic potential or health risk of the analyte at the time of consumption, the detection limit, and the sample-to-result. Mammalian cell-based assays analyze pathogens' interaction with host cells and are responsive only to live pathogens or active toxins. In this study, a human embryonic kidney (HEK293) cell line expressing Toll-Like Receptor 5 (TLR-5) and chromogenic reporter system (HEK dual hTLR5) was used for the detection of viable Salmonella in a 96-well tissue culture plate. This cell line responds to low concentrations of TLR5 agonist flagellin. Stimulation of TLR5 ligand activates nuclear factor-kB (NF-κB)-linked alkaline phosphatase (AP-1) signaling cascade inducing the production of secreted embryonic alkaline phosphatase (SEAP). With the addition of a ρ-nitrophenyl phosphate as a substrate, a colored end product representing a positive signal is quantified. The assay's specificity was validated with the top 20 Salmonella enterica serovars and 19 non-Salmonella spp. The performance of the assay was also validated with spiked food samples. The total detection time (sample-to-result), including shortened pre-enrichment (4 h) and selective enrichment (4 h) steps with artificially inoculated outbreak-implicated food samples (chicken, peanut kernel, peanut butter, black pepper, mayonnaise, and peach), was 15 h when inoculated at 1-100 CFU/25 g sample. These results show the potential of HEK-DualTM hTLR5 cell-based functional biosensors for the rapid screening of Salmonella.

Electrochemical Impedance Spectroscopy-Based Microfluidic Biosensor Using Cell-Imprinted Polymers for Bacteria Detection

The rapid and sensitive detection of bacterial contaminants using low-cost and portable point-of-need (PoN) biosensors has gained significant interest in water quality monitoring. Cell-imprinted polymers (CIPs) are emerging as effective and inexpensive materials for bacterial detection as they provide specific binding sites designed to capture whole bacterial cells, especially when integrated into PoN microfluidic devices. However, improving the sensitivity and detection limits of these sensors remains challenging. In this study, we integrated CIP-functionalized stainless steel microwires (CIP-MWs) into a microfluidic device for the impedimetric detection of E. coli bacteria. The sensor featured two parallel microchannels with three-electrode configurations that allowed simultaneous control and electrochemical impedance spectroscopy (EIS) measurements. A CIP-MW and a non-imprinted polymer (NIP)-MW suspended perpendicular to the microchannels served as the working electrodes in the test and control channels, respectively. Electrochemical spectra were fitted with equivalent electrical circuits, and the charge transfer resistances of both cells were measured before and after incubation with target bacteria. The charge transfer resistance of the CIP-MWs after 30 min of incubation with bacteria was increased. By normalizing the change in charge transfer resistance and analyzing the dose-response curve for bacterial concentrations ranging from 0 to 107 CFU/mL, we determined the limits of detection and quantification as 2 × 102 CFU/mL and 1.4 × 104 CFU/mL, respectively. The sensor demonstrated a dynamic range of 102 to 107 CFU/mL, where bacterial counts were statistically distinguishable. The proposed sensor offers a sensitive, cost-effective, durable, and rapid solution for on-site identification of waterborne pathogens.

Recombinase-aided amplification assay for rapid detection of imipenem-resistant Pseudomonas aeruginosa and rifampin-resistant Pseudomonas aeruginosa

The indiscriminate use of antibiotics has resulted in a growing resistance to drugs in Pseudomonas aeruginosa. The identification of antibiotic resistance genes holds considerable clinical significance for prompt diagnosis. In this study, we established and optimized a Recombinase-Aided Amplification (RAA) assay to detect two genes associated with drug resistance, oprD and arr, in 101 clinically collected P. aeruginosa isolates. Through screening for the detection or absence of oprD and arr, the results showed that there were 52 Imipenem-resistant P. aeruginosa (IRPA) strains and 23 Rifampin-resistant P. aeruginosa (RRPA) strains. This method demonstrated excellent detection performance even when the sample concentration is 10 copies/μL at isothermal conditions and the results could be obtained within 20 minutes. The detection results were in accordance with the results of conventional PCR and Real-time PCR. The detection outcomes of the arr gene were consistently with the resistance spectrum. However, the antimicrobial susceptibility results revealed that 65 strains were resistant to imipenem, while 49 strains sensitive to imipenem with oprD were identified. This discrepancy could be attributed to genetic mutations. In summary, the RAA has higher sensitivity, shorter time, and lower-cost instrument requirements than traditional detection methods. In addition, to analyze the epidemiological characteristics of the aforementioned drug-resistant strains, we conducted Multilocus Sequence Typing (MLST), virulence gene, and antimicrobial susceptibility testing. MLST analysis showed a strong correlation between the sequence types ST-1639, ST-639, ST-184 and IRPA, while ST-261 was the main subtype of RRPA. It was observed that these drug-resistant strains all possess five or more virulence genes, among which exoS and exoU do not coexist, and they are all multidrug-resistant strains. The non-coexistence of exoU and exoS in P.aeruginosa is related to various factors including bacterial regulatory mechanisms and pathogenic mechanisms. This indicates that the relationship between the presence of virulence genes and the severity of patient infection is worthy of attention. In conclusion, we have developed a rapid and efficient RAA (Recombinase-Aided Amplification) detection method that offers significant advantages in terms of speed, simplicity, and cost-effectiveness (especially in time and equipment aspect). This novel approach is designed to meet the demands of clinical diagnostics.

Bacterial Pesticides: Mechanism of Action, Possibility of Food Contamination, and Residue Analysis Using MS

As Sustainable Development Goals (SDGs) and the realities of climate change become widely accepted around the world, the next-generation of integrated pest management will become even more important for establishing a sustainable food production system. To meet the current challenge of food security and climate change, biological control has been developed as one sustainable crop protection technology. However, most registered bacteria are ubiquitous soil-borne bacteria that are closely related to food poisoning and spoilage bacteria. Therefore, this review outlined (1) the mechanism of action of bacterial pesticides, (2) potential concerns about secondary contamination sources associated with past food contamination, and, as a prospective solution, focused on (3) principles and methods of bacterial identification, and (4) the possibility of identifying residual bacteria based on mass spectrometry.

Chemical tongues as multipurpose bioanalytical tools for the characterization of complex biological samples

Chemical tongues are emerging powerful bioanalytical tools that mimic the mechanism of the human taste system to recognize the comprehensive characteristics of complex biological samples. By using an array of chromogenic or fluorogenic probes that interact non-specifically with various components in the samples, this tool generates unique colorimetric or fluorescence patterns that reflect the biological composition of a sample. These patterns are then analyzed using multivariate analysis or machine learning to distinguish and classify the samples. This review focuses on our efforts to provide an overview of the fundamental principles of chemical tongues, probe design, and their applications as versatile tools for analyzing proteins, cells, and bacteria in biological samples. Compared to conventional methods that rely on specific targeting (e.g., antibodies or enzymes) or comprehensive omics analyses, chemical tongues offer advantages in terms of cost and the ability to analyze samples without the need for specific biomarkers. The complementary use of chemical tongues and conventional methods is expected to enable a more detailed understanding of biological samples and lead to the elucidation of new biological phenomena.