Monitoring emerging pathogens using negative nucleic acid test results from endemic pathogens in pig populations: Application to porcine enteric coronaviruses.

This study evaluated the use of endemic enteric coronaviruses polymerase chain reaction (PCR)-negative testing results as an alternative approach to detect the emergence of animal health threats with similar clinical diseases presentation. This retrospective study, conducted in the United States, used PCR-negative testing results from porcine samples tested at six veterinary diagnostic laboratories. As a proof of concept, the database was first searched for transmissible gastroenteritis virus (TGEV) negative submissions between January 1st, 2010, through April 29th, 2013, when the first porcine epidemic diarrhea virus (PEDV) case was diagnosed. Secondly, TGEV- and PEDV-negative submissions were used to detect the porcine delta coronavirus (PDCoV) emergence in 2014. Lastly, encountered best detection algorithms were implemented to prospectively monitor the 2023 enteric coronavirus-negative submissions. Time series (weekly TGEV-negative counts) and Seasonal Autoregressive-Integrated Moving-Average (SARIMA) were used to control for outliers, trends, and seasonality. The SARIMA's fitted and residuals were then subjected to anomaly detection algorithms (EARS, EWMA, CUSUM, Farrington) to identify alarms, defined as weeks of higher TGEV-negativity than what was predicted by models preceding the PEDV emergence. The best-performing detection algorithms had the lowest false alarms (number of alarms detected during the baseline) and highest time to detect (number of weeks between the first alarm and PEDV emergence). The best-performing detection algorithms were CUSUM, EWMA, and Farrington flexible using SARIMA fitted values, having a lower false alarm rate and identified alarms 4 to 17 weeks before PEDV and PDCoV emergences. No alarms were identified in the 2023 enteric negative testing results. The negative-based monitoring system functioned in the case of PEDV propagating epidemic and in the presence of a concurrent propagating epidemic with the PDCoV emergence. It demonstrated its applicability as an additional tool for diagnostic data monitoring of emergent pathogens having similar clinical disease as the monitored endemic pathogens.

Tris stabilized AuNPs based lateral flow immunochromatography for the simultaneous detection of porcine epidemic diarrhea virus and rotavirus on-site.

Porcine epidemic diarrhea virus (PEDV) and rotavirus has posed a significant threat to the pig industry annually across different nations, resulting in huge economic losses. The frequent co-infection of these two viruses in clinical settings complicates the process of differential diagnoses. Rapid and accurate detection of PEDV and rotavirus is in great demand for timely diarrhea disease prevention and control. In this study, tris stabilized AuNPs were prepared and a sensitive lateral flow immunoassay (LFIA) sensor was developed for the simultaneous and rapid detection of PEDV and rotavirus on site. After the system optimization, the established LFIA can simultaneously identify PEDV and rotavirus with limits of detection (LOD) of 1.25 × 103 TCID50 mL-1 and 3.13 × 102 pg mL-1, respectively. When applying for clinical samples, the LFIA show a concordance of 95 % and 100 % to reverse transcript polymerase chain reaction (RT-PCR) for PEDV and rotavirus respectively. Therefore, this LFIA can qualitatively detect PEDV and rotavirus in 18 min with high sensitivity and accuracy without any sophisticated equipment and operation, making it a promising candidate for the early diagnosis of PEDV or/and rotavirus diarrhea on site.

Establishment of a rapid real-time fluorescence-based recombinase-aided amplification method for detection of avian infectious bronchitis virus.

Infectious bronchitis (IB) is an acute, highly contagious contact respiratory disease of chickens caused by infectious bronchitis virus (IBV). IBV is very prone to mutation, which brings great difficulties to the prevention and control of the disease. Therefore, there is a pressing need for a method that is fast, sensitive, specific, and convenient for detecting IBV. In this study, a real-time fluorescence-based recombinase-aided amplification (RF-RAA) method was established. Primers and probe were designed based on the conserved regions of the IBV M gene and the reaction concentrations were optimized, then the specificity, sensitivity, and reproducibility of this assay were tested. The results showed that the RF-RAA method could be completed at 39℃ within 20 min, during which the results could be interpreted visually in real-time. The RF-RAA method had good specificity, no cross-reaction with common poultry pathogens, and it detected a minimum concentration of template of 2 copies/µL for IBV. Besides, its reproducibility was stable. A total of 144 clinical samples were tested by RF-RAA and real-time quantitative PCR (qPCR), 132 samples of which were positive and 12 samples were negative, and the coincidence rate of the two methods was 100 %. In conclusion, the developed RF-RAA detection method is rapid, specific, sensitive, reproducible, and convenient, which can be utilized for laboratory detection and clinical diagnosis of IBV.

