Correlation of SARS-CoV-2 in Wastewater and Individual Testing Results in a Jail, Atlanta, Georgia, USA.

Institution-level wastewater-based surveillance was implemented during the COVID-19 pandemic, including in carceral facilities. We examined the relationship between COVID-19 diagnostic test results of residents in a jail in Atlanta, Georgia, USA (average population ≈2,700), and quantitative reverse transcription PCR signal for SARS-CoV-2 in weekly wastewater samples collected during October 2021‒May 2022. The jail offered residents rapid antigen testing at entry and periodic mass screenings by reverse transcription PCR of self-collected nasal swab specimens. We aggregated individual test data, calculated the Spearman correlation coefficient, and performed logistic regression to examine the relationship between strength of SARS-CoV-2 PCR signal (cycle threshold value) in wastewater and percentage of jail population that tested positive for COVID-19. Of 13,745 nasal specimens collected, 3.9% were COVID-positive (range 0%-29.5% per week). We observed a strong inverse correlation between diagnostic test positivity and cycle threshold value (r = -0.67; p<0.01). Wastewater-based surveillance represents an effective strategy for jailwide surveillance of COVID-19.

Multisource information fusion method for vegetable disease detection.

Automated detection and identification of vegetable diseases can enhance vegetable quality and increase profits. Images of greenhouse-grown vegetable diseases often feature complex backgrounds, a diverse array of diseases, and subtle symptomatic differences. Previous studies have grappled with accurately pinpointing lesion positions and quantifying infection degrees, resulting in overall low recognition rates. To tackle the challenges posed by insufficient validation datasets and low detection and recognition rates, this study capitalizes on the geographical advantage of Shouguang, renowned as the "Vegetable Town," to establish a self-built vegetable base for data collection and validation experiments. Concentrating on a broad spectrum of fruit and vegetable crops afflicted with various diseases, we conducted on-site collection of greenhouse disease images, compiled a large-scale dataset, and introduced the Space-Time Fusion Attention Network (STFAN). STFAN integrates multi-source information on vegetable disease occurrences, bolstering the model's resilience. Additionally, we proposed the Multilayer Encoder-Decoder Feature Fusion Network (MEDFFN) to counteract feature disappearance in deep convolutional blocks, complemented by the Boundary Structure Loss function to guide the model in acquiring more detailed and accurate boundary information. By devising a detection and recognition model that extracts high-resolution feature representations from multiple sources, precise disease detection and identification were achieved. This study offers technical backing for the holistic prevention and control of vegetable diseases, thereby advancing smart agriculture. Results indicate that, on our self-built VDGE dataset, compared to YOLOv7-tiny, YOLOv8n, and YOLOv9, the proposed model (Multisource Information Fusion Method for Vegetable Disease Detection, MIFV) has improved mAP by 3.43%, 3.02%, and 2.15%, respectively, showcasing significant performance advantages. The MIFV model parameters stand at 39.07 M, with a computational complexity of 108.92 GFLOPS, highlighting outstanding real-time performance and detection accuracy compared to mainstream algorithms. This research suggests that the proposed MIFV model can swiftly and accurately detect and identify vegetable diseases in greenhouse environments at a reduced cost.

Successful revascularization of coronary occlusion due to lotus root-like plaques by IVUS-based real-time three-dimensional wiring using the tip detection method in a patient with cardiac arrest.

A 32-year-old man, who was treated for T-cell lymphoma, presented in cardiac arrest. He had been treated for heart failure with reduced ejection fraction. Veno-arterial extracorporeal membrane oxygenation was initiated immediately. We diagnosed him as non-ST elevated myocardial infarction. Coronary angiography demonstrated the occlusion of the trifurcation in the proximal left anterior descending artery (LAD). We failed to advance the first guidewire into the distal LAD by angio-based conventional wiring. Intravascular ultrasonography (IVUS) of the proximal diagonal branch revealed two diaphragms separating the distal lumen without connection, which looks like lotus root-like appearance. We quickly penetrated the plaque using IVUS-based real-time 3D wiring using the tip detection method. The contrast injection via the microcatheter showed the distal diagonal branch (D2). After the balloon dilation in D2, IVUS image revealed a torn plaque between D2 and the distal LAD. Subsequently we advanced the guidewire to the distal LAD using IVUS-based real-time 3D wiring using the tip detection method through the tear of the plaque. Finally, we successfully performed the revascularization of LAD in a preferable procedure time. The patient recovered well and was discharged 39 days after cardiac arrest. This case highlights the efficacy of IVUS-based real-time 3D wiring using the tip detection method even in the emergent and challenging situation.

