Methods For Detection Of Pathogens Research Articles

Sensitive and rapid identification of pathogens by droplet digital PCR in a cohort of septic patients: a prospective diagnostic study.

There is a critical need for a rapid and sensitive pathogen detection method for septic patients. This study aimed to investigate the diagnostic efficacy of Digital droplet polymerase chain reaction (ddPCR) in identifying pathogens among suspected septic patients. We conducted a prospective pilot diagnostic study to clinically validate the multiplex ddPCR panel in diagnosing suspected septic patients. A total of 100 sepsis episodes of 89 patients were included in the study. In comparison to blood culture, the ddPCR panel exhibited an overall sensitivity of 75.0% and a specificity of 69.7%, ddPCR yielded an additional detection rate of 17.0% for sepsis cases overall, with a turnaround time of 2.5 h. The sensitivity of ddPCR in the empirical antibiotic treatment and the non-empirical antibiotic treatment group were 78.6% versus 80.0% (p > 0.05). Antimicrobial resistance genes were identified in a total of 13 samples. Whenever ddPCR detected the genes beta-lactamase-Klebsiella pneumoniae carbapenemase (blaKPC) or beta-lactamase-New Delhi metallo (blaNDM), these findings corresponded to the cultivation of carbapenem-resistant gram-negative bacteria. Dynamic ddPCR monitoring revealed a consistent alignment between the quantitative ddPCR results and the trends observed in C-reactive protein and procalcitonin levels. Compared to blood culture, ddPCR exhibited higher sensitivity for pathogen diagnosis in suspected septic patients, and it provided pathogen and drug resistance information in a shorter time. The quantitative results of ddPCR generally aligned with the trends seen in C-reactive protein and procalcitonin levels, indicating that ddPCR can serve as a dynamic monitoring tool for pathogen load in septic patients.

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.

Functionalized carbon quantum dots fluorescent sensor array assisted by a machine learning algorithm for rapid foodborne pathogens identification

Food safety is a global concern, and conventional methods for detecting foodborne bacteria, such as polymerase chain reaction and enzyme-linked immunosorbent assay, often involve time-consuming processes, specialized equipment, or specific recognition of particular bacterial strains. There is an urgent need for more efficient and convenient detection methods for foodborne pathogens. This study addresses this need by introducing an easily constructed fluorescent sensor array for the identification of various foodborne bacteria. The sensor array comprises carbon quantum dots (CQDs) functionalized with ampicillin, polymyxin, and gentamicin, each exhibiting different affinities for binding with specific bacteria. Leveraging machine learning algorithms, the proposed sensor array enables rapid, accurate, and highly sensitive identification of foodborne pathogens. This approach offers a convenient solution for the development of rapid, accurate, and hypersensitive detection methods for multiple bioactive samples. In summary, the fluorescent sensor array in this study holds promise for advancing the field of foodborne bacteria detection.

Clinical application of metagenomic next-generation sequencing for the diagnosis of suspected infection in adults: A cross-sectional study.

Metagenomic next-generation sequencing (mNGS) has become an available method for pathogen detection. The clinical application of mNGS requires further evaluation. We conducted a cross-sectional study of 104 patients with suspected infection between May 2019 and May 2021. The risk factors associated with infection were analyzed using univariate logistic analysis. The diagnostic performance of pathogens was compared between mNGS and conventional microbiological tests. About 104 patients were assigned into 3 groups: infected group (n = 69), noninfected group (n = 20), and unknown group (n = 15). With the composite reference standard (combined results of all microbiological tests, radiological testing results, and a summary of the hospital stay of the patient) as the gold standard, the sensitivity, specificity, positive predictive value, negative predictive value of mNGS was 84.9%, 50.0%, 88.6%, and 42.1%, respectively. Compared with conventional microbiological tests, mNGS could detect more pathogens and had obvious advantages in Mycobacterium tuberculosis, Aspergillus, and virus detection. Moreover, mNGS had distinct benefits in detecting mixed infections. Bacteria-fungi-virus mixed infections were the most common in patients with severe pneumonia. mNGS had a higher sensitivity than conventional microbiological tests, especially for M. tuberculosis, Aspergillus, viruses, and mixed infections. We suggest that mNGS should be used more frequently in the early diagnosis of pathogens in critically ill patients in the future.

Cascaded signal amplification strategy for ultra-specific, ultra-sensitive, and visual detection of Shigella flexneri.