Seroprevalence of the novel swine acute diarrhea syndrome coronavirus in China assessed by enzyme-linked immunosorbent assay.

The endemic outbreak of SADS-CoV has resulted in economic losses and potentially threatened the safety of China's pig industry. The molecular epidemiology of SADS-CoV in pig herds has been investigated in many provinces in China. However, there are no data over a long-time span, and there is a lack of extensive serological surveys to assess the prevalence of SADS-CoV in Chinese swine herds since the discovery of SADS-CoV. In this study, an indirect anti-SADS-CoV IgG enzyme-linked immunosorbent assay (ELISA) based on the SADS-CoV S1 protein was established to investigate the seroprevalence of SADS-CoV in Chinese swine herds. Cross-reactivity assays, indirect immunofluorescence, and western blotting assays showed that the developed ELISA had excellent SADS-CoV specificity. In total, 12,978 pig serum samples from 29 provinces/municipalities/autonomous regions in China were tested from 2022 to 2023. The results showed that the general seroprevalence of SADS-CoV in China was 59.97%, with seroprevalence ranging from 16.7% to 77.12% in different provinces and from 42.61% to 68.45% in different months. SADS-CoV is widely prevalent in China, and its seroprevalence was higher in Northeast China, North China, and Central China than in other regions. Among the four seasons, the prevalence of SADS-CoV was the highest in spring and the lowest in autumn. The results of this study provide the general seroprevalence profile of SADS-CoV in China, facilitating the understanding of the prevalence of SADS-CoV in pigs. More importantly, this study is beneficial in formulating preventive and control measures for SADS-CoV and may provide directions for vaccine development.

A portable transistor immunosensor for fast identification of porcine epidemic diarrhea virus.

Widespread distribution of porcine epidemic diarrhea virus (PEDV) has led to catastrophic losses to the global pig farming industry. As a result, there is an urgent need for rapid, sensitive and accurate tests for PEDV to enable timely and effective interventions. In the present study, we develop and validate a floating gate carbon nanotubes field-effect transistor (FG CNT-FET)-based portable immunosensor for rapid identification of PEDV in a sensitive and accurate manner. To improve the affinity, a unique PEDV spike protein-specific monoclonal antibody is prepared by purification, and subsequently modified on FG CNT-FET sensor to recognize PEDV. The developed FET biosensor enables highly sensitive detection (LoD: 8.1 fg/mL and 100.14 TCID50/mL for recombinant spike proteins and PEDV, respectively), as well as satisfactory specificity. Notably, an integrated portable platform consisting of a pluggable FG CNT-FET chip and a portable device can discriminate PEDV positive from negative samples and even identify PEDV and porcine deltacoronavirus within 1 min with 100% accuracy. The portable sensing platform offers the capability to quickly, sensitively and accurately identify PEDV, which further points to a possibility of point of care (POC) applications of large-scale surveillance in pig breeding facilities.

Quantitative assay to analyze neutralization and inhibition of authentic Middle East respiratory syndrome coronavirus.

To date, there is no licensed vaccine for Middle East respiratory syndrome coronavirus (MERS-CoV). Therefore, MERS-CoV is one of the diseases targeted by the Coalition for Epidemic Preparedness Innovations (CEPI) vaccine development programs and has been classified as a priority disease by the World Health Organization (WHO). An important measure of vaccine immunogenicity and antibody functionality is the detection of virus-neutralizing antibodies. We have developed and optimized a microneutralization assay (MNA) using authentic MERS-CoV and standardized automatic counting of virus foci. Compared to our standard virus neutralization assay, the MNA showed improved sensitivity when analyzing 30 human sera with good correlation of results (Spearman's correlation coefficient r = 0.8917, p value < 0.0001). It is important to use standardized materials, such as the WHO international standard (IS) for anti-MERS-CoV immunoglobulin G, to compare the results from clinical trials worldwide. Therefore, in addition to the neutralizing titers (NT50 = 1384, NT80 = 384), we determined the IC50 and IC80 of WHO IS in our MNA to be 0.67 IU/ml and 2.6 IU/ml, respectively. Overall, the established MNA is well suited to reliably quantify vaccine-induced neutralizing antibodies with high sensitivity.

COVID­19 detection from chest X-ray images using transfer learning.