Adverse drug reaction signal detection methods in spontaneous reporting system: A systematic review.

BACKGROUND: A series of signal detection methods have been developed to detect adverse drug reaction (ADR) signals in spontaneous reporting system. However, different signal detection methods yield quite different signal detection results, and we do not know which method has the best detection performance. How to choose the most suitable signal detection method is an urgent problem to be solved. In this study, we systematically reviewed the characteristics and application scopes of current signal detection methods, with the goal of providing references for the optimization selection of signal detection methods in spontaneous reporting system. METHODS: We searched six databases from inception to January 2023. The search strategy targeted literatures regarding signal detection methods in spontaneous reporting system. We used thematic analysis approach to summarize the advantages, disadvantages, and application scope of each signal detection method. RESULTS: A total of 93 literatures were included, including 27 reviews and 66 methodological studies. Moreover, 31 signal detection methods were identified in these literatures. Each signal detection method has its inherent advantages and disadvantages, resulting in different application scopes of these methods. CONCLUSION: Our systematic review finds that there are variabilities in the advantages, disadvantages, and application scopes of different signal detection methods. This finding indicates that the most suitable signal detection method varies across different drug safety scenarios. Moreover, when selecting signal detection method in a particular drug safety scenario, the following factors need to be considered: purpose of research, database size, drug characteristics, adverse event characteristics, and characteristics of the relations between drugs and adverse events.

A simplified molecular tool for detecting the Chagas etiological agent using a vector feces sample in field conditions.

Triatomine bugs are vectors of Trypanosoma cruzi, the etiologic agent of Chagas disease in the American continent. Here, we have tested a loop-mediated isothermal amplification (LAMP) test for a direct detection of T. cruzi in feces of Triatoma infestans, the main vector of this parasite in the Southern Cone of America. The analytical evaluation showed positive results with samples of triatomine feces artificially inoculated with DNA from strains of T. cruzi corresponding to each Discrete Typing Units (I-VI), with a sensitivity of up to one parasite per reaction. Conversely, the reaction yielded negative results when tested with DNA from Trypanosoma rangeli and other phylogenetically related and unrelated organisms. In triatomines captured under real field conditions (from urban households), and defined as positive or negative for T. cruzi using the reference microscopy technique, the LAMP test achieved a concordance of 100 %. Our results demonstrate that this LAMP reaction exhibits excellent analytical specificity and sensitivity without interference from the fecal matrix, since all the reactions were conducted without purification steps. This simple molecular diagnostic technique can be easily used by vector control agencies under field conditions.

Research on the Method of Foreign Object Detection for Railway Tracks Based on Deep Learning.

Addressing the limitations of current railway track foreign object detection techniques, which suffer from inadequate real-time performance and diminished accuracy in detecting small objects, this paper introduces an innovative vision-based perception methodology harnessing the power of deep learning. Central to this approach is the construction of a railway boundary model utilizing a sophisticated track detection method, along with an enhanced UNet semantic segmentation network to achieve autonomous segmentation of diverse track categories. By employing equal interval division and row-by-row traversal, critical track feature points are precisely extracted, and the track linear equation is derived through the least squares method, thus establishing an accurate railway boundary model. We optimized the YOLOv5s detection model in four aspects: incorporating the SE attention mechanism into the Neck network layer to enhance the model's feature extraction capabilities, adding a prediction layer to improve the detection performance for small objects, proposing a linear size scaling method to obtain suitable anchor boxes, and utilizing Inner-IoU to refine the boundary regression loss function, thereby increasing the positioning accuracy of the bounding boxes. We conducted a detection accuracy validation for railway track foreign object intrusion using a self-constructed image dataset. The results indicate that the proposed semantic segmentation model achieved an MIoU of 91.8%, representing a 3.9% improvement over the previous model, effectively segmenting railway tracks. Additionally, the optimized detection model could effectively detect foreign object intrusions on the tracks, reducing missed and false alarms and achieving a 7.4% increase in the mean average precision (IoU = 0.5) compared to the original YOLOv5s model. The model exhibits strong generalization capabilities in scenarios involving small objects. This proposed approach represents an effective exploration of deep learning techniques for railway track foreign object intrusion detection, suitable for use in complex environments to ensure the operational safety of rail lines.

