Long-term post-acute sequelae of COVID-19 infection: a retrospective, multi-database cohort study in Hong Kong and the UK.

Background: Evidence on post-acute sequelae of SARS-CoV-2 (PASC) has shown inconsistent findings. This study aimed to generate coherent evidence on the post-acute sequelae of COVID-19 infection using electronic healthcare records across two regions. Methods: In this retrospective, multi-database cohort study, patients with COVID-19 aged 18 or above between April 1st 2020 and May 31st 2022 from the Hong Kong Hospital Authority (HKHA) and March 16th 2020 and May 31st 2021 from the UK Biobank (UKB) databases and their matched controls were followed for up to 28 and 17 months, respectively. Covariates between patients with COVID-19 and non-COVID-19 controls were adjusted using propensity score-based inverse probability treatment weighting. Cox proportional regression was used to estimate the hazard ratio (HR) of clinical sequelae, cardiovascular, and all-cause mortality 21 days after COVID-19 infection. Findings: A total of 535,186 and 16,400 patients were diagnosed with COVID-19 from HKHA and UKB, of whom 253,872 (47.4%) and 7613 (46.4%) were male, with a mean age (±SD) of 53.6 (17.8) years and 65.0 (8.5) years, respectively. Patients with COVID-19 incurred greater risk of heart failure (HR 1.82; 95% CI 1.65, 2.01), atrial fibrillation (1.31; 1.16, 1.48), coronary artery disease (1.32; 1.07, 1.63), deep vein thrombosis (1.74; 1.27, 2.37), chronic pulmonary disease (1.61; 1.40, 1.85), acute respiratory distress syndrome (1.89; 1.04, 3.43), interstitial lung disease (3.91; 2.36, 6.50), seizure (2.32; 1.12, 4.79), anxiety disorder (1.65; 1.29, 2.09), post-traumatic stress disorder (1.52; 1.23, 1.87), end-stage renal disease (1.76; 1.31, 2.38), acute kidney injury (2.14; 1.69, 2.71), pancreatitis (1.42; 1.10, 1.83), cardiovascular (2.86; 1.25, 6.51) and all-cause mortality (4.16; 2.11, 8.21) mortality during their post-acute phase of infection. Interpretation: The consistent greater risk of PASC highlighted the need for sustained multi-disciplinary care for COVID-19 survivors. Funding: Health Bureau, The Government of the Hong Kong Special Administrative Region, Collaborative Research Fund, The Government of the Hong Kong Special Administrative Region and AIR@InnoHK, administered by the Innovation and Technology Commission, The Government of the Hong Kong Special Administrative Region.

Risk of adverse events and delirium after COVID-19 vaccination in patients living with dementia

OBJECTIVES The aim of this study was to compare incidences of adverse events of special interest (AESI) and delirium in three cohorts: after COVID-19 vaccination, pre-pandemic and SARS-CoV-2 polymerase chain reaction (PCR) test positive. DESIGN This is a population-based cohort study using electronic medical records linked with vaccination records in Hong Kong. SETTING AND PARTICIPANTS A total of 17,449 older people with dementia received at least one dose of CoronaVac (n=14,719) or BNT162b2 (n=2730) between 23 February 2021 and 31 March 2022. 43,396 pre-pandemic and 3592 SARS-CoV-2 test positive patients were also included in this study. METHODS The incidences of AESI and delirium up to 28 days after vaccination in the vaccinated dementia cohort were compared with the pre-pandemic and SARS-CoV-2 test positive dementia cohorts by calculating incidence rate ratios (IRRs). Patients who received multiple doses were followed up separately for each dose, up to the third dose. RESULTS We did not detect an increased risk of delirium and most AESI following vaccination compared to the pre-pandemic period and those tested positive for SARS-CoV-2. No AESI group nor delirium incidence exceeded 10 per 1000 person-days in vaccinated individuals. CONCLUSIONS AND IMPLICATIONS The findings provide evidence for the safe use of COVID-19 vaccines in older patients with dementia. In the short run, benefit appears to outweigh the harm due to vaccine, however, longer follow-up should be continued to identify remote adverse events.

Vital node identification based on cycle structure in a multiplex network.