Pathogen infections including Shigella flexneri have posed a significant threat to human health for numerous years. Although culturing and qPCR were the gold standards for pathogen detection, time-consuming and instrument-dependent restrict their application in rapid diagnosis and economically less-developed regions. Thus, it is urgently needed to develop rapid, simple, sensitive, accurate, and low-cost detection methods for pathogen detection. In this study, an immunomagnetic beads-recombinase polymerase amplification-CRISPR/Cas12a (IMB-RPA-CRISPR/Cas12a) method was built based on a cascaded signal amplification strategy for ultra-specific, ultra-sensitive, and visual detection of S. flexneri in the laboratory. Firstly, S. flexneri was specifically captured and enriched by IMB (Shigella antibody-coated magnetic beads), and the genomic DNA was released and used as the template in the RPA reaction. Then, the RPA products were mixed with the pre-loaded CRISPR/Cas12a for fluorescence visualization. The results were observed by naked eyes under LED blue light, with a sensitivity of 5CFU/mL in a time of 70min. With no specialized equipment or complicated technical requirements, the IMB-RPA-CRISPR/Cas12a diagnostic method can be used for visual, rapid, and simple detection of S. flexneri and can be easily adapted to monitoring other pathogens.

Establishment and application of a quadruplex real-time RT-qPCR assay for differentiation of TGEV, PEDV, PDCoV, and PoRVA

Porcine viral diarrhea is a common ailment in clinical settings, causing significant economic losses to the swine industry. Notable culprits behind porcine viral diarrhea encompass transmissible gastroenteritis virus (TGEV), porcine epidemic diarrhea virus (PEDV), porcine deltacoronavirus (PDCoV), and porcine rotavirus-A (PoRVA). Co-infections involving the viruses are a common occurrence in clinical settings, thereby amplifying the complexities associated with differential diagnosis. As a consequence, it is therefore necessary to develop a method that can detect and differentiate all four porcine diarrhea viruses (TGEV, PEDV, PDCoV, and PoRVA) with a high sensitivity and specificity. Presently, polymerase chain reaction (PCR) is the go-to method for pathogen detection. In comparison to conventional PCR, TaqMan real-time PCR offers heightened sensitivity, superior specificity, and enhanced accuracy. This study aimed to develop a quadruplex real-time RT-qPCR assay, utilizing TaqMan probes, for the distinctive detection of TGEV, PEDV, PDCoV, and PoRVA. The quadruplex real-time RT-qPCR assay, as devised in this study, exhibited the capacity to avoid the detection of unrelated pathogens and demonstrated commendable specificity, sensitivity, repeatability, and reproducibility, boasting a limit of detection (LOD) of 27 copies/μL. In a comparative analysis involving 5483 clinical samples, the results from the commercial RT-qPCR kit and the quadruplex RT-qPCR for TGEV, PEDV, PDCoV, and PoRVA detection were entirely consistent. Following sample collection from October to March in Guangxi Zhuang Autonomous Region, we assessed the prevalence of TGEV, PEDV, PDCoV, and PoRVA in piglet diarrhea samples, revealing positive detection rates of 0.2 % (11/5483), 8.82 % (485/5483), 1.22 % (67/5483), and 4.94 % (271/5483), respectively. The co-infection rates of PEDV/PoRVA, PEDV/PDCoV, TGEV/PED/PoRVA, and PDCoV/PoRVA were 0.39 %, 0.11 %, 0.01 %, and 0.03 %, respectively, with no detection of other co-infections, as determined by the quadruplex real-time RT-qPCR. This research not only established a valuable tool for the simultaneous differentiation of TGEV, PEDV, PDCoV, and PoRVA in practical applications but also provided crucial insights into the prevalence of these viral pathogens causing diarrhea in Guangxi.

LAMP assay to detect Elsinoë necatrix; an important Eucalyptus shoot and leaf pathogen.

Eucalyptus scab and shoot malformation caused by Elsinoë necatrix is an emerging disease and a serious threat to the global commercial forestry industry. The disease was first discovered in North Sumatra, Indonesia and now requires a simple and effective method for early pathogen detection. In this study, a rapid and sensitive Loop-mediated isothermal amplification (LAMP) assay was developed for E. necatrix. A unique region in a secondary metabolite gene cluster was used as target for the assay. To test robustness of the assay, LAMP amplification was verified in 15 strains of E. necatrix. A specificity test against 23 closely related Elsinoë species and three fungal species commonly isolated on Eucalyptus showed that the LAMP assay exclusively identified E. necatrix isolates. The assay had a high level of sensitivity, able to detect 0.01 ng (approximately 400 target copies) of pure E. necatrix DNA. Furthermore, using a simple DNA extraction method, it was possible to use this assay to detect E. necatrix in infected Eucalyptus leaves.