The Role of Changes in the Expression of Inflammation-Associated Genes in the Variants of Cerebral Small Vessel Disease.

Age-dependent cerebral small vessel disease (CSVD) is a common disease with a high social burden characterized by heterogeneity of forms and frequent comorbidity with Alzheimer's disease (AD). Previously, we identified two MRI types of CSVD with specific clinical presentation and, probably, different mechanisms. The present study included 34 patients with CSVD and white matter hyperintensity (WMH) of stage Fazekas (F) 3 (mean age 61.7 ± 8.9) and 11 volunteers (mean age 57.3 ± 9.7). Total RNA was isolated from peripheral blood leukocytes. The expression of 58 protein-coding genes associated with CSVD and/or AD and 4 reference genes were assessed as part of the original panel for the NanoString nCounter analyzer. Testing results were validated by real-time PCR. There was a significant decrease in the expression levels of the ACOX1, CD33, CD2AP, TNFR1, and VEGFC genes in MRI type 2 relative to the control group as well as a decrease in the expression level of the CD33 gene in MRI type 2 compared to MRI type 1. Processes associated with inflammatory pathways with decreased expression of the identified genes are important in the development of MRI type 2 of CSVD. Given the direct connection of the established genes with AD, the importance of this form of CSVD in comorbidity with AD has been assumed.

A "Pincer" Type of Acridine-Triazole Fluorescent Dye for Iodine Detection by Both 'Naked-Eye' Colorimetric and Fluorometric Modes.

Iodine, primarily in the form of iodide (I-), is the bioavailable form for the thyroid in the human body. Both deficiency and excess intake of iodide can lead to serious health issues, such as thyroid disease. Selecting iodide ions among anions has been a significant challenge for decades due to interference from other anions. In this study, we designed and synthesized a new pincer-type acridine-triazole fluorescent probe (probe 1) with an acridine ring as a spacer and a triazole as a linking arm attached to two naphthol groups. This probe can selectively recognize iodide ions in a mixed solvent of THF/H2O (v/v, 9/1), changing its color from colorless to light yellow, making it suitable for highly sensitive and selective colorimetric and fluorescent detection in water systems. We also synthesized another molecular tweezer-type acridine-triazole fluorescent probe (probe 2) that exhibits uniform detection characteristics for iodide ions in the acetonitrile system. Interestingly, compared to probe 2, probe 1 can be detected by the naked eye due to its circulation effect, providing a simple method for iodine detection. The detection limit of probe 1 is determined to be 10-8 mol·L-1 by spectrometric titration and isothermal titration calorimetry measurements. The binding stoichiometry between probe 1 and iodide ions is calculated to be 1:1 by these methods, and the binding constant is 2 × 105 mol·L-1.

Expanding the horizons of levan: from microbial biosynthesis to applications and advanced detection methods.

Levan, a ß-(2,6)-linked fructose polymer, exhibits diverse properties that impart versatility, rendering it a highly sought-after biopolymer with various industrial applications. Levan can be produced by various microorganisms using sucrose, food industry byproducts and agricultural wastes. Microbial levan represents the most potent cost-effective process for commercial-scale levan production. This study reviews the optimization of levan production by understanding its biosynthesis, physicochemical properties and the fermentation process. In addition, genetic and protein engineering for its increased production and emerging methods for its detection are introduced and discussed. All of these comprehensive studies could serve as powerful tools to optimize levan production and broaden its applications across various industries.

[Verification Study of the Detection Method for Unauthorized Genetically Modified Papaya by Combining DNA Polymerases and Real-time PCR Instruments].

