Detection Of Bacterial Pathogens Research Articles

Isolation and chemical immobilization of E. coli‐specific bacteriophage with NH2‐MIL‐101(Fe) MOF, a high photoluminescence rod‐shaped microcrystals for low‐level bacteria detection

As concern raised by the World Health Organization (WHO) of antibiotic‐resistant and bio‐defensive bacteria, a metal–organic framework (MOF) based optical biosensor came into consideration for precise, quick, and sensitive detection of Escherichia coli (E. coli) (ATCC 10799) using bacteriophage as a bio‐recognition element. In the present study, amine‐functionalized Fe‐based MOF, i.e., NH2‐MIL‐101(Fe), was synthesized by the solvothermal method (approx. 531–1106 nm in size and 20 mV zeta potentials by DLS) and further characterized by SEM, XRD, ATR‐FTIR, UV–VIS, and photoluminescent (PL) spectroscopy. The lytic bacteriophage was isolated from a sewage sample, purified, and concentrated using the ultra‐centrifugation method and achieved a high titer of 7.3 × 1012 PFU/ml. The concentration, stability, and accessible receptor binding domains (RBDs) of the biorecognition element for binding with their analytes play an important role in developing sensitive and specific biosensor systems. To fulfill the mentioned criteria, optimized glutaraldehyde concentration was estimated at 0.25%, at 30 °C for conjugating maximum bacteriophage titer of 8.6 × 105 PFU/ml for each 1 mg amine functionalized iron‐based MOF. The synthesized detection probe has shown excellent photoluminescence and antibacterial activity and achieved a detection limit of 652 CFU/ml over a bacterial detection concentration range from 5.78 × 101 to 5.78 × 106 CFU/ml for E. coli with 10–12 min of response time, high specificity, and long‐term stability even at room temperature. Therefore, it can be inferred that this MOF‐based strategy can be helpful in the specific and sensitive detection of various bacterial pathogens using bacteriophage as a bio‐recognition element.

Machine Learning-Assisted Raman Spectroscopy and SERS for Bacterial Pathogen Detection: Clinical, Food Safety, and Environmental Applications

Detecting pathogenic bacteria and their phenotypes including microbial resistance is crucial for preventing infection, ensuring food safety, and promoting environmental protection. Raman spectroscopy offers rapid, seamless, and label-free identification, rendering it superior to gold-standard detection techniques such as culture-based assays and polymerase chain reactions. However, its practical adoption is hindered by issues related to weak signals, complex spectra, limited datasets, and a lack of adaptability for detection and characterization of bacterial pathogens. This review focuses on addressing these issues with recent Raman spectroscopy breakthroughs enabled by machine learning (ML), particularly deep learning methods. Given the regulatory requirements, consumer demand for safe food products, and growing awareness of risks with environmental pathogens, this study emphasizes addressing pathogen detection in clinical, food safety, and environmental settings. Here, we highlight the use of convolutional neural networks for analyzing complex clinical data and surface enhanced Raman spectroscopy for sensitizing early and rapid detection of pathogens and analyzing food safety and potential environmental risks. Deep learning methods can tackle issues with the lack of adequate Raman datasets and adaptability across diverse bacterial samples. We highlight pending issues and future research directions needed for accelerating real-world impacts of ML-enabled Raman diagnostics for rapid and accurate diagnosis and surveillance of pathogens across critical fields.

Molecular Detection of Viral and Bacterial Pathogens in Red Foxes (Vulpes vulpes) from Italy.

Animals, including wildlife, are part of One-Health concept since many infectious diseases can affect both humans and animals. In this study, 126 red foxes (Vulpes vulpes) from Northern Italy in 2022-2023 were tested by molecular assays for Protoparvovirus carnivoran 1 (PPVC-1), Canine adenovirus type 1 and 2 (CAdV-1 and CAdV-2), Circovirus canine (CanineCV), Canine distemper virus (CDV), and Leptospira spp. A total of 39 of 126 (30.9%) red foxes were infected with at least one pathogen and five of these were coinfected: 20/126 (15.9%) red foxes tested positive for PPVC-1, 3/126 (2.4%) for CAdV, 20/126 (15.9%) for CanineCV, and 2/126 (1.6%) for Leptospira spp. DNA. No foxes tested positive for CDV RNA. The pathogens identified were genetically analysed. New findings were reported such as a fox with multiple feline panleukopenia virus (FPV) and canine parvovirus type 2b (CPV-2b) infection associated with quasispecies dynamics, typical genetic characteristics of the identified CanineCV, and the first detection in red foxes of Leptospira ST198 related to L. interrogans serogroup Australis. Further studies are necessary to investigate the transmission between domestic animals and wildlife and to understand the role of red foxes in the maintenance of these pathogens not only in the wild but also in urban and peri-urban environments.