One-pot dual-CRISPR/Cas12b-mediated dUTP-LAMP for rapid and sensitive detection of foodborne pathogens without carryover contamination

Rapid and accurate detection of foodborne pathogens is of great significance for ensuring food safety. Currently, the CRISPR-Dx based on Cas12b, a new and better performing Cas enzyme, is very rare, and the vast majority require a two-step method, facing aerosol contamination. In this work, we reported a one-pot dual-CRISPR/Cas12b mediated dUTP-LAMP method for rapid and sensitive detection of foodborne pathogens without nucleic acid extraction and contamination-free. The design mechanism of sgRNAs in the one-pot CRISPR/Cas12b-mediated LAMP method was investigated in detail for the first time. Moreover, a novel PAM sequence UUN of AapCas12b was discovered, and combined with dUTP-LAMP system for one-pot dual-sgRNA detection without carryover contamination. Our developed method can achieve high specificity, nucleic acid extraction-free detection of 2.33 × 104 CFU/mL Salmonella within 1 h. Furthermore, accurate detection of Salmonella was successfully performed in real samples of milk, fruit and egg, demonstrating the practical significance of the developed method.

Recent advances in the biosensors application for reviving infectious disease management in silkworm model: a new way to combat microbial pathogens.

Silkworms are an essential economic insect but are susceptible to diseases during rearing, leading to yearly losses in cocoon production. While chemical control is currently the primary method to reduce disease incidences, its frequent use can result in loss of susceptibility to pathogens and, ultimately, antibiotic resistance. To effectively prevent or control disease, growers must accurately, sensitively, and quickly detect causal pathogens to determine the best management strategies. Accurate recognition of diseased silkworms can prevent pathogen transmission and reduce cocoon loss. Different pathogen detection methods have been developed to achieve this objective, but they need more precision, specificity, consistency, and promptness and are generally unsuitable for in-situ analysis. Therefore, detecting silkworm diseases under rearing conditions is still an unsolved problem. As a consequence of this, there is an enormous interest in the development of biosensing systems for the early and precise identification of pathogens. There is also significant room for improvement in translating novel biosensor techniques to identify silkworm pathogens. This study explores the types of silkworm diseases, their symptoms, and their causal microorganisms. Moreover, we compare the traditional approaches used in silkworm disease diagnostics along with the latest sensing technologies, with a precise emphasis on lateral flow assay-based biosensors that can detect and manage silkworm pathogens.

Comparison of metagenomic next-generation sequencing and conventional culture for the diagnostic performance in febrile patients with suspected infections

BackgroundTimely and accurate identification of pathogens is crucial for appropriate treatment and prognosis of infectious diseases. As an increasingly popular pathogen detection method, the performance of metagenomic next-generation sequencing (mNGS) in detecting pathogens in febrile patients with suspected infection requires further exploration.MethodsThis study included 368 febrile patients with suspected infections who were admitted to the Infectious Disease Department of Qilu Hospital, Shandong University between January 5, 2021 and April 14, 2023. Both mNGS testing and conventional culture were performed in all patients. Clinical data of enrolled patients were collected, and the diagnostic performances of mNGS and culture were compared.ResultsOf the 368 enrolled patients, 231 were finally diagnosed with infection and 137 were with diseases other than infection. The sensitivity (58.01% vs. 21.65%, p < 0.001) and negative predictive value (54.67% vs. 42.9%) of mNGS were superior to those of culture. In contrast, the culture exhibited higher specificity (99.27% vs. 85.40%, p < 0.001) and positive predictive value (98.84% vs. 87.01%) than mNGS. Among infected patients with positive mNGS results, 64 received adjusted antibiotic therapy including treatment transitions, antibiotic downgrading, and combination therapy. Among them, 9 had additional antifungal drugs and 21 patients had a treatment turning point based on the mNGS results and these patients recovered and discharged due to timely antibiotic adjustment. Both positive rates of puncture fluid mNGS and tissue mNGS were higher than those of culture in the patients who had prior antibiotic use, and this difference was statistically significant (p = 0.000).ConclusionmNGS is more sensitive and accurate than traditional culture, making it ideal for identifying pathogens and screening infectious diseases, especially for those with uncultivated or difficult-to-cultivate species. Early diagnosis allows for prompt treatment with targeted antibiotics, and mNGS is recommended when samples are limited.