In the Japanese official detection method for unauthorized genetically modified (GM) papayas, one of two types of real-time PCR reagents with DNA polymerase (TaqMan Gene Master Mix [TaqMan Gene] or FastGene QPCR Probe Mastermix w/ROX [FastGene]) is primarily used for measurement. In 2022, we conducted a laboratory performance study on the unauthorized GM papaya line PRSV-YK, and the results revealed that high threshold cycle (Cq) values for the PRSV-YK detection test were obtained using TaqMan Gene with the 7500 Fast & 7500 Real-Time PCR System (ABI7500) and QuantStudio 12K Flex (QS12K), indicating the possibility of false negatives. The possibility of similar problems with all unauthorized GM papaya lines detection tests needs to be evaluated. In this study, we performed detection tests on unauthorized GM papaya lines (PRSV-YK, PRSV-SC, and PRSV-HN), the cauliflower mosaic virus 35S promotor (CaM), and a papaya positive control (Chy), and examined how the limits of detection (LOD) for each test are affected by two types of DNA polymerases (TaqMan Gene and FastGene) and three types of real-time PCR instruments (ABI7500, QS12K, and LightCycler 480 Instrument II [LC480]). In the PRSV-YK and PRSV-SC detection tests using ABI7500 and QS12K, measurement with TaqMan Gene showed a higher LOD than FastGene. In this case, an exponential amplification curve was confirmed on the amplification plot; however, the amplification curve did not cross the ΔRn threshold line and the correct Cq value was not obtained with a threshold line=0.2. The other tests (PRSV-HN, CaM, and Chy with ABI7500 and QS12K, and all detection tests with LC480) showed no important differences in the LOD for each test using either DNA polymerase. Therefore, when performing PRSV-YK and PRSV-SC detection tests with the ABI7500 or QS12K, FastGene should be used to avoid false negatives for foods containing GM papaya lines PRSV-YK and PRSV-SC at low mixing levels.

[Discussion on the application status and method improvement of gas chromatography in occupational health inspection standards in China].

As a rapid, accurate and efficient analytical technique, gas chromatography is widely used in the detection of volatile organic compounds and inorganic small molecule toxins, and it is the main analytical method in the national testing standards for occupational health. The existing effective national standards of gas chromatography for the detection of some substances have low column efficiency, high toxicity of reagents, poor correlation of the standard curve and low desorption efficiency and other problems, some of which can be solved through method improvement. At the same time, with the use of new materials and new processes, new types of toxic substances are emerging, and there are still many occupational disease hazards of limited value without supporting detection methods, gas chromatography can be applied to the detection of some toxic substances to better complement the vacancy of China's occupational health detection methods. This paper analyzes the current situation of the application of gas chromatography in occupational health testing standards, discusses the improvement of some of these methods, and helps to promote the application and development of gas chromatography in occupational health testing.

Focus on the frontier issue: progress in noninvasive prenatal screening for fetal trisomy from clinical perspectives.

The discovery of cell-free fetal DNA (cffDNA) in maternal blood and the rapid development of massively parallel sequencing have revolutionized prenatal testing from invasive to noninvasive. Noninvasive prenatal screening (NIPS) based on cffDNA enables the detection of fetal trisomy through sequencing, comparison, and bioassays. Its accuracy is better than that of traditional screening methods, and it is the most advanced clinical application of high-throughput sequencing technologies. However, the existing sequencing methods are limited by high costs and complex sequencing procedures. These limitations restrict the availability of NIPS for pregnant women. Many amplification methods have been developed to overcome the limitations of sequencing methods. The rapid development of non-sequencing methods has not been accompanied by reviews to summarize them. In this review, we initially describe the detection principles for sequencing-based NIPS. We summarize the rapidly evolving amplification technologies, focusing on the need to reduce costs and simplify the procedures. To ensure that the testing systems are feasible and that the testing processes are reliable, we expand our vision to the clinic. We evaluate the clinical validity of NIPS in terms of sensitivity, specificity, and positive predictive value. Finally, we summarize the application guidelines and discuss the corresponding quality control methods for NIPS. In addition to cffDNA, extracellular vesicle DNA, RNA, protein/peptide, and fetal cells can also be detected as biomarkers of NIPS. With the development of prenatal testing, NIPS has become increasingly important. Notably, NIPS is a screening test instead of a diagnostic test. The testing methods and procedures used in the NIPS process require standardization.

Research progress on pesticide residue detection based on microfluidic technology.

The problem of pesticide residue contamination has attracted widespread attention and poses a risk to human health. The current traditional pesticide residue detection methods have difficulty meeting rapid and diverse field screening requirements. Microfluidic technology integrates functions from sample preparation to detection, showing great potential for quick and accurate high-throughput detection of pesticide residues. This paper reviews the latest research progress on microfluidic technology for pesticide residue detection. First, the commonly used microfluidic materials are summarized, including silicon, glass, paper, polydimethylsiloxane, and polymethyl methacrylate. We evaluated their advantages and disadvantages in pesticide residue detection applications. Second, the current pesticide residue detection technology based on microfluidics and its application to real samples are summarized. Finally, we discuss this technology's present challenges and future research directions. This study is expected to provide a reference for the future development of microfluidic technology for pesticide residue detection.

