Detection Of Bacterial Pathogens Research Articles

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.

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.

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.

A threshold of 100 or more colony-forming units on the anesthesia machine predicts bacterial pathogen detection: a retrospective laboratory-based analysis.

Preventing the spread of pathogens in the anesthesia work area reduces surgical site infections. Improved cleaning reduces the percentage of anesthesia machine samples with ≥ 100 colony-forming units (CFU) per surface area sampled. Targeting a threshold of < 100CFU when cleaning anesthesia machines may be associated with a lower prevalence of bacterial pathogens. We hypothesized that anesthesia work area reservoir samples returning < 100CFU would have a low (< 5%) prevalence ofpathogens. In this retrospective cohort study of bacterial count data from nine hospitals, obtained between 2017 and 2022, anesthesia attending and assistants' hands, patient skin sites (nares, axilla, and groin), and anesthesia machine (adjustable pressure-limiting valve and agent dials) reservoirs were sampled at case start and at case end. The patient intravenous stopcock set was sampled at case end. The isolation of ≥ 1CFU of Staphylococcus aureus, methicillin-resistant Staphylococcusaureus, Enterococcus, vancomycin-resistant Enterococcus, gram-negative (i.e., Klebsiella, Acinetobacter, Pseudomonas, and Enterobacter spp.) or coagulase-negative Staphylococcus was compared for reservoir samples returning ≥ 100CFU vs those returning < 100CFU. Bacterial pathogens were isolated from 24% (7,601/31,783) of reservoir samples, 93% (98/105) of operating rooms, and 83% (2,170/2,616) of cases. The ratio of total pathogen isolates to total CFU was < 0.0003%. Anesthesia machine reservoirs returned ≥ 100CFU for 44% (2,262/5,150) of cases. Twenty-three percent of samples returning ≥ 100CFU were positive for ≥ 1 bacterial pathogen (521/2,262; 99% lower confidence limit, 22%) vs 3% of samples returning < 100CFU (96/2,888; 99% upper limit, 4%). Anesthesia machine reservoir samples returning < 100CFU were associated with negligible pathogen detection. This threshold can be used for assessment of terminal, routine, and between-case cleaning of the anesthesia machine and equipment. Such feedback may be useful because the 44% prevalence of ≥ 100CFU was comparable to the 46% (25/54) reported earlier from an unrelated hospital.

Microscopic identification of foodborne bacterial pathogens based on deep learning method

Accurate and rapid detection of foodborne bacterial pathogens is critical for food quality control. Nowadays, tracking morphological bacterial properties using microscope is still a priority at the grass-roots food supervision department due to its simplicity and low cost. However, the method requires highly professional personnel and there are certain misjudgments in the process of analysis. Automatically recognizing foodborne pathogen using deep learning algorithm to replace manual microscopy will not only reduce expert cost, artificial misjudgment, and operation time in detection, but also provide more objective and accurate identification. Here, we firstly constructed a high-quality and large-scale dataset of foodborne pathogenic bacteria, allowing the deep learning-based model to be efficiently trained and achieve accurate identification. The deep convolutional neural network-based model is capable of identifying six common foodborne pathogens, including Escherichia coli (O157:H7), Vibrio parahaemolyticus, Staphylococcus aureus, Bacillus cereus, Salmonella typhi, and Streptococcus hemolyticus, with accuracy rates of 90%–100%. This method can assist or replace the manual microscopic inspection step in traditional detection methods, and is promising to break through the traditional approach that heavily relies on manual judgment, greatly reduce the cost of experts and human errors, and provide rapid, accurate, and powerful discriminatory results in large quantities for detection.

A CRISPR/LbCas12a-based method for detection of bacterial fruit blotch pathogens in watermelon.

Acidovorax citrulli is the main pathogen causing bacterial fruit blotch, which seriously threatens the global watermelon industry. At present, rapid, sensitive, and low-cost detection methods are urgently needed. The established CRISPR/LbCas12a visual detection method can specifically detect A. citrulli and does not cross-react with other pathogenic bacteria such as Erwinia tracheiphila, Pseudomonas syringae, and Xanthomonas campestris. The sensitivity of this method for genomic DNA detection is as low as 0.7 copies/μL, which is higher than conventional PCR and real-time PCR. In addition, this method only takes 2.5 h from DNA extraction to quantitative detection and does not require complex operation and sample treatment. Additionally, the technique was applied to test real watermelon seed samples for A. citrulli, and the results were contrasted with those of real-time fluorescence quantitative PCR and conventional PCR. The high sensitivity and specificity have broad application prospects in the rapid detection of bacterial fruit blotch bacterial pathogens of watermelon.IMPORTANCEBacterial fruit blotch, Acidovorax citrulli, is an important seed-borne bacterial disease of watermelon, melon, and other cucurbits. The lack of rapid, sensitive, and reliable pathogen detection methods has hampered research on fruit spot disease prevention and control. Here, we demonstrate the CRISPR/Cas12a system to analyze aspects of the specificity and sensitivity of A. citrulli and to test actual watermelon seed samples. The results showed that the CRISPR/Cas12a-based free-amplification method for detecting bacterial fruit blotch pathogens of watermelons was specific for A. citrulli target genes and 100-fold more sensitive than conventional PCR with quantitative real-time PCR. This method provides a new technical tool for the detection of A. citrulli.