Chicken IgY based electrochemical immunosensor for the detection of bacterial pathogen E. coli in water

On a global scale, approximately 1 in 8 (13.6 %) of worldwide mortalities have been associated with bacterial infections. Therefore, the necessity for rapid and cost-effective diagnostic kits is paramount to accurately diagnose bacterial infections. Besides, the utilization of IgY antibodies emerges as a natural and economical option for effective sensing of pathogens. Notably, electrochemical IgY-based immunosensors amalgamate the inherent specificity of immunoreactions with highly sensitive electrochemical sensors, thereby facilitating the advancement of next generation Point-of-Care (POC) devices. In this work, a label-free IgY-based immunosensor for the detection of E. coli, employing Boron and Nitrogen-doped Graphene Quantum Dots (B, N-doped GQDs) nanomaterial as the electrode interface was successfully devised. This developed immunosensor has demonstrated commendable sensitivity of 0.0395 µA. (CFU/mL)−1 & 0.0491 µA. (CFU/mL)−1 from Cyclic Voltammetry (CV) & Amperometry (Amp) techniques across various E. coli concentrations (0.5 x 101 – 107 CFU/mL) and exhibited exceptional selectivity of < 5.88 %, thereby ensuring precise E. coli detection while mitigating interference from other agents. Moreover, real sample analysis with the developed sensor displayed exemplary recoveries, affirming its efficacy in detecting E. coli. The immunosensor’s promising results in terms of its figures-of-merit underscore the potential diagnostic utility of IgY in E. coli screening bacterial sensing applications.

MassARRAY: a high-throughput solution for rapid detection of foodborne pathogens in real-world settings.

Bacterial foodborne pathogens pose a substantial global public health concern, prompting government agencies and public health organizations to establish food safety guidelines and regulations aimed at mitigating the risk of foodborne illness. The advent of DNA-based amplification coupled with mass spectrometry, known as MassARRAY analysis, has proven to be a highly precise, sensitive, high-throughput, and cost-effective method for bacterial detection. This study aimed to develop, validate, and evaluate a MassARRAY-based assay for the detection and identification of significant enteropathogenic bacteria. The MassARRAY-based assay was developed for the detection of 10 crucial bacterial foodborne pathogens, including Campylobacter coli, Campylobacter jejuni, Clostridium perfringens, Escherichia coli, Enterococcus faecalis, Enterococcus faecium, Listeria monocytogenes, Salmonella spp., Shigella spp., and Staphylococcus aureus. The assay was optimized using the reference gDNA (n = 19), followed by validation using gDNA (n = 85) of reference and laboratory isolates. Additionally, the evaluation of the assay's reaction using a mixture of gDNA from all nine targeted species was performed. The limit of detection of the developed MassARRAY-based assay was determined using bacterial cells. Moreover, the validation method for field samples was evaluated by comparing it with standard microbiological testing methods routinely analyzed. The developed MassARRAY-based assay demonstrated 100% concordance with known bacterial pure cultures. The assay's reaction using a mixture of gDNA from all nine targeted species revealed the MassARRAY's capability to detect all targeted species in a single assay with the lowest concentration of 1 ng/μL of gDNA. The limits of detection of the assay range from 357 ± 101 to 282,000 ± 79,196 cells. Moreover, the validation of the assay in field samples revealed a 100% correlation between the data obtained from the standard microbiological method and the MassARRAY-based assay. These findings suggested that the developed MassARRAY-based assay exhibited the excellence in high-throughput detection of foodborne bacterial pathogens with high accuracy, reliability, and potential applicability within real-world field samples.

Estimated prevalence and associations of sexually transmissible bacterial enteric pathogens in asymptomatic men who have sex with men: a systematic review and meta-analysis

ObjectiveThe reservoir of sexually transmissible bacterial enteric pathogens in asymptomatic men who have sex with men (MSM) may impact future outbreaks, and the evolution of antimicrobial resistance. We aimed to...