Development of an Immunocapture-Based Polymeric Optical Fiber Sensor for Bacterial Detection in Water.

Conventional methods for pathogen detection in water rely on time-consuming enrichment steps followed by biochemical identification strategies, which require assay times ranging from 24 hours to a week. However, in recent years, significant efforts have been made to develop biosensing technologies enabling rapid and close-to-real-time detection of waterborne pathogens. In previous studies, we developed a plastic optical fiber (POF) immunosensor using an optoelectronic configuration consisting of a U-Shape probe connected to an LED and a photodetector. Bacterial detection was evaluated with the immunosensor immersed in a bacterial suspension in water with a known concentration. Here, we report on the sensitivity of a new optoelectronic configuration consisting of two POF U-shaped probes, one as the reference and the other as the immunosensor, for the detection of Escherichia coli. In addition, another methos of detection was tested where the sensors were calibrated in the air, before being immersed in a bacterial suspension and then read in the air. This modification improved sensor sensitivity and resulted in a faster detection time. After the immunocapture, the sensors were DAPI-stained and submitted to confocal microscopy. The histograms obtained confirmed that the responses of the immunosensors were due to the bacteria. This new sensor detected the presence of E. coli at 104 CFU/mL in less than 20 min. Currently, sub-20 min is faster than previous studies using fiber-optic based biosensors. We report on an inexpensive and faster detection technology when compared with conventional methods.

Development of a Bioaerosol Sampling Method for Airborne Pathogen Detection with Focus on SARS-CoV-2

As worldwide evidence shows that the predominant transmission route of SARS-CoV-2 and other respiratory pathogens is airborne, the need for suitable methods for the sampling of bioparticles directly from the air is more urgent than ever. The present paper describes the development of a method for the collection of biological aerosols, using a preexisting cyclonic impinger, the Coriolis μ, combined with a lysis buffer and subsequent qPCR analysis of the generated samples in lab. Four phases of method development are described: exploratory, validation, blank tests, and application. The application phase consisted of a field experiment in which the method was simultaneously applied at two daycare facilities. The method achieved a good level of accuracy and reliability in detecting different types of infectious agents in the air, with a global uncertainty of 19.6%. Furthermore, our method allows the simultaneous detection of 26 different respiratory pathogens in air samples, it is relatively simple, and the equipment is easy to use. Additionally, the time to collect a representative sample is short compared to other methods. The method does not cause significant disturbance to those present in the sampled rooms, and it is safe for operators and flexible, meaning it can be used in virtually any environment regardless of use, size, or occupancy. Further research is being developed to allow quantitative analysis of the collected samples and to test the methods’ ability to assess the viability of the microorganisms collected in the sample.

Development of a 3D Microfluidic Analytical Device for the Detection of Pathogenic Bacteria in Commercial Food Samples with Loop-Mediated Isothermal Amplification

Traditional methods of detecting foodborne pathogens take several days to produce the required results. Furthermore, various molecular techniques (e.g., PCR) that also produce reliable results in the detection of pathogenic bacteria have been introduced, but the cost–time ratio required does not allow them to be considered a substantial solution to this specific problem. Three-dimensional (3D) printing technology provides the ability to design and manufacture microfluidic analytical devices using conventional 3D printers, which, in combination with colorimetric loop-mediated isothermal amplification (LAMP), may further simplify the process. The overall reduction in time and cost may provide the opportunity to upscale this diagnostic modality. Moreover, unlike most microfluidic analytical devices, this technique is simpler and more user-friendly, as it does not require any expertise or additional equipment apart from a conventional oven. A 3D-printed microfluidic analytical device in combination with LAMP was developed and tested for the simultaneous detection of foodborne pathogens in food samples. A total of 150 commercial food specimens (50 milk, 50 chicken, 50 lettuce samples) were analyzed for possible contamination with Salmonella typhimurium, Listeria monocytogenes and Escherichia coli. The 3D-printed microfluidic device was 100% precise for both negative (80 samples) and positive samples (7 samples were positive for S. typhimurium, 28 for L. monocytogenes, and 35 for E. coli) for all pathogens. Overall, the amount of data analyzed led to a high level of confidence in the precision of this device. As such, this new 3D device in combination with LAMP provides a precise detection method for food pathogens with a low detection limit.