Research progress in methods for detecting neutralizing antibodies against SARS-CoV-2.

The emergence of SARS-CoV-2 has seriously affected the lives of people worldwide. Clarifying the attenuation rule of SARS-CoV-2 neutralizing antibody (NAb) in vivo is the key to prevent reinfection and recurrence of virus. Currently, the commonly used methods for detecting NAb include virus neutralization tests, pseudovirus neutralization assays, lateral flow immunochromatography and enzyme-linked immunosorbent assays. The detection of NAb not only can be used to evaluate the level of immunity after vaccination or infection but also can provide important theoretical support for virus reinfection, recurrence and vaccine iteration. In this research, the related technologies of SARS-CoV-2 NAb detection were reviewed, aiming to provide better research ideas for SARS-CoV-2 epidemic prevention and control.

Recent developments in off-odor formation mechanism and the potential regulation by starter cultures in dry-cured ham.

Foul-smelling odors are main quality defects of dry-cured ham, which are connected with the excessive degradation of the structural proteins and excessive oxidation of lipids caused by the abnormal growth of spoilage microorganisms, threatening the development of dry-cured ham industry. Characterizing the key microorganisms and metabolites resulted in the spoilage of dry-cured ham, and discussing the relationship between spoilage microorganisms and metabolites are the key aspects to deeply understand the formation mechanism of off-odor in dry-cured ham. Until now, there is no detailed discussion or critical review on the role of spoilage microorganisms in developing the off-odor of dry-cured ham, and the regulation of off-odor and spoilage microorganisms by starter cultures has been not discussed. This review shows the recent achievement in the off-odor formation mechanism of dry-cured ham, and outlines the potential regulation of off-odor defects in dry-cured ham by starter cultures. Results from current research show that the abnormal growth of Lactic acid bacteria, Micrococcaceae, Enterobacteriaceae, Yeasts and Molds plays a key role in developing the off-odor defects of dry-cured ham, while the key spoilage microorganisms of different type hams are discrepant. High profile of aldehydes, acids, sulfur compounds and biogenic amines are responsible for off-odor development in spoiled dry-cured ham. Several starter cultures derived from these species of Staphylococcus, Penicillium, Debaryomyces, Pediococcus and Lactobacillus show a great potential to prevent microbiological hazards and improve flavor quality of dry-cured ham, whereas, the ecology, function and compatibility of these starter cultures with the processing parameters of dry-cured ham need to be further evaluated in the future.

Recent advances in inspection technologies of food safety health hazards for fish and fish products.

The development of reliable and sensitive detection methods is essential for addressing the escalating concerns surrounding fish and fish products, driven by increasing market demands. This comprehensive review presents recent advances in detection approaches, specifically focusing on microplastic, biological, and chemical hazards associated with these products. The overview encompasses 21 distinct detection methods, categorized based on the type of hazard they target. For microplastic hazards, six methods are visual, spectroscopic, and thermal analyses. Biological hazard identification relies on six approaches employing nucleic-acid sequence, immunological, and biosensor technologies. The investigation of chemical hazards encompasses ten methods, including chromatography, spectroscopy, mass spectrometry, immunological, biosensor, and electrochemical techniques. The review provides in-depth insights into the basic principles, general characteristics, and the recognized advantages and disadvantages of each method. Moreover, it elaborates on recent advancements within these methodologies. The concluding section of the review discusses current challenges and outlines future perspectives for these detection methods. Overall, this comprehensive summary not only serves as a guide for researchers involved in fish safety and quality control but also emphasizes the significance of staying abreast of evolving detection technologies to ensure the continued safety of fish and fish products in response to emerging food safety hazards.

Role of Residual Monomers in the Manifestation of (Cyto)toxicity by Polystyrene Microplastic Model Particles.