Rapid detection of viral, bacterial, fungal, and oomycete pathogens on tomato with microneedles, LAMP on a microfluidic chip, and smartphone device.

Rapid detection of plant diseases before they escalate can improve disease control. Our team has developed rapid nucleic acid extraction methods with microneedles (MN) and combined these with LAMP assays for pathogen detection in the field. In this work, we developed LAMP assays for early blight (Alternaria linariae, A. alternata, and A. solani) and bacterial spot of tomato (Xanthomonas perforans) and validated these LAMP assays and two previously developed LAMP assays for tomato spotted wilt virus and late blight. Tomato plants were inoculated and disease severity was measured. Extractions were performed using MN and LAMP assays were run in tubes (with hydroxynaphthol blue) on a heat block or on a newly designed microfluidic slide chip on a heat block or a slide heater. Fluorescence on the microfluidic chip slides was visualized using EvaGreen and photographed on a smartphone. Plants inoculated with X. perforans or tomato spotted wilt virus tested positive prior to visible disease symptoms, while P. infestans and A. linariae were detected at the time of visual disease symptoms. LAMP assays were more sensitive than PCR and the limit of detection was 1 pg of DNA for both A. linariae and X. perforans. The LAMP assay designed for early blight detected all three species of Alternaria that infect tomato and is thus an Alternaria spp. assay. This study demonstrates the utility of rapid MN extraction followed by LAMP on a microfluidic chip for rapid diagnosis of four important tomato pathogens.

Disposable electrochemical biosensors for the detection of bacteria in the light of antimicrobial resistance.

Persistent and inappropriate use of antibiotics is causing rife antimicrobial resistance (AMR) worldwide. Common bacterial infections are thus becoming increasingly difficult to treat without the use of last resort antibiotics. This has necessitated a situation where it is imperative to confirm the infection to be bacterial, before treating it with antimicrobial speculatively. Conventional methods of bacteria detection are either culture based which take anywhere between 24 and 96 hor require sophisticated molecular analysis equipment with libraries and trained operators. These are difficult propositions for resource limited community healthcare setups of developing or less developed countries. Customized, inexpensive, point-of-care (PoC) biosensors are thus being researched and developed for rapid detection of bacterial pathogens. The development and optimization of disposable sensor substrates is the first and crucial step in development of such PoC systems. The substrates should facilitate easy charge transfer, a high surface to volume ratio, be tailorable by the various bio-conjugation chemistries, preserve the integrity of the biorecognition element, yet be inexpensive. Such sensor substrates thus need to be thoroughly investigated. Further, if such systems were made disposable, they would attain immunity to biofouling. This article discusses a few potential disposable electrochemical sensor substrates deployed for detection of bacteria for environmental and healthcare applications. The technologies have significant potential in helping reduce bacterial infections and checking AMR. This could help save lives of people succumbing to bacterial infections, as well as improve the overall quality of lives of people in low- and middle-income countries.

Detection and diagnosis of bacterial pathogens in urine using laser-induced breakdown spectroscopy

The presence of bacterial cells from three species has been detected in clinical specimens of human urine using laser-induced breakdown spectroscopy (LIBS) by using a partial least squares discriminant analysis (PLS-DA) of 360 spectra obtained from 12 specimens of infected urine and 239 spectra obtained from eight specimens of sterile urine. Nominally sterile urine specimens obtained from four patients at a local hospital after being screened negative for the presence of bacterial pathogens were spiked with known aliquots of Escherichia coli, Staphylococcus aureus, and Enterobacter cloacae to simulate clinical urinary tract infections. Fifteen emission line intensities measured from the LIBS spectra and 92 ratios of those line intensities were used as 107 independent variables in the PLS-DA for discrimination between bacteria-containing specimens and sterile specimens. The PLS-DA models possessed a 98.3% sensitivity and a 97.9% specificity for the detection of pathogenic cells in urine when single-shot LIBS spectra were tested. To increase the signal to noise ratio, thirty spectra acquired from a single specimen were also averaged together and the averaged spectra were used to construct a model. When each averaged spectrum was withheld from the model individually for testing, the diagnostic test possessed a 100% sensitivity and a 100% specificity for the detection of bacterial cells in urine, although the number of test spectra was necessarily reduced.The entire LIBS spectrum from 200 nm – 590 nm was input into an artificial neural network analysis with principal component analysis pre-processing (PCA-ANN) to diagnose the bacterial species once detected. This PCA-ANN test possessed an overall sensitivity of 97.2%, an overall specificity of 98.6%, and an overall classification accuracy of 97.9% when using 80% of the data to build a model and withholding 20% for cross-validation testing. The PCA-ANN was also performed on each of the 12 bacteria-containing filters individually, using the other 11 filters to build the model. The average sensitivity of this test, calculated by averaging the sensitivities measured for each of the three bacterial species, was 70.9% and the average specificity was 85.5%. Based on these results, the average classification accuracy for the test when used to discriminate between the three microorganisms was 80.6%. These results indicate the potential usefulness of LIBS for rapidly detecting and possibly diagnosing urinary tract infections in a clinical setting.