A novel method for simultaneous detection of hematological tumors and infectious pathogens by metagenomic next generation sequencing of plasma

BackgroundMetagenomic next-generation sequencing (mNGS) is valuable for pathogen identification; however, distinguishing between infectious diseases and conditions with potentially similar clinical manifestations, including malignant tumors, is challenging. Therefore, we developed a method for simultaneous detection of infectious pathogens and cancer in blood samples. MethodsPlasma samples (n = 244) were collected from 150 and 94 patients with infections and hematological malignancies, respectively, and analyzed by mNGS for pathogen detection, alongside human tumor chromosomal copy number variation (CNV) analysis (≥5Mbp or 10Mbp CNV region). Further, an evaluation set, comprising 87 plasma samples, was analyzed by mNGS and human CNV analysis, to validate the feasibility of the method. ResultsAmong 94 patients with hematological malignancy, sensitivity values of CNV detection for tumor diagnosis were 69.15 % and 32.98 % for CNV region 5Mbp and 10Mbp, respectively, with corresponding specificities of 92.62 % and 100 % in the infection group. Area under the ROC curve (AUC) values for 5Mbp and 10Mbp region were 0.825 and 0.665, respectively, which was a significant difference of 0.160 (95 % CI: 0.110–0.210; p < 0.001), highlighting the superiority of 5Mbp output region data. Six patients with high-risk CNV results were identified in the validation study: three with history of tumor treatment, two eventually newly-diagnosed with hematological malignancies, and one with indeterminate final diagnosis. ConclusionsConcurrent CNV analysis alongside mNGS for infection diagnosis is promising for detecting malignant tumors. We recommend adopting a CNV region of 10Mbp over 5Mbp for our model, because of the lower false-positive rate (FPR).

Label‐free surface‐enhanced Raman spectroscopy coupled with machine learning algorithms in pathogenic microbial identification: Current trends, challenges, and perspectives

AbstractInfectious diseases caused by microbial pathogens remain a primary contributor to global health burdens. Prompt control and effective prevention of these pathogens are critical for public health and medical diagnostics. Conventional microbial detection methods suffer from high complexity, low sensitivity, and poor selectivity. Therefore, developing rapid and reliable methods for microbial pathogen detection has become imperative. Surface‐enhanced Raman Spectroscopy (SERS), as an innovative non‐invasive diagnostic technique, holds significant promise in pathogenic microorganism detection due to its rapid, reliable, and cost‐effective advantages. This review comprehensively outlines the fundamental theories of Raman Spectroscopy (RS) with a focus on label‐free SERS strategy, reporting on the latest advancements of SERS technique in detecting bacteria, viruses, and fungi in clinical settings. Furthermore, we emphasize the application of machine learning algorithms in SERS spectral analysis. Finally, challenges faced by SERS application are probed, and the prospective development is discussed.

Comparison of Three Preharvest Sampling Strategies to Monitor Pathogens in Cattle Lairage Areas

The objective of this study was to compare preharvest monitoring strategies by evaluating three different sampling methods in the lairage area to determine pathogen recovery for each sampling method and incoming pathogen prevalence from the cattle to inform in-plant decision making. Samples were gathered over a 5-month period, from February to June 2022, at a harvesting and processing facility located in Eastern Nebraska. Sampling methods included (i) fecal pats, (ii) boot swabs, and (iii) MicroTally swab. A total of 329 samples were collected over the study period (fecal pats: n = 105, boot swabs: n = 104, and MicroTally swabs: n = 120). Specific media combinations, an incubation temperature of 42°C, and incubation timepoints (18–24 h) were utilized for each matrix and the prevalence of Salmonella, Escherichia coli O157:H7, and six non-O157 Shiga-toxin producing E. coli (STEC) was evaluated using the BAX system Real-Time PCR assay. Overall, results from the study concluded that boot swabs were an effective sampling method for pathogen detection in the cattle lairage area. Boot swabs (97.1%) were statistically more likely to detect for Salmonella (p < 0.05) when compared to fecal pats (67.6%) and MicroTally swab (77.5%) methods. For E. coli O157:H7 and STEC – O26, O121, O45, and O103 prevalence, boot swabs were significantly better at detecting for these pathogens (p < 0.05) than MicroTally swabs (OR = 3.16 – 11.95) and a comparable sampling method to fecal pats (OR = 0.93 – 2.01, p > 0.05). Lastly, all three sampling methods detected a very low prevalence for E. coli O111 and O145; therefore, no further analysis was conducted. The boot swab sampling method was strongly favored because they require little training to implement, are inexpensive, and they do not require much sampling labor; therefore, would be a simple and effective sampling method to implement within the industry to evaluate pathogen prevalence preharvest.