Abstract: Multiplex networks frame the heterogeneous nature of real systems, where the multiple roles of nodes, both functionally and structurally, are well represented. We identify these vital nodes in a multiplex network so that we can control a pandemic outbreak like COVID-19, eliminate damage from a network attack, maintain traffic, and so on. Vital node identification has attracted scientists in various fields for decades. In this paper, we propose a hybrid supra-cycle number and hybrid supra-cycle ratio based on the cycle structure, and present an extensive experimental analysis by comparing our indexes and several different indexes in four real multiplex networks on layer nodes and multiplex nodes. The experimental results show that these proposed indexes have good robustness, synchronization, and transmission dynamics. Finally, we provide an in-depth understanding of multiplex networks and cycle structure, and we sincerely hope more valuable academic achievements are proposed in the future.

Rates of Antipsychotic Drug Prescribing Among People Living With Dementia During the COVID-19 Pandemic.

Importance: Concerns have been raised that the use of antipsychotic medication for people living with dementia might have increased during the COVID-19 pandemic. Objective: To examine multinational trends in antipsychotic drug prescribing for people living with dementia before and during the COVID-19 pandemic. Design, Setting, and Participants: This multinational network cohort study used electronic health records and claims data from 8 databases in 6 countries (France, Germany, Italy, South Korea, the UK, and the US) for individuals aged 65 years or older between January 1, 2016, and November 30, 2021. Two databases each were included for South Korea and the US. Exposures: The introduction of population-wide COVID-19 restrictions from April 2020 to the latest available date of each database. Main Outcomes and Measures: The main outcomes were yearly and monthly incidence of dementia diagnosis and prevalence of people living with dementia who were prescribed antipsychotic drugs in each database. Interrupted time series analyses were used to quantify changes in prescribing rates before and after the introduction of population-wide COVID-19 restrictions. Results: A total of 857 238 people with dementia aged 65 years or older (58.0% female) were identified in 2016. Reductions in the incidence of dementia were observed in 7 databases in the early phase of the pandemic (April, May, and June 2020), with the most pronounced reduction observed in 1 of the 2 US databases (rate ratio [RR], 0.30; 95% CI, 0.27-0.32); reductions were also observed in the total number of people with dementia prescribed antipsychotic drugs in France, Italy, South Korea, the UK, and the US. Rates of antipsychotic drug prescribing for people with dementia increased in 6 databases representing all countries. Compared with the corresponding month in 2019, the most pronounced increase in 2020 was observed in May in South Korea (Kangwon National University database) (RR, 2.11; 95% CI, 1.47-3.02) and June in the UK (RR, 1.96; 95% CI, 1.24-3.09). The rates of antipsychotic drug prescribing in these 6 databases remained high in 2021. Interrupted time series analyses revealed immediate increases in the prescribing rate in Italy (RR, 1.31; 95% CI, 1.08-1.58) and in the US Medicare database (RR, 1.43; 95% CI, 1.20-1.71) after the introduction of COVID-19 restrictions. Conclusions and Relevance: This cohort study found converging evidence that the rate of antipsychotic drug prescribing to people with dementia increased in the initial months of the COVID-19 pandemic in the 6 countries studied and did not decrease to prepandemic levels after the acute phase of the pandemic had ended. These findings suggest that the pandemic disrupted the care of people living with dementia and that the development of intervention strategies is needed to ensure the quality of care.

Development and experimental validation of an improved mathematical irradiance model for in-duct ultraviolet germicidal irradiation (UVGI) applications