Agreement of antimicrobial susceptibility testing of Pasteurella multocida and Mannheimia haemolytica found in pre-weaned dairy calves with bovine respiratory disease

Bovine respiratory disease (BRD) is one of the primary dis­eases affecting pre-weaned dairy calves and involves respi­ratory pathogens, such as Mannheimia haemolytica (Mh) and Pasteurella multocida (Pm). While agreement between methods for detection of bacterial pathogens in dairy calves with BRD is very good, there are limited data regarding how antimicro­bial susceptibility (AS) of isolates collected via these methods agree. The objective was to evaluate agreement among the AS profiles of Mh or Pm obtained by transtracheal wash (TTW), nasal swab (NS), nasopharyngeal swab (DNP), and bronchoal­veolar lavage (BAL) in pre-weaned dairy calves with BRD that were positive in all 4 samples.

Bacterial enrichment prior to third-generation metagenomic sequencing improves detection of BRD pathogens and genetic determinants of antimicrobial resistance in feedlot cattle.

Bovine respiratory disease (BRD) is one of the most important animal health problems in the beef industry. While bacterial culture and antimicrobial susceptibility testing have been used for diagnostic testing, the common practice of examining one isolate per species does not fully reflect the bacterial population in the sample. In contrast, a recent study with metagenomic sequencing of nasal swabs from feedlot cattle is promising in terms of bacterial pathogen identification and detection of antimicrobial resistance genes (ARGs). However, the sensitivity of metagenomic sequencing was impeded by the high proportion of host biomass in the nasal swab samples. This pilot study employed a non-selective bacterial enrichment step before nucleic acid extraction to increase the relative proportion of bacterial DNA for sequencing. Non-selective bacterial enrichment increased the proportion of bacteria relative to host sequence data, allowing increased detection of BRD pathogens compared with unenriched samples. This process also allowed for enhanced detection of ARGs with species-level resolution, including detection of ARGs for bacterial species of interest that were not targeted for culture and susceptibility testing. The long-read sequencing approach enabled ARG detection on individual bacterial reads without the need for assembly. Metagenomics following non-selective bacterial enrichment resulted in substantial agreement for four of six comparisons with culture for respiratory bacteria and substantial or better correlation with qPCR. Comparison between isolate susceptibility results and detection of ARGs was best for macrolide ARGs in Mannheimia haemolytica reads but was also substantial for sulfonamide ARGs within M. haemolytica and Pasteurella multocida reads and tetracycline ARGs in Histophilus somni reads. By increasing the proportion of bacterial DNA relative to host DNA through non-selective enrichment, we demonstrated a corresponding increase in the proportion of sequencing data identifying BRD-associated pathogens and ARGs in deep nasopharyngeal swabs from feedlot cattle using long-read metagenomic sequencing. This method shows promise as a detection strategy for BRD pathogens and ARGs and strikes a balance between processing time, input costs, and generation of on-target data. This approach could serve as a valuable tool to inform antimicrobial management for BRD and support antimicrobial stewardship.

Effects of natural treatments on the varroa mite infestation levels and overall health of honey bee (Apis mellifera) colonies.