Polystyrene (PS) is an important model polymer for the investigation of effects of microplastic (MP) and nanoplastic (NP) particles on living systems. Aqueous dispersions of PS MP or NP contain residual monomers of styrene. In consequence, it is not clear if the effects observed in standard (cyto)toxicity studies are evoked by the polymer (MP/NP) particle or by residual monomers. We addressed that question by comparing standard PS model particle dispersions with in-house synthesized PS particle dispersions. We proposed a rapid purification method of PS particle dispersions by dialysis against mixed solvents and developed a simple method of UV-vis spectrometry to detect residual styrene in the dispersions. We found that standard PS model particle dispersions, which contain residual monomers, exerted a low but significant cytotoxicity on mammalian cells, while the in-house synthesized PS, after rigorous purification to reduce the styrene content, did not. However, the PS particles per se but not the residual styrene in both PS particle dispersions resulted in immobilization of Daphnia. Only by using freshly monomer-depleted particles, will it be possible in the future to assess the (cyto)toxicities of PS particles, avoiding an otherwise not controllable bias effect of the monomer.

Ratiometric optical detection of pyrophosphate based on aggregation-caused dual-signal response of gold nanoclusters.

Sensing of pyrophosphate ion (PPi) has received much attention due to the strong demand for clinical diagnostics. Here, based on gold nanoclusters (Au NCs), a ratiometric optical detection method for PPi is developed by simultaneously detecting the dual signals of fluorescence (FL) and second-order scattering (SOS). The PPi is detected by inhibiting the formation of aggregates of Fe3+ with Au NCs. Binding of Fe3+ to Au NCs causes aggregation of Au NCs, which leads to fluorescence quenching and scattering increasing. The presence of PPi can competitively bind Fe3+ to re-disperse the Au NCs and finally recover the fluorescence and reduce the scattering signal. The designed PPi sensor shows a high sensitivity with a linear range 5-50 µM and a detection limit of 1.2 µM. In addition, the assay has excellent selectivity for PPi, which makes its application in real biological samples extremely valuable.

DSSS Signal Detection Based on CNN.

With the wide application of direct sequence spread spectrum (DSSS) signals, the comprehensive performance of DSSS communication systems has been continuously improved, making the electronic reconnaissance link in communication countermeasures more difficult. Electronic reconnaissance technology, as the fundamental means of modern electronic warfare, mainly includes signal detection, recognition, and parameter estimation. At present, research on DSSS detection algorithms is mostly based on the correlation characteristics of DSSS signals, and autocorrelation algorithm is the most mature and widely used method in practical engineering. With the continuous development of deep learning, deep-learning-based methods have gradually been introduced to replace traditional algorithms in the field of signal processing. This paper proposes a spread spectrum signal detection method based on convolutional neural network (CNN). Through experimental analysis, the detection performance of the CNN model proposed in this paper on DSSS signals in various situations has been compared and analyzed with traditional autocorrelation detection methods for different signal-to-noise ratios. The experiments verified the estimation performance of the model in this paper under different signal-to-noise ratios, different spreading code lengths, different spreading code types, and different modulation methods and compared it with the autocorrelation detection algorithm. It was found that the detection performance of the model in this paper was higher than that of the autocorrelation detection method, and the overall performance was improved by 4 dB.

Preliminary Study on Rapid and Simultaneous Detection of Viable Escherichia coli O157:H7, Staphylococcus aureus, and Salmonella by PMA-mPCR in Food.

Escherichia coli O157:H7, Staphylococcus aureus, and Salmonella are major foodborne pathogens that are widespread in nature and responsible for several outbreaks of food safety accidents. Thus, a rapid and practical technique (PMA-mPCR) was developed for the simultaneous detection of viable E. coli O157:H7, S. aureus, and Salmonella in pure culture and in a food matrix. To eliminate false positive results, propidium monoazide (PMA) was applied to selectively suppress the DNA amplification of dead cells. The results showed the optimum concentration of PMA is 5.0 µg/mL. The detection limit of this assay by mPCR was 103 CFU/mL in the culture broth, and by PMA-mPCR was 104 CFU/mL both in pure culture and a food matrix (milk and ground beef). In addition, the detection of mixed viable and dead cells was also explored in this study. The detection sensitivity ratio of viable and dead counts was less than 1:10. Therefore, the PMA-mPCR assay proposed here might provide an efficient detection tool for the simultaneous detection of viable E. coli O157:H7, S. aureus, and Salmonella and also have great potential for the detection and concentration assessment of VBNC cells.