The application of ultraviolet germicidal irradiation (UVGI) technology inside the heating, ventilation, and air-conditioning (HVAC) air ducts to purify circulating air and improve indoor air quality has attracted extensive interest during the COVID-19 pandemic. In this study, a new view-factor-based mathematical model was developed to calculate the irradiation distribution for a typical twin-tube UV lamp placed at the center of a square duct, in which the contributions from direct emissive irradiance, specular reflection irradiance, and diffuse reflection irradiance were quantified. Furthermore, the “projection area” method was introduced to mathematically estimate the shadowing effects between the two lamps by considering multiple-lamp scenarios in real in-duct UVGI system designs. Subsequently, a computational fluid dynamics (CFD) simulation was employed to compute the average received UV dose and disinfection efficiency of the system. The mathematical model combined with the CFD simulation was validated using the experimental data. It is concluded that by increasing the UV lamps, UV lamp power, and using more reflective duct wall materials, the in-duct UVGI disinfection performance can be improved. For the multiple-lamp arrangements, placing lamps perpendicular to the airflow in the same row results in a more uniform irradiance distribution and higher overall irradiation than placing them in different rows along the duct, thus increasing the disinfection efficiency. In addition, the duct wall with highly diffusive reflection provides a more uniform irradiance distribution and overall higher average radiation, thus providing better disinfection performance for an in-duct UVGI reactor.

Trafficked Malayan pangolins contain viral pathogens of humans.

Pangolins are the most trafficked wild animal in the world according to the World Wildlife Fund. The discovery of SARS-CoV-2-related coronaviruses in Malayan pangolins has piqued interest in the viromes of these wild, scaly-skinned mammals. We sequenced the viromes of 161 pangolins that were smuggled into China and assembled 28 vertebrate-associated viruses, 21 of which have not been previously reported in vertebrates. We named 16 members of Hunnivirus, Pestivirus and Copiparvovirus pangolin-associated viruses. We report that the L-protein has been lost from all hunniviruses identified in pangolins. Sequences of four human-associated viruses were detected in pangolin viromes, including respiratory syncytial virus, Orthopneumovirus, Rotavirus A and Mammalian orthoreovirus. The genomic sequences of five mammal-associated and three tick-associated viruses were also present. Notably, a coronavirus related to HKU4-CoV, which was originally found in bats, was identified. The presence of these viruses in smuggled pangolins identifies these mammals as a potential source of emergent pathogenic viruses.

Explainable multi-instance and multi-task learning for COVID-19 diagnosis and lesion segmentation in CT images.

Coronavirus Disease 2019 (COVID-19) still presents a pandemic trend globally. Detecting infected individuals and analyzing their status can provide patients with proper healthcare while protecting the normal population. Chest CT (computed tomography) is an effective tool for screening of COVID-19. It displays detailed pathology-related information. To achieve automated COVID-19 diagnosis and lung CT image segmentation, convolutional neural networks (CNNs) have become mainstream methods. However, most of the previous works consider automated diagnosis and image segmentation as two independent tasks, in which some focus on lung fields segmentation and the others focus on single-lesion segmentation. Moreover, lack of clinical explainability is a common problem for CNN-based methods. In such context, we develop a multi-task learning framework in which the diagnosis of COVID-19 and multi-lesion recognition (segmentation of CT images) are achieved simultaneously. The core of the proposed framework is an explainable multi-instance multi-task network. The network learns task-related features adaptively with learnable weights, and gives explicable diagnosis results by suggesting local CT images with lesions as additional evidence. Then, severity assessment of COVID-19 and lesion quantification are performed to analyze patient status. Extensive experimental results on real-world datasets show that the proposed framework outperforms all the compared approaches for COVID-19 diagnosis and multi-lesion segmentation.

The Prevalence and Progression of Myopia in Elementary School Students in Shanxi Province, China During the COVID-19 Pandemic.

PURPOSE: To investigate the prevalence of myopia and the risk factors associated with its progression in elementary school students during the COVID-19 pandemic in Shanxi Province, China. METHODS: The investigation included 960 students spanning first to sixth grade from six elementary schools in Shanxi Province, China. All participants received non-cycloplegic refraction and vision tests in December of 2019 (before the COVID-19 pandemic) and in June of 2020 (after classes resumed). Information concerning the students' eye-use behaviors, physical activities, diet and sleep during the pandemic was collected using a questionnaire survey. A total of 913 students (457 males) completed all tests and the questionnaire. RESULTS: The overall prevalence rate of myopia was 16.6% in December of 2019, and it increased with age. There was no gender difference in the prevalence of myopia (χ2 = 3.210, P = .073), but females exhibited a lower average spherical equivalent (SE) (P = .026). When the classes were resumed 6 months later, the overall prevalence rate of myopia was found to be 39.4%, which was significantly higher than it before the pandemic (χ2 = 117.425, P < .001). The average SE of the participants was -0.95D, which was significantly lower than the average SE (-0.43D) before the pandemic (P < .001). SE variation (ΔSE) in grade 6 was significantly higher than that in grade 1. No significant difference in ΔSE was found between males and females. Analyses of ordinary least squares (OLS)-estimated linear, natural logarithmic and quadratic functions revealed that the progression of myopia during the COVID-19 pandemic was significantly correlated with screen time, types of electronic devices, the amount of sleep, age, and the number of parents with myopia. CONCLUSIONS: The prevalence rate and progression of myopia among elementary school students in Shanxi Province increased significantly during the COVID-19 pandemic, which was likely related to China's home-based online learning programs. Therefore, it is necessary to optimize the educational programs for elementary school students when they study at home. We recommend increased time for outdoor activities and limiting screen time.