The survival of the honey bee (Apis mellifera), which has a crucial role in pollination and ecosystem maintenance, is threatened by many pathogens, including parasites, bacteria, fungi and viruses. The ectoparasite Varroa destructor is considered the major cause of the worldwide decline in honey bee colony health. Although several synthetic acaricides are available to control Varroa infestations, resistant mites and side effects on bees have been documented. The development of natural alternatives for mite control is therefore encouraged. The study aims at exploring the effects of cinnamon and oregano essential oils (EOs) and of a mixed fruit cocktail juice on mite infestation levels and bee colony health. A multi-method study including hive inspection, mite count, molecular detection of fungal, bacterial and viral pathogens, analysis of defensin-1, hymenoptaecin and vitellogenin immune gene expression, colony density and honey production data, was conducted in a 20-hive experimental apiary. The colonies were divided into five groups: four treatment groups and one control group. The treatment groups were fed on a sugar syrup supplemented with cinnamon EO, oregano EO, a 1:1 mixture of both EOs, or a juice cocktail. An unsupplemented syrup was, instead, used to feed the control group. While V. destructor affected all the colonies throughout the study, no differences in mite infestation levels, population density and honey yield were observed between treatment and control groups. An overexpression of vitellogenin was instead found in all EO-treated groups, even though a significant difference was only found in the group treated with the 1:1 EO mixture. Viral (DWV, CBPV and BQCV), fungal (Nosema ceranae) and bacterial (Melissococcus plutonius) pathogens from both symptomatic and asymptomatic colonies were detected.

Establishment of real-time viral pathogen detection method-surface-enhanced Raman scattering system for porcine circovirus type II

The transition towards large-scale intensive pig farming methods has resulted in an increase in cases of complex, mixed infections, and secondary infections. Porcine circovirus (PCV), the causative agent of PCV disease (PCVD), is a small nonenveloped DNA virus containing a single-stranded circular genome. Previously, PCV was even considered nonpathogenic to pigs. However, a novel strain of PCV, designated PCV-2, has been associated with various disease syndromes in pigs over the last 5 years. PCV-2 primarily induces post-weaning multisystemic wasting syndrome, characterized by clinical signs such as debility, dyspnea, palpable lymphadenopathy, diarrhea, and pallor or icterus. PCV-2 is now recognized as an important emerging pathogen. Early diagnosis and disease prevention have become crucial for effective pig farm management. However, clinical diagnosis may be complicated by antibiotic treatment and atypical disease symptoms. To ensure accurate pathology diagnosis, it is essential to integrate robust laboratory diagnostics with traditional methods. Surface-enhanced Raman Scattering (SERS) spectroscopy has emerged as a potentially powerful technique for whole-organism fingerprinting, enabling rapid identification of bacteria. Biosensors utilizing SERS offer promising capabilities for sensitive and quick detection of bacterial pathogens, thereby reducing diagnosis time. In this study, our aim was to characterize and evaluate a SERS-based diagnostic system for detecting and identifying PCV-2 present in pooled swine sera and feces. We compared the spectra of PCV-2 recovered from the specimens to those of pure PCV-2 to determine the PCV-2 molecular fingerprint. Our results demonstrated successful detection, identification, and classification of PCV-2 in swine sera and feces using SERS. SERS provided reproducible molecular spectroscopic signatures suitable for analytical applications. This approach presents a new and potent tool for real-time surveillance of animal viral pathogens in clinical settings.

Development of DNA Microarray for Parallel Detection of Community-Acquired Pneumonia Bacterial Pathogens

Development of DNA Microarray for Parallel Detection of Community-Acquired Pneumonia Bacterial Pathogens

Comparative performance of biofire pneumonia panel and standard culture-based methods for diagnosing pneumonia in critically ill patients: Impact on antibiotic stewardship