COVID-19 is a kind of coronavirus that appeared in China in the Province of Wuhan in December 2019. The most significant influence of this virus is its very highly contagious characteristic which may lead to death. The standard diagnosis of COVID-19 is based on swabs from the throat and nose, their sensitivity is not high enough and so they are prone to errors. Early diagnosis of COVID-19 disease is important to provide the chance of quick isolation of the suspected cases and to decrease the opportunity of infection in healthy people. In this research, a framework for chest X-ray image classification tasks based on deep learning is proposed to help in early diagnosis of COVID-19. The proposed framework contains two phases which are the pre-processing phase and classification phase which uses pre-trained convolution neural network models based on transfer learning. In the pre-processing phase, different image enhancements have been applied to full and segmented X-ray images to improve the classification performance of the CNN models. Two CNN pre-trained models have been used for classification which are VGG19 and EfficientNetB0. From experimental results, the best model achieved a sensitivity of 0.96, specificity of 0.94, precision of 0.9412, F1 score of 0.9505 and accuracy of 0.95 using enhanced full X-ray images for binary classification of chest X-ray images into COVID-19 or normal with VGG19. The proposed framework is promising and achieved a classification accuracy of 0.935 for 4-class classification.

Desorption Separation Ionization Mass Spectrometry (DSI-MS) for Rapid Analysis of COVID-19.

During the coronavirus disease 2019 (COVID-19) pandemic, which has witnessed over 772 million confirmed cases and over 6 million deaths globally, the outbreak of COVID-19 has emerged as a significant medical challenge affecting both affluent and impoverished nations. Therefore, there is an urgent need to explore the disease mechanism and to implement rapid detection methods. To address this, we employed the desorption separation ionization (DSI) device in conjunction with a mass spectrometer for the efficient detection and screening of COVID-19 urine samples. The study encompassed patients with COVID-19, healthy controls (HC), and patients with other types of pneumonia (OP) to evaluate their urine metabolomic profiles. Subsequently, we identified the differentially expressed metabolites in the COVID-19 patients and recognized amino acid metabolism as the predominant metabolic pathway involved. Furthermore, multiple established machine learning algorithms validated the exceptional performance of the metabolites in discriminating the COVID-19 group from healthy subjects, with an area under the curve of 0.932 in the blind test set. This study collectively suggests that the small-molecule metabolites detected from urine using the DSI device allow for rapid screening of COVID-19, taking just three minutes per sample. This approach has the potential to expand our understanding of the pathophysiological mechanisms of COVID-19 and offers a way to rapidly screen patients with COVID-19 through the utilization of machine learning algorithms.

Development of a colloidal gold immunochromatographic strip for rapid detection of avian coronavirus infectious bronchitis virus.

Avian infectious bronchitis virus (IBV) still causes serious economic losses in the poultry industry. Currently, there are multiple prevalent genotypes and serotypes of IBVs. It is imperative to develop a new diagnosis method that is fast, sensitive, specific, simple, and broad-spectrum. A monoclonal hybridoma cell, N2D5, against the IBV N protein was obtained after fusion of myeloma SP2/0 cells with spleen cells isolated from the immunized Balb/c mice. The N2D5 monoclonal antibody (mAb) and the previously prepared mouse polyclonal antibody against the IBV N protein were used to target IBV as a colloidal gold-mAb conjugate and a captured antibody, respectively, in order to develop an immunochromatographic strip. The optimal pH and minimum antibody concentration in the reaction system for colloidal gold-mAb N2D5 conjugation were pH 6.5 and 30 µg/mL, respectively. Common avian pathogens were tested to evaluate the specificity of the strip and no cross-reaction was observed. The sensitivity of the strip for detecting IBV was 10-1.4522 EID50/mL. The strip showed a broad-spectrum cross-reactive capacity for detecting IBV antigens, including multiple IBV genotypes in China and all of the seven serotypes of IBV that are currently prevalent in southern China. Additionally, the result can be observed within 2 min without any equipment. The throat and cloacal swab samples of chickens that were artificially infected with three IBV strains were tested using the developed strip and the qPCR method; the strip test demonstrated a high consistency in detecting IBV via qPCR gene detection. In conclusion, the immunochromatographic strip that was established is rapid, sensitive, specific, simple, practical, and broad-spectrum; additionally, it has the potential to serve as an on-site rapid detection method of IBV and can facilitate the surveillance and control of the disease, especially in resource-limited areas.

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Objective: To construct a deep learning-based target detection method to help radiologists perform rapid diagnosis of lesions in the CT images of patients with novel coronavirus pneumonia (NCP) by restoring detailed information and mining local information. Methods: We present a deep learning approach that integrates detail upsampling and attention guidance. A linear upsampling algorithm based on bicubic interpolation algorithm was adopted to improve the restoration of detailed information within feature maps during the upsampling phase. Additionally, a visual attention mechanism based on vertical and horizontal spatial dimensions embedded in the feature extraction module to enhance the capability of the object detection algorithm to represent key information related to NCP lesions. Results: Experimental results on the NCP dataset showed that the detection method based on the detail upsampling algorithm improved the recall rate by 1.07% compared with the baseline model, with the AP50 reaching 85.14%. After embedding the attention mechanism in the feature extraction module, 86.13% AP50, 73.92% recall, and 90.37% accuracy were achieved, which were better than those of the popular object detection models. Conclusion: The feature information mining of CT images based on deep learning can further improve the lesion detection ability. The proposed approach helps radiologists rapidly identify NCP lesions on CT images and provides an important clinical basis for early intervention and high-intensity monitoring of NCP patients.

A novel linear B cell epitope of the canine coronavirus nucleocapsid protein identified by a monoclonal antibody.

The infection of canine coronavirus (CCoV) causes a highly contagious disease in dogs with acute gastroenteritis. The efficient serological diagnostics is critical for controlling the disease caused by CCoV. Nucleocapsid (N) protein of CCoV is an important target for developing serological approaches. However, little is known about the antigenic sites in the N protein of CCoV. In this study, we generated a monoclonal antibody (mAb) against the N protein of CCoV, designated as 13E8, through the fusion of the sp2/0 cells with the spleen cells from a mouse immunized with the purified recombinant GST-N protein. Epitope mapping revealed that mAb 13E8 recognized a novel linear B cell epitope in N protein at 294-314aa (named as EP-13E8) by using a serial of truncated N protein through Western blot and ELISA. Sequence analysis showed that the sequence of EP-13E8 was highly conserved (100 %) among different CCoV strains analyzed, but exhibited a low similarity (31.8-63.6 %) with the responding sequence in other coronaviruses of the same genus such as FCoV, PEDV and HCoV except for TGEV (95.5 % identity). Structural assay suggested that the epitope of EP-13E8 were located in the close proximity on the surface of the N protein. Overall, the mAb 13E8 against N protein generated and its epitope EP-13E8 identified here paid the way for further developing epitope-based serological diagnostics for CCoV.

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.

Comparative Performance in the Detection of Four Coronavirus Genera from Human, Animal, and Environmental Specimens.

Emerging coronaviruses (CoVs) are understood to cause critical human and domestic animal diseases; the spillover from wildlife reservoirs can result in mild and severe respiratory illness in humans and domestic animals and can spread more readily in these naïve hosts. A low-cost CoV molecular method that can detect a variety of CoVs from humans, animals, and environmental specimens is an initial step to ensure the early identification of known and new viruses. We examine a collection of 50 human, 46 wastewater, 28 bat, and 17 avian archived specimens using 3 published pan-CoV PCR assays called Q-, W-, and X-CoV PCR, to compare the performance of each assay against four CoV genera. X-CoV PCR can detect all four CoV genera, but Q- and W-CoV PCR failed to detect δ-CoV. In total, 21 (42.0%), 9 (18.0%), and 21 (42.0%) of 50 human specimens and 30 (65.22%), 6 (13.04%), and 27 (58.70%) of 46 wastewater specimens were detected using Q-, W-, and X-CoV PCR assays, respectively. The X-CoV PCR assay has a comparable sensitivity to Q-CoV PCR in bat CoV detection. Combining Q- and X-CoV PCR assays can increase sensitivity and avoid false negative results in the early detection of novel CoVs.

Molecular and Serological Detection of Bovine Coronaviruses in Marmots (Marmota marmota) in the Alpine Region.

In this study, virological surveillance focused on coronaviruses in marmots in the Alpine region in 2022, captured as part of a population control reduction program in the Livigno area. Seventy-six faecal samples were randomly collected from marmots at the time of capture and release and tested for genome detection of pan-coronavirus, pan-pestivirus, canine distemper virus, and influenza A and D virus. Nine faecal samples were positive in the Pan-CoV RT-PCR, while all were negative for the other viruses. Pan-coronavirus positives were further identified using Illumina's complete genome sequencing, which showed the highest homology with Bovine Coronavirus previously detected in roe deer in the Alps. Blood samples (n.35) were collected randomly from animals at release and tested for bovine coronavirus (BCoV) antibodies using competitive ELISA and VNT. Serological analyses revealed that 8/35 sera were positive for BCoV antibodies in both serological tests. This study provides molecular and serological evidence of the presence of BCoV in an alpine marmot population due to a likely spillover event. Marmots share areas and pastures with roe deer and other wild ruminants, and environmental transmission is a concrete possibility.

Pyrococcus furiosus Argonaute Based Detection Assays for Porcine Deltacoronavirus.

Porcine deltacoronavirus (PDCoV) is a major cause of diarrhea and diarrhea-related deaths among piglets and results in massive losses to the overall porcine industry. The clinical manifestations of porcine diarrhea brought on by the porcine epidemic diarrhea virus (PEDV), porcine transmissible gastroenteritis virus (TGEV), and PDCoV are oddly similar to each other. Hence, the identification of different pathogens through molecular diagnosis and serological techniques is crucial. Three novel detection methods for identifying PDCoV have been developed utilizing recombinase-aided amplification (RAA) or reverse transcription recombinase-aided amplification (RT-RAA) in conjunction with Pyrococcus furiosus Argonaute (PfAgo): RAA-PfAgo, one-pot RT-RAA-PfAgo, and one-pot RT-RAA-PfAgo-LFD. The indicated approaches have a detection limit of around 60 copies/µL of PDCoV and do not cross-react with other viruses including PEDV, TGEV, RVA, PRV, PCV2, or PCV3. The applicability of one-pot RT-RAA-PfAgo and one-pot RT-RAA-PfAgo-LFD were examined using clinical samples and showed a positive rate comparable to the qPCR method. These techniques offer cutting-edge technical assistance for identifying, stopping, and managing PDCoV.

Development and preliminary validation of a MERS-CoV ELISA for serological testing of camels and alpacas.

This study describes the development and preliminary validation of a new serological assay using MERS-CoV S1 protein in an indirect enzyme-linked immunosorbent assay (ELISA) format. This assay has the advantage of being able to test MERS-CoV serum samples in a PC2 laboratory without the need for a high-level biocontainment laboratory (PC3 or PC4), which requires highly trained and skilled staff and a high level of resources and equipment. Furthermore, this MERS-CoV S1 ELISA enables a larger number of samples to be tested quickly, with results obtained in approximately five hours. The MERS-CoV S1 ELISA demonstrated high analytical specificity, with no cross-reactivity observed in serum of animals infected with other viruses, including different coronaviruses. We tested 166 positive and 40 negative camel serum samples and have estimated the diagnostic sensitivity (DSe) to be 99.4% (95% CI: 96.7 - 100.0%) and diagnostic specificity (DSp) to be 100% (95% CI: 97.2%-100.0%) relative to the assigned serology results (ppNT and VNT) using a S/P ratio cut-off value of >0.58. The findings of this study showed that our MERS-CoV S1 ELISA was more sensitive than the commercial EUROIMMUN ELISA (Se 99.4% vs 84.9%) and comparable to the ppNT assay, and therefore could be used as a diagnostic aid in countries in the Middle East where MERS-CoV is endemic in dromedary camels. The assay reagents and protocol were easily adapted and transferred from an Australian laboratory to a laboratory in the University of Hong Kong. Thus, the results described here show that the MERS-CoV S1 ELISA represents a cheap, rapid, robust, and reliable assay to support surveillance of MERS-CoV in camels in endemic regions.

Platelet and Monocyte Microvesicles as Potential Biomarkers of COVID-19 Severity: A Cross-Sectional Analysis.