Next Generation Sequencing, and Development of a Pipeline as a Tool for the Detection and Discovery of Citrus Pathogens to Facilitate Safer Germplasm Exchange

Citrus is affected by many diseases, and hence, the movement of citrus propagative materials is highly regulated in the USA. Currently used regulatory pathogen detection methods include biological and laboratory-based technologies, which are time-consuming, expensive, and have many limitations. There is an urgent need to develop alternate, rapid, economical, and reliable testing methods for safe germplasm exchange. Citrus huanglongbing (HLB) has devastated citrus industries leading to an increased need for germplasm exchanges between citrus growing regions for evaluating many potentially valuable hybrids for both HLB resistance and multilocational performance. In the present study, Next-Generation Sequencing (NGS) methods were used to sequence the transcriptomes of 21 test samples, including 15 well-characterized pathogen-positive plants. A workflow was designed in the CLC Genomics Workbench software, v 21.0.5 for bioinformatics analysis of the sequence data for the detection of pathogens. NGS was rapid and found to be a valuable technique for the detection of viral and bacterial pathogens, and for the discovery of new citrus viruses, complementary to the existing array of biological and laboratory assays. Using NGS methods, we detected beet western yellows virus, a newly reported citrus virus, and a variant of the citrus yellow vein-associated virus associated with the “fatal yellows” disease.

A molecular toolbox for fast and convenient diagnosis of emerging and reemerging bacterial pathogens causing fever of intermediate duration

PurposeFever of intermediate duration (FID) is defined as a fever in the community without a specific origin or focus, with a duration between 7 and 28 days. FID is often caused by pathogens associated with animal contact or their arthropods parasites, such as ticks, fleas, or lice. The purpose of this work is to design a collection of molecular tools to promptly and accurately detect common bacterial pathogens causing FID, including bacteria belonging to genera Rickettsia, Bartonella, Anaplasma, and Ehrlichia, as well as Coxiella burnetii.MethodsReference DNA sequences from a collection of Rickettsia, Bartonella, Anaplasma, and Ehrlichia species were used to design genus-specific primers and FRET probes targeted to conserved genomic regions. For C. burnetii, primers previously described were used, in combination with a newly designed specific probe. Real-time PCR assays were optimized using reference bacterial genomic DNA in a background of human genomic DNA.ResultsThe four real-time PCR assays can detect as few as ten copies of target DNA from those five genera of FDI-causing bacteria in a background of 300 ng of human genomic DNA, mimicking the low microbial load generally found in patient’s blood.ConclusionThese assays constitute a fast and convenient “toolbox” that can be easily implemented in diagnostic laboratories to provide timely and accurate detection of bacterial pathogens that are typical etiological causes of febrile syndromes such as FID in humans.

Clinical performance evaluation of TAQPATH Enteric Bacterial Select Panel for the detection of common enteric bacterial pathogens in comparison to routine stool culture and other qPCR-based diagnostic tests.

Enteric bacterial infections caused by Salmonella, Shigella, pathogenic Escherichia coli, and Campylobacter represent one of the most common causes of infectious enteritis worldwide. The timely and accurate diagnosis of pathogens causing gastroenteritis is crucial for patient care, public health, and disease surveillance. While stool culture has long been the standard and highly specific method for detecting enteric pathogens, it is labor-intensive and time-consuming with limited sensitivity. To improve patient outcomes, there is a need to implement new cost-effective approaches for the detection of bacterial enteric pathogens with higher sensitivity and faster time to result. This study shows that multiplex real-time polymerase chain reaction-based tests, such as the TAQPATH Enteric Bacterial Select Panel, are accurate and cost-effective diagnostic alternatives for the detection and differentiation of the most common enteric bacterial pathogens, offering quicker time to result and higher sensitivity compared to routine stool culture.

Sulfhydryl-mediated etching suppression of gold nanostars for rapid and sensitive detection of bacterial pathogens

Human infectious diseases caused by bacterial pathogens, such as meningitis and pyaemia, threaten the public health worldwide due to their acute onset and high mortality. Sensitive and rapid detection of bacterial pathogens is urgent for the treatment of infectious diseases clinically. However, current techniques are generally time-consuming and require large-scale equipment. Plasmonic nanosensors built on metallic nanomaterials have emerged as excellent tools for biomedical diagnostics. Herein, we report an etching suppression strategy for the sensitive and rapid detection of bacterial pathogens through gold nanostars. Sulfhydryl-containing ligands suppress the iodide-mediated surface etching of gold nanostars, depending on the type, concentration, and size of sulfhydryl-containing ligands. Using mercaptophenylboronic acid (MBA) and Pseudomonas aeruginosa (P. aeruginosa)-specific aptamer as multifunctional ligands, we specifically identify bacterial pathogens through sulfhydryl-mediated etching suppression with a linear detection range from 10 to 106 CFU/mL and a limit of detection of 2 CFU/mL. This etching suppression-based strategy enables the detection of Neisseria meningitidis in the cerebrospinal fluid and P. aeruginosa in the serum within 15 min, showing great potential as a point-of-care platform for clinical diagnosis.

Trends in Development of Aptamer-Based Biosensor Technology for Detection of Bacteria.

The contamination of food by bacterial pathogens represents a substantial hazard for human and animal health. Therefore, considerable effort is focused on the development of effective methods for monitoring food safety. A current trend in this field is the development of biosensors that can be used in remote food laboratories and even in farms to check food contamination prior to its delivery to consumers or its further processing in the food industry. Among receptors that can recognize proteins or lipopolysaccharides (LPS) on bacterial surfaces, aptamers play an important role. An aptamer consists of a single strand of DNA or RNA that folds into a 3D structure when placed in a solution, forming a binding site for the target. This chapter presents an overview of recent achievements in bacterial pathogen detection through the development of electrochemical, optical, and acoustic biosensors based on DNA aptamers. Thus far, these biosensors exhibit good sensitivity and selectivity, comparable with conventional methods currently used in food laboratories. However, these biosensors offer several advantages over conventional methods: they are of low cost, easier to handle, and respond more quickly. Biosensor technology is therefore an important tool for monitoring food safety.