IntroductionLower respiratory tract infections (LRTIs) are a common cause of morbidity and mortality worldwide. Accurate identification of the pathogens causing LRTIs is crucial for ensuring of diagnostic and antibiotic stewardship. The Biofire Pneumonia Panel (BFPP) is a molecular diagnostic test that allows rapid detection of various bacterial and viral pathogens. In this study, we compared the performance of BFPP with standard culture methods for the detection of pathogens. Materials and methodsRespiratory samples from 70 patient with suspected LRTIs were tested using both BFPP and standard culture methods. The distribution of isolated bacterial pathogens was analyzed, and the sensitivity and specificity of BFPP were calculated. Additionally, the performance of BFPP in detecting antimicrobial resistance genes was evaluated. The results were compared with those obtained from VITEK-2 antimicrobial susceptibility testing and culture-based methods. ResultsAmong the suspected LRTI cases, BFPP identified a single pathogen in 32.8% of cases and multiple pathogens in 40% of cases. The standard culture method detected a single pathogen in 47.1% of cases. BFPP showed a sensitivity of 93.9% and a specificity of 45.9% for the total sample. The performance of BFPP in detecting antimicrobial resistance genes varied for different pathogens with overall sensitivity of 40.1% and specificity of 95.9%. ConclusionThe Biofire Pneumonia Panel (BFPP) demonstrated high sensitivity for several bacterial pathogens, indicating its potential as a rapid diagnostic tool. However, its performance varied for different microorganisms, and it had limitations in detecting certain pathogens and antimicrobial resistance genes for which still required more further studies to explore different resistance gene mechanism that can be incorporated in this panel in future. The BFPP can complement standard culture methods as a rapid tool in the diagnosis of LRTIs.

Towards facilitated interpretation of shotgun metagenomics long-read sequencing data analyzed with KMA for the detection of bacterial pathogens and their antimicrobial resistance genes.

Metagenomic sequencing is a promising method that has the potential to revolutionize the world of pathogen detection and antimicrobial resistance (AMR) surveillance in food-producing environments. However, the analysis of the huge amount of data obtained requires performant bioinformatics tools and databases, with intuitive and straightforward interpretation. In this study, based on long-read metagenomics data of chicken fecal samples with a spike-in mock community, we proposed confidence levels for taxonomic identification and AMR gene detection, with interpretation guidelines, to help with the analysis of the output data generated by KMA, a popular k-mer read alignment tool. Additionally, we demonstrated that the completeness and diversity of the genomes present in the reference databases are key parameters for accurate and easy interpretation of the sequencing data. Finally, we explored whether KMA, in a two-step procedure, can be used to link the detected AMR genes to their bacterial host chromosome, both detected within the same long-reads. The confidence levels were successfully tested on 28 metagenomics datasets which were obtained with sequencing of real and spiked samples from fecal (chicken, pig, and buffalo) or food (minced beef and food enzyme products) origin. The methodology proposed in this study will facilitate the analysis of metagenomics sequencing datasets for KMA users. Ultimately, this will contribute to improvements in the rapid diagnosis and surveillance of pathogens and AMR genes in food-producing environments, as prioritized by the EU.

Detection and diagnosis of bacterial pathogens in blood using laser-induced breakdown spectroscopy

The ability to rapidly and accurately detect and identify pathogenic bacteria in clinically-obtained blood specimens with laser-induced breakdown spectroscopy (LIBS) is evaluated. Samples of blood obtained from five patients in a local hospital were confirmed to be negative for the presence of bacteria by the pathology department and were then tested with LIBS. Specimens of blood were tested as obtained from the hospital with no other alteration as control samples and were also intentionally spiked with known aliquots of Escherichia coli, Staphylococcus aureus, Enterobacter cloacae, and Pseudomonas aeruginosa to simulate blood infections. LIBS spectra were acquired from blood deposited on nitrocellulose filters. The intensities of 15 emission lines measured in the spectra and 92 ratios of those line intensities were used as 107 independent variables in a partial least squares discriminant analysis (PLS-DA) to discriminate between sterile control samples and those spiked with bacteria. In addition, the entire LIBS spectrum from 200 nm – 590 nm was input into an artificial neural network analysis with principal component analysis pre-processing (PCA-ANN) to diagnose the bacterial species once detected.The PLS-DA test possessed a 96.3% sensitivity and a 98.6% specificity for the detection of pathogenic bacteria in blood when 776 spectra from 26 filters were tested by removing one entire filter at a time from the model and testing each spectrum individually. When all the spectra obtained from a filter were averaged to enhance the signal to noise of the spectrum, 19 of 19 filters of infected blood tested positive and 7 of 7 filters with sterile blood tested negative, yielding 100% sensitivity and 100% specificity. The PCA-ANN test performed on the entire LIBS spectrum possessed a 100% sensitivity and 100% specificity when using 80% of the data to build a model and withholding 20% for cross-validation testing. The same PCA-ANN performed on each of the 19 filters individually, using the other 18 filters to build the model, possessed an average sensitivity of 85.5%, an average specificity of 95.0%, and a classification accuracy of 92.5%. These results indicate the potential usefulness of LIBS for detecting and diagnosing blood infections in a clinical setting.

