ADP-Ribosylation in Antiviral Innate Immune Response.

Adenosine diphosphate (ADP)-ribosylation is a reversible post-translational modification catalyzed by ADP-ribosyltransferases (ARTs). ARTs transfer one or more ADP-ribose from nicotinamide adenine dinucleotide (NAD+) to the target substrate and release the nicotinamide (Nam). Accordingly, it comes in two forms: mono-ADP-ribosylation (MARylation) and poly-ADP-ribosylation (PARylation). ADP-ribosylation plays important roles in many biological processes, such as DNA damage repair, gene regulation, and energy metabolism. Emerging evidence demonstrates that ADP-ribosylation is implicated in host antiviral immune activity. Here, we summarize and discuss ADP-ribosylation modifications that occur on both host and viral proteins and their roles in host antiviral response.

The S protein of a novel recombinant PEDV strain promotes the infectivity and pathogenicity of PEDV in mid-west China.

Porcine epidemic diarrhoea virus (PEDV) is an emerging and re-emerging swine enterovirus that causes highly contagious diarrhoea and mortality in piglets. To better understand the current prevalence of PEDV in mid-west China, and to find out the reason for the re-emergence of PEDV from the viral genomic characteristics. Herein, we firstly investigated epidemiology of PEDV in mid-west China from 2019 to 2020. A total of 62.23% (257/413) of diarrhoea samples were positive for PEDV, and the PEDV-positive cases were mainly detected in winter. Then, we selected the SXSL strain as a representative strain to study the genetic and pathogenic characterization of PEDV pandemic strains in mid-west China. The recombination analysis showed that SXSL strain was a recombinant strain, and the major and minor parent strains of the recombination are CH/SCZJ/2018 strain and GDS48 strain, respectively. Complete genome sequencing and homology analysis showed that the S protein of SXSL strain contained multiple amino acid indels and mutations compared to the PEDV representative strains. Furthermore, we evaluated the effect of S protein on the infectivity and pathogenicity of PEDV by the PEDV reverse genetics system, and results showed that SXSL S protein increased the infectivity and pathogenicity of chimeric virus. Overall, our findings provided important information for understanding the roles of S protein in the prevalence of PEDV in mid-west China and developing vaccines based on PEDV pandemic strains.

Coupling freedom from disease principles and early warning from wastewater surveillance to improve health security.

Infectious disease surveillance is vitally important to maintaining health security, but these efforts are challenged by the pace at which new pathogens emerge. Wastewater surveillance can rapidly obtain population-level estimates of disease transmission, and we leverage freedom from disease principles to make use of nondetection of SARS-CoV-2 in wastewater to estimate the probability that a community is free from SARS-CoV-2 transmission. From wastewater surveillance of 24 treatment plants across upstate New York from May through December of 2020, trends in the intensity of SARS-CoV-2 in wastewater correlate with trends in COVID-19 incidence and test positivity (⍴ > 0.5), with the greatest correlation observed for active cases and a 3-day lead time between wastewater sample date and clinical test date. No COVID-19 cases were reported 35% of the time the week of a nondetection of SARS-CoV-2 in wastewater. Compared to the United States Centers for Disease Control and Prevention levels of transmission risk, transmission risk was low (no community spared) 50% of the time following nondetection, and transmission risk was moderate or lower (low community spread) 92% of the time following nondetection. Wastewater surveillance can demonstrate the geographic extent of the transmission of emerging pathogens, confirming that transmission risk is either absent or low and alerting of an increase in transmission. If a statewide wastewater surveillance platform had been in place prior to the onset of the COVID-19 pandemic, policymakers would have been able to complement the representative nature of wastewater samples to individual testing, likely resulting in more precise public health interventions and policies.

High-throughput wastewater analysis for substance use assessment in central New York during the COVID-19 pandemic.

Wastewater entering sewer networks represents a unique source of pooled epidemiological information. In this study, we coupled online solid-phase extraction with liquid chromatography-high resolution mass spectrometry to achieve high-throughput analysis of health and lifestyle-related substances in untreated municipal wastewater during the coronavirus disease 2019 (COVID-19) pandemic. Twenty-six substances were identified and quantified in influent samples collected from six wastewater treatment plants during the COVID-19 pandemic in central New York. Over a 12 week sampling period, the mean summed consumption rate of six major substance groups (i.e., antidepressants, antiepileptics, antihistamines, antihypertensives, synthetic opioids, and central nervous system stimulants) correlated with disparities in household income, marital status, and age of the contributing populations as well as the detection frequency of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA in wastewater and the COVID-19 test positivity in the studied sewersheds. Nontarget screening revealed the covariation of piperine, a nontarget substance, with SARS-CoV-2 RNA in wastewater collected from one of the sewersheds. Overall, this proof-of-the-concept study demonstrated the utility of high-throughput wastewater analysis for assessing the population-level substance use patterns during a public health crisis such as COVID-19.

Artificial Intelligence Clinicians Can Use Chest Computed Tomography Technology to Automatically Diagnose Coronavirus Disease 2019 (COVID-19) Pneumonia and Enhance Low-Quality Images.