Background: Coronavirus disease (COVID-19) induces inflammation, coagulopathy following platelet and monocyte activation, and fibrinolysis, resulting in elevated D-dimer levels. Activated platelets and monocytes produce microvesicles (MVs). We analyzed the differences in platelet and monocyte MV counts in mild, moderate, and severe COVID-19, as well as their correlation with D-dimer levels. Methods: In this cross-sectional study, blood specimens were collected from 90 COVID-19 patients and analyzed for D-dimers using SYSMEX CS-2500. Platelet MVs (PMVs; PMVCD42b+ and PMVCD41a+), monocyte MVs (MMVs; MMVCD14+), and phosphatidylserine-binding annexin V (PS, AnnV+) were analyzed using a BD FACSCalibur instrument. Results: PMV and MMV counts were significantly increased in COVID-19 patients. AnnV+ PMVCD42b+ and AnnV+ PMVCD41a+ cell counts were higher in patients with severe COVID-19 than in those with moderate clinical symptoms. The median (range) of AnnV+ PMVCD42b+ (MV/µL) in mild, moderate, and severe COVID-19 was 1,118.3 (328.1-1,910.5), 937.4 (311.4-2,909.5), and 1,298.8 (458.2-9,703.5), respectively (P =0.009). The median (range) for AnnV+ PMVCD41a+ (MV/µL) in mild, moderate, and severe disease was 885.5 (346.3-1,682.7), 663.5 (233.8-2,081.5), and 1,146.3 (333.3-10,296.6), respectively (P =0.007). D-dimer levels (ng/mL) weak correlated with AnnV+ PMVCD41a+ (P =0.047, r=0.258). Conclusions: PMV PMVCD42b+ and PMVCD41a+ counts were significantly increased in patients with severe clinical symptoms, and PMVCD41a+ counts correlated with D-dimer levels. Therefore, MV counts can be used as a potential biomarker of COVID-19 severity.

An isothermal nucleic acid amplification-based enzymatic recombinase amplification method for dual detection of porcine epidemic diarrhea virus and porcine rotavirus A.

Viral diarrhea is the predominant digestive tract sickness in piglings, resulting in substantial profit losses in the porcine industry. Porcine rotavirus A (PoRVA) and porcine epidemic diarrhea virus (PEDV) are the main causes of grave gastroenteritis and massive dysentery, especially in piglets. PoRVA and PEDV have high transmissibility, exhibit similar clinical symptoms, and frequently co-occur. Therefore, to avoid financial losses, a quick, highly efficient, objective diagnostic test for the prevention and detection of these diseases is required. Enzymatic recombinase amplification (ERA) is a novel technology based on isothermal nucleic acid amplification. It demonstrates high sensitivity and excellent specificity, with a short processing time and easy operability, compared with other in vitro nucleic acid amplification technologies. In this study, a dual ERA method to detect and distinguish between PEDV and PoRVA nucleic acids was established. The method shows high sensitivity, as the detection limits were 101 copies/µL for both viruses. To test the usefulness of this method in clinical settings, we tested 64 swine clinical samples. Our results were 100% matched with those acquired using a commercially available kit. Therefore, we have successfully developed a dual diagnostic ERA nucleic acids method for detecting and distinguishing between PEDV and PoRVA.

A multiplex real-time RT-qPCR assay for simultaneous detection of porcine epidemic diarrhea virus, porcine deltacoronavirus, and swine acute diarrhea syndrome coronavirus.

Porcine epidemic diarrhea virus (PEDV), porcine deltacoronavirus (PDCoV), and swine acute diarrhea syndrome coronavirus (SADS-CoV) cause intestinal diseases with similar manifestations in suckling piglets. In this study, we developed a multiplex real-time PCR for differential diagnosis of PEDV, PDCoV, and SADS-CoV. The assay demonstrated high specificity with a detection limit of 5 copies/µl for each virus. The assay specifically detected PEDV, PDCoV, and SADS-CoV and excluded all other swine pathogens circulating in pigs. Furthermore, the assay exhibited satisfactory performance in analyzing clinical samples. The data indicate that the newly developed multiplex real-time PCR method can be applied for differential diagnosis of porcine enteric coronaviruses.

Pyrococcus furiosus Argonaute-mediated porcine epidemic diarrhea virus detection.

Porcine epidemic diarrhea virus (PEDV), an enteric coronavirus, induces severe vomiting and acute watery diarrhea in unweaned piglets. The pig industry has suffered tremendous financial losses due to the high mortality rate of piglets caused by PEDV. Consequently, a simple and rapid on-site diagnostic technology is crucial for preventing and controlling PEDV. This study established a detection method for PEDV using recombinase-aided amplification (RAA) and Pyrococcus furiosus Argonaute (PfAgo), which can detect 100 copies of PEDV without cross-reactivity with other pathogens. The entire reaction of RAA and PfAgo to detect PEDV does not require sophisticated instruments, and the reaction results can be observed with the naked eye. Overall, this integrated RAA-PfAgo cleavage assay is a practical tool for accurately and quickly detecting PEDV. KEY POINTS: • PfAgo has the potential to serve as a viable molecular diagnostic tool for the detection and diagnosis of viral genomes • The RAA-PfAgo detection technique has a remarkable level of sensitivity and specificity • The RAA-PfAgo detection system can identify PEDV without needing advanced equipment.