Real-time surveillance of zoonotic and pig pathogens using surface-enhanced Raman scattering system

The shift towards large-scale intensive pig farming methods has led to an increase in cases of complex, mixed infections, and secondary infections. Early diagnosis and disease prevention have become crucial for effective pig farm management. However, clinical diagnosis may be complicated by antibiotic treatment and atypical disease symptoms. To ensure accurate pathology diagnosis, it is essential to integrate robust laboratory diagnostics with traditional methods. Surface-enhanced Raman scattering (SERS) spectroscopy has emerged as a potentially powerful technique for whole-organism fingerprinting, enabling rapid identification of bacteria. Biosensors utilizing SERS offer promising capabilities for sensitive and quick detection of bacterial pathogens, thus reducing diagnosis time. In this study, we aimed to characterize and evaluate a SERS-based diagnostic system for detecting and identifying bacteria in specific pathogen free (SPF) mice, focusing on two bacterial zoonoses and swine bacteria present in pooled swine sera, feces, and meat. We compared the spectra of bacteria recovered from the specimens to those of pure cultured bacteria and conducted principal component analysis to determine the bacterial molecular fingerprint. Our results demonstrated successful detection, identification, and classification of these bacteria in mice specimens (sera and feces) and swine specimens (sera, feces, and meat) using SERS. SERS provided reproducible molecular spectroscopic signatures suitable for analytical applications. This approach presents a new and potent tool for real-time surveillance of animal bacterial pathogens in clinical settings.

Rapid Detection of Bacterial Pathogens Causing Lower Respiratory Tract Infections via Microfluidic-Chip-Based Loop-Mediated Isothermal Amplification.

Respiratory tract infections (RTIs) are among the most common problems in clinical settings. Rapid and accurate identification of bacterial pathogens will provide practical guidelines for managing and treating RTIs. This study describes a method for rapidly detecting bacterial pathogens that cause respiratory tract infections via multi-channel loop-mediated isothermal amplification (LAMP). LAMP is a sensitive and specific diagnostic tool that rapidly detects bacterial nucleic acids with high accuracy and reliability. The proposed method offers a significant advantage over traditional bacterial culturing methods, which are time-consuming and often require greater sensitivity for detecting low levels of bacterial nucleic acids. This article presents representative results of K. pneumoniae infection and its multiple co-infections using LAMP to detect samples (sputum, bronchial lavage fluid, and alveolar lavage fluid) from the lower respiratory tract. In summary, the multi-channel LAMP method provides a rapid and efficient means of identifying single and multiple bacterial pathogens in clinical samples, which can help prevent the spread of bacterial pathogens and aid in the appropriate treatment of RTIs.

Laboratory tools for the direct detection of bacterial respiratory infections and antimicrobial resistance: a scoping review.

Rapid laboratory tests are urgently required to inform antimicrobial use in food animals. Our objective was to synthesize knowledge on the direct application of long-read metagenomic sequencing to respiratory samples to detect bacterial pathogens and antimicrobial resistance genes (ARGs) compared to PCR, loop-mediated isothermal amplification, and recombinase polymerase amplification. Our scoping review protocol followed the Joanna Briggs Institute and PRISMA Scoping Review reporting guidelines. Included studies reported on the direct application of these methods to respiratory samples from animals or humans to detect bacterial pathogens ±ARGs and included turnaround time (TAT) and analytical sensitivity. We excluded studies not reporting these or that were focused exclusively on bioinformatics. We identified 5,636 unique articles from 5 databases. Two-reviewer screening excluded 3,964, 788, and 784 articles at 3 levels, leaving 100 articles (19 animal and 81 human), of which only 7 studied long-read sequencing (only 1 in animals). Thirty-two studies investigated ARGs (only one in animals). Reported TATs ranged from minutes to 2 d; steps did not always include sample collection to results, and analytical sensitivity varied by study. Our review reveals a knowledge gap in research for the direct detection of bacterial respiratory pathogens and ARGs in animals using long-read metagenomic sequencing. There is an opportunity to harness the rapid development in this space to detect multiple pathogens and ARGs on a single sequencing run. Long-read metagenomic sequencing tools show potential to address the urgent need for research into rapid tests to support antimicrobial stewardship in food animal production.