Evaluation of the association of a variant in PNPLA3 and TM6SF2 with fibrosis progression in patients with chronic hepatitis C infection after eradication: A retrospective study.

The relationship of single nucleotide polymorphisms (SNPs) in patatin-like phospholipase domain containing 3 (PNPLA3) rs738409, transmembrane 6 superfamily member 2 (TM6SF2) rs58542926, and membrane bound O-acyltransferase domain containing 7 (MBOAT7) rs641738 with outcomes in patients with hepatitis C infection (HCV) is unclear. This study aimed to evaluate the association of PNPLA3, TM6SF2, and MBOAT7 with the baseline fibrosis stage and progression of liver fibrosis after HCV eradication with direct antiviral agents (DAAs). A total of 171 patients who received the DAAs at the Peking University First Hospital between June 2015 and June 2020 were included in the retrospective cohort. Transient elastography was used to determine liver stiffness measurements (LSMs) at the baseline, the end of treatment (EOT), 24 weeks after treatment (W24), and the last follow-up (LFU) visit. We used the QIAamp Blood Mini Kit (Qiagen) for whole blood genomic DNA extraction and polymerase chain reaction for PNPLA3, TM6SF2, and MBOAT7 amplification of the target gene. The PNPLA3 rs738409 SNP was associated with the baseline fibrosis stage in multivariate logistic regression analysis adjusted for other factors, and the adjusted odds ratio (OR) for advanced fibrosis (≥F3) at baseline was 2.52 (95% confidence interval[CI] = 1.096-5.794, p = 0.03). The G and GG alleles were predictive of advanced fibrosis (OR = 1.98, 95% CI = 1.021-4.196, p = 0.015; OR = 3.12, 95% CI = 1.572-6.536, p = 0.005). Similarly, the OR of TM6SF2 rs58542926 at baseline was 2.608 (95% CI = 1.081-6.29, p = 0.033). T and TT alleles were predictive of advanced fibrosis (OR = 2.3, 95% CI = 1.005-5.98, p = 0.007; OR = 3.05, 95% CI = 1.32-6.87, p = 0.001). After adjustment, the MBOAT7 rs641738 T plus TT alleles were not independently associated with the baseline fibrosis stage (95% CI = 0.707-2.959, p = 0.312). At the EOT, there were 35 patients and 136 patients in the fibrosis improvement and fibrosis non-improvement group, respectively. Logistic regression analysis showed that the G allele in PNPLA3 rs738409 was associated with fibrosis progression (OR = 2.47, 95% CI = 1.125-5.89, p = 0.003). The GG alleles were predictive of fibrosis progression (OR = 2.95, 95% CI = 1.35-6.35, p = 0.005). Similarly, the ORs of the T and TT alleles in TM6SF2 rs58542926 for fibrosis progression were 1.82 and 2.21, respectively (95% CI = 1.006-5.373, p = 0.045; 95% CI = 1.18-5.75, p = 0.01). At the W24 visit, we found that there was an association between the G allele in PNPLA3 rs738409 and fibrosis progression (OR = 2.218, 95% CI = 1.095-5.631, p = 0.015). Moreover, GG alleles were also predictive for fibrosis progression (OR = 2.558, 95% CI = 1.252-5.15, p = 0.008). Similarly, the OR of T allele and TT alleles in TM6SF2 rs58542926 for fibrosis progression was 2.056 and 2.652 (95% CI = 1.013-5.592, p = 0.038; 95% CI = 1.25-5.956, p = 0.015). For additional affirmation, we surveyed fibrosis progression utilizing the Cox proportional hazards model. G and GG alleles in PNPLA3 rs738409 were associated with an increased risk of progression to advanced fibrosis in multivariate model (hazard ratio [HR]1.566, 95% CI = 1.02-2.575, p = 0.017; and HR2.109, 95% CI = 1.36-3.271, p = 0.001, respectively). Besides, T and TT alleles in TM6SF2 rs58542926 were associated with an increased risk of progression to advanced fibrosis in multivariate model (HR = 1.322, 95% CI = 1.003-1.857, p = 0.045; and HR = 1.855, 95% CI = 1.35-2.765, p = 0.006, respectively). In contrast, rs641738 in MBOAT7 did not show a significant trend in the univariate and multivariate models. The PNPLA3 CG/GG SNP at rs738409 and TM6SF2 CT/TT SNP at rs58542926 were associated with the baseline fibrosis stage and fibrosis progression after HCV eradication with DAAs.

Multimorbidity and adverse events of special interest associated with Covid-19 vaccines in Hong Kong.

Prior research using electronic health records for Covid-19 vaccine safety monitoring typically focuses on specific disease groups and excludes individuals with multimorbidity, defined as ≥2 chronic conditions. We examine the potential additional risk of adverse events 28 days after the first dose of CoronaVac or Comirnaty imposed by multimorbidity. Using a territory-wide public healthcare database with population-based vaccination records in Hong Kong, we analyze a retrospective cohort of patients with chronic conditions. Thirty adverse events of special interest according to the World Health Organization are examined. In total, 883,416 patients are included and 2,807 (0.3%) develop adverse events. Results suggest vaccinated patients have lower risks of adverse events than unvaccinated individuals, multimorbidity is associated with increased risks regardless of vaccination, and the association of vaccination with adverse events is not modified by multimorbidity. To conclude, we find no evidence that multimorbidity imposes extra risks of adverse events following Covid-19 vaccination.

Clinical Applicable AI System Based on Deep Learning Algorithm for Differentiation of Pulmonary Infectious Disease.

Objective: To assess the performance of a novel deep learning (DL)-based artificial intelligence (AI) system in classifying computed tomography (CT) scans of pneumonia patients into different groups, as well as to present an effective clinically relevant machine learning (ML) system based on medical image identification and clinical feature interpretation to assist radiologists in triage and diagnosis. Methods: The 3,463 CT images of pneumonia used in this multi-center retrospective study were divided into four categories: bacterial pneumonia (n = 507), fungal pneumonia (n = 126), common viral pneumonia (n = 777), and COVID-19 (n = 2,053). We used DL methods based on images to distinguish pulmonary infections. A machine learning (ML) model for risk interpretation was developed using key imaging (learned from the DL methods) and clinical features. The algorithms were evaluated using the areas under the receiver operating characteristic curves (AUCs). Results: The median AUC of DL models for differentiating pulmonary infection was 99.5% (COVID-19), 98.6% (viral pneumonia), 98.4% (bacterial pneumonia), 99.1% (fungal pneumonia), respectively. By combining chest CT results and clinical symptoms, the ML model performed well, with an AUC of 99.7% for SARS-CoV-2, 99.4% for common virus, 98.9% for bacteria, and 99.6% for fungus. Regarding clinical features interpreting, the model revealed distinctive CT characteristics associated with specific pneumonia: in COVID-19, ground-glass opacity (GGO) [92.5%; odds ratio (OR), 1.76; 95% confidence interval (CI): 1.71-1.86]; larger lesions in the right upper lung (75.0%; OR, 1.12; 95% CI: 1.03-1.25) with viral pneumonia; older age (57.0 years ± 14.2, OR, 1.84; 95% CI: 1.73-1.99) with bacterial pneumonia; and consolidation (95.8%, OR, 1.29; 95% CI: 1.05-1.40) with fungal pneumonia. Conclusion: For classifying common types of pneumonia and assessing the influential factors for triage, our AI system has shown promising results. Our ultimate goal is to assist clinicians in making quick and accurate diagnoses, resulting in the potential for early therapeutic intervention.

Containment, Health, and Social Policies in the Time of COVID-19 - Determinants and Outcomes of Initial Responses across 120 Countries.

The coronavirus disease 2019 (COVID-19) pandemic has triggered an unprecedented number of policy responses around the world across multiple policy domains. While governments have combined containment and health policies with social policies (CHSPs) during the initial phase of the pandemic in various ways, the current literature offers little knowledge of the patterns of these combinations and their determinants and outcomes. This paper fills this gap by investigating CHSP combinations across ≥120 countries. We further examined whether the CHSP response was determined by political regimes or compensation hypotheses-serving the purposes of responding to pre-existing economic downturns, inequality or social unrest. We also investigated the associations between CHSP responses and mobility, virus infection and unemployment. Using policy data from the Oxford COVID-19 Government Response Tracker, results from sequence analysis indicated that governments' CHSP responses could be clustered into five categories: high social policies (SPs), middle SPs, containment and health (CH) leading SPs, low SPs and gradual high SPs. We used multinomial regression models to investigate determinants of CHSP responses. We found that CHSP responses did not differ by political regimes, and CHSP combinations were not driven by compensation hypotheses. Instead, gross domestic product per capita and government effectiveness were the key drivers for high levels of policy responses. We also found that low SP responses were associated with fewer mobility changes. Taken together, our findings suggest that lower-income countries required more support and resources in order for them to adopt necessary CH and SP responses.

Data-driven identification of SARS-CoV-2 subpopulations using PhenoGraph and binary-coded genomic data.

For epidemic prevention and control, the identification of SARS-CoV-2 subpopulations sharing similar micro-epidemiological patterns and evolutionary histories is necessary for a more targeted investigation into the links among COVID-19 outbreaks caused by SARS-CoV-2 with similar genetic backgrounds. Genomic sequencing analysis has demonstrated the ability to uncover viral genetic diversity. However, an objective analysis is necessary for the identification of SARS-CoV-2 subpopulations. Herein, we detected all the mutations in 186 682 SARS-CoV-2 isolates. We found that the GC content of the SARS-CoV-2 genome had evolved to be lower, which may be conducive to viral spread, and the frameshift mutation was rare in the global population. Next, we encoded the genomic mutations in binary form and used an unsupervised learning classifier, namely PhenoGraph, to classify this information. Consequently, PhenoGraph successfully identified 303 SARS-CoV-2 subpopulations, and we found that the PhenoGraph classification was consistent with, but more detailed and precise than the known GISAID clades (S, L, V, G, GH, GR, GV and O). By the change trend analysis, we found that the growth rate of SARS-CoV-2 diversity has slowed down significantly. We also analyzed the temporal, spatial and phylogenetic relationships among the subpopulations and revealed the evolutionary trajectory of SARS-CoV-2 to a certain extent. Hence, our results provide a better understanding of the patterns and trends in the genomic evolution and epidemiology of SARS-CoV-2.

Fast SARS-CoV-2 virus detection using disposable cartridge strips and a semiconductor-based biosensor platform.

Detection of the SARS-CoV-2 spike protein and inactivated virus was achieved using disposable and biofunctionalized functional strips, which can be connected externally to a reusable printed circuit board for signal amplification with an embedded metal-oxide-semiconductor field-effect transistor (MOSFET). A series of chemical reactions was performed to immobilize both a monoclonal antibody and a polyclonal antibody onto the Au-plated electrode used as the sensing surface. An important step in the biofunctionalization, namely, the formation of Au-plated clusters on the sensor strips, was verified by scanning electron microscopy, as well as electrical measurements, to confirm successful binding of thiol groups on this Au surface. The functionalized sensor was externally connected to the gate electrode of the MOSFET, and synchronous pulses were applied to both the sensing strip and the drain contact of the MOSFET. The resulting changes in the dynamics of drain waveforms were converted into analog voltages and digital readouts, which correlate with the concentration of proteins and virus present in the tested solution. A broad range of protein concentrations from 1 fg/ml to 