Bridging Flocculation of a Sterically Stabilized Cationic Latex as a Biosensor for the Detection of Microbial DNA after Amplification via PCR.

There is a high demand for rapid, sensitive, and accurate detection methods for pathogens. This paper demonstrates a method of detecting the presence of amplified DNA from a range of pathogens associated with serious infections including Gram-negative bacteria, Gram-positive bacteria, and viruses. DNA is amplified using a polymerase chain reaction (PCR) and consequently detected using a sterically stabilized, cationic polymer latex. The DNA induces flocculation of this cationic latex, which consequently leads to rapid sedimentation and a visible change from a milky-white dispersion to one with a transparent supernatant, presenting a clear visible change, indicating the presence of amplified DNA. Specifically, a number of different pathogens were amplified using conventional or qPCR, including Staphylococcus aureus, Escherichia coli, and Herpes Simplex Virus (HSV-2). This method was demonstrated to detect the presence of bacteria in suspension concentrations greater than 380 CFU mL-1 and diagnose the presence of specific genomes through primer selection, as exemplified using methicillin resistant and methicillin susceptible Staphylococcus aureus. The versatility of this methodology was further demonstrated by showing that false positive results do not occur when a PCR of fungal DNA from C. albicans is conducted using bacterial universal primers.

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.

Directly Self-Assembly of Aligned Ag NWs Films at the Air-Water Interface for the Detection of Pathogens in Artificial Breath Aerosols.

Exhaled aerosols from humans, containing various pathogens, are crucial for early disease diagnosis. However, the traditional pathogen detection methods, such as polymerase chain reaction, are often slow and cumbersome due to complex sampling and procedures. This study introduces a novel, direct, and label-free detection method for pathogens in respiratory aerosols, utilizing a highly aligned silver nanowire (Ag NW) film combined with a filter membrane (Ag NWs@filter) as a surface-enhanced Raman spectroscopy-active substrate. A large-scale, ordered silver nanowire film was developed through a simplified self-assembly process. This process eliminates the need for an organic phase and complex surface modifications of Ag NWs, which are common in other preparation methods. Subsequently, the fabricated Ag NWs@filter demonstrated its capability to continuously capture and efficiently preconcentrate pathogens from aerosols, achieving a remarkable detection limit of 3 × 103 CFU/mL, demonstrated using Escherichia coli (E. coli) as a model pathogen. Moreover, the classification between E. coli and Pseudomonas aeruginosa achieved an overall accuracy of 96.5% by the principal component analysis with linear discriminant analysis models. The success of this sensing strategy illustrates its potential in detecting and identifying a variety of biomarkers present in respiratory aerosols, marking a significant step forward in the field of pathogen detection.

A machine vision-assisted Argonaute-mediated fluorescence biosensor for the detection of viable Salmonella in food without convoluted DNA extraction and amplification procedures

The precise identification viable pathogens hold paramount significance in the prevention of foodborne diseases outbreaks. In this study, we integrated machine vision and learning with single microsphere to develop a phage and Clostridium butyricum Argonaute (CbAgo)-mediated fluorescence biosensor for detecting viable Salmonella typhimurium (S. typhimurium) without convoluted DNA extraction and amplification procedures. Phage and lysis buffer was utilized to capture and lyse viable S. typhimurium, respectively. Subsequently, CbAgo can cleave the bacterial DNA to obtain target DNA that guides a newly targeted cleavage of fluorescent probes. After that, the resulting fluorescent signal accumulates on the streptavidin-modified single microsphere. The overall detection process is then analyzed and interpreted by machine vision and learning algorithms, achieving highly sensitive detection of S. typhimurium with a limit of detection at 40.5 CFU/mL and a linear range of 50–107 CFU/mL. Furthermore, the proposed biosensor demonstrates standard recovery rates and coefficients of variation at 93.22% − 106.02% and 1.47% − 12.75%, respectively. This biosensor exhibits exceptional sensitivity and selectivity, presenting a promising method for the rapid and effective detection of foodborne pathogens. Environmental ImplicationBacterial pathogens exist widely in the environment and seriously threaten the safety of human life. In this study, we developed a phage and Clostridium butyricum Argonaute-mediated fluorescence biosensor for the detection of viable Salmonella typhimurium in environmental water and food samples. Compared with other Salmonella detection methods, this method does not need complex DNA extraction and amplification steps, which reduces the use of chemical reagents and experimental consumables in classic DNA extraction kit methods.