PURPOSE: Nowadays, the number of patients with COVID-19 pneumonia worldwide is still increasing. The clinical diagnosis of COVID-19 pneumonia faces challenges, such as the difficulty to perform RT-PCR tests in real time, the lack of experienced radiologists, clinical low-quality images, and the similarity of imaging features of community-acquired pneumonia and COVID-19. Therefore, we proposed an artificial intelligence model GARCD that uses chest CT images to assist in the diagnosis of COVID-19 in real time. It can show better diagnostic performance even facing low-quality CT images. METHODS: We used 14,129 CT images from 104 patients. A total of 12,929 samples were used to build artificial intelligence models, and 1200 samples were used to test its performance. The image quality improvement module is based on the generative adversarial structure. It improves the quality of the input image under the joint drive of feature loss and content loss. The enhanced image is sent to the disease diagnosis model based on residual convolutional network. It automatically extracts the semantic features of the image and then infers the probability that the sample belongs to COVID-19. The ROC curve is used to evaluate the performance of the model. RESULTS: This model can effectively enhance the low-quality image and make the image that is difficult to be recognized become recognizable. The model proposed in this paper reached 97.8% AUC, 96.97% sensitivity and 91.16% specificity in an independent test set. ResNet, GADCD, CNN, and DenseNet achieved 80.9%, 97.3%, 70.7% and 85.7% AUC in the same test set, respectively. By comparing the performance with related works, it is proved that the model proposed has stronger clinical usability. CONCLUSION: The method proposed can effectively assist doctors in real-time detection of suspected cases of COVID-19 pneumonia even faces unclear image. It can quickly isolate patients in a targeted manner, which is of positive significance for preventing the further spread of COVID-19 pneumonia.

Co-quantification of crAssphage increases confidence in wastewater-based epidemiology for SARS-CoV-2 in low prevalence areas.

Wastewater surveillance of SARS-CoV-2 RNA is increasingly being incorporated into public health efforts to respond to the COVID-19 pandemic. In order to obtain the maximum benefit from these efforts, approaches to wastewater monitoring need to be rapid, sensitive, and relatable to relevant epidemiological parameters. In this study, we present an ultracentrifugation-based method for the concentration of SARS-CoV-2 wastewater RNA and use crAssphage, a bacteriophage specific to the human gut, to help account for RNA loss during transit in the wastewater system and sample processing. With these methods, we were able to detect, and sometimes quantify, SARS-CoV-2 RNA from 20 mL wastewater samples within as little as 4.5 hours. Using known concentrations of bovine coronavirus RNA and deactivated SARS-CoV-2, we estimate recovery rates of approximately 7-12% of viral RNA using our method. Results from 24 sewersheds across Upstate New York during the spring and summer of 2020 suggested that stronger signals of SARS-CoV-2 RNA from wastewater may be indicative of greater COVID-19 incidence in the represented service area approximately one week in advance. SARS-CoV-2 wastewater RNA was quantifiable in some service areas with daily positives tests of less than 1 per 10,000 people or when weekly positive test rates within a sewershed were as low as 1.7%. crAssphage DNA concentrations were significantly lower during periods of high flow in almost all areas studied. After accounting for flow rate and population served, crAssphage levels per capita were estimated to be about 1.35 × 1011 and 2.42 × 108 genome copies per day for DNA and RNA, respectively. A negative relationship between per capita crAssphage RNA and service area size was also observed likely reflecting degradation of RNA over long transit times. Our results reinforce the potential for wastewater surveillance to be used as a tool to supplement understanding of infectious disease transmission obtained by traditional testing and highlight the potential for crAssphage co-detection to improve interpretations of wastewater surveillance data.

Establishment of method for dual simultaneous detection of PEDV and TGEV by combination of magnetic micro-particles and nanoparticles.

Transmissible gastroenteritis virus (TGEV) and porcine epidemic diarrhea virus (PEDV) are the main pathogens causing viral diarrhea in pig, mixed infections of these two viruses are very common in intensive pig rearing. However, there is a lack of a method to simultaneously detect and distinguish PEDV and TGEV in preclinical levels. In this study, we aimed to establish a dual ultrasensitive nanoparticle DNA probe-based PCR assay (dual UNDP-PCR) based on functionalized magnetic bead enrichment and specific nano-technology amplification for simultaneous detection and distinguish diagnosis of PEDV and TGEV. The detection limit of dual UNDP-PCR for single or multiple infections of PEDV and TGEV is 25 copies/g, which is 400 times more sensitive than the currently known duplex RT-PCR, showing better specificity and sensitivity without cross-reaction with other viruses. For pre-clinical fecal samples, the dual UNDP-PCR showed a markedly higher positive detection rate (52.08%) than conventional duplex RT-PCR (13.21%), can rapidly and accurately identify targeted pathogens whenever simple virus infection or co-infection. In summary, this study provides a technique for detecting and distinguishing PEDV and TGEV in preclinical levels, which is high sensitivity, specificity, repeatability, low cost and broad application prospect.

Estimating the distribution trend of soil heavy metals in mining area from HyMap airborne hyperspectral imagery based on ensemble learning

The problem of heavy metal pollution of soils in China is severe. The traditional spectral methods for soil heavy metal monitoring and assessment cannot meet the needs for large-scale areas. Therefore, in this study, we used HyMap-C airborne hyperspectral imagery to explore the estimation of soil heavy metal concentration. Ninety five soil samples were collected synchronously with airborne image acquisition in the mining area of Yitong County, China. The pre-processed spectrum of airborne images at the sampling point was then selected by the competitive adaptive reweighted sampling (CARS) method. The selected spectral features and the heavy metal data of soil samples were inverted to establish the inversion model. An ensemble learning method based on a stacking strategy is proposed for the inversion modeling of soil samples and image data. The experimental results show that this CARS-Stacking method can better predict the four heavy metals in the study area than other methods. For arsenic (As), chromium (Cr), lead (Pb), and zinc (Zn), the determination coefficients of the test data set (RP2) are 0.73, 0.63, 0.60, and 0.71, respectively. It was found that the estimated results and the distribution trend of heavy metals are almost the same as in actual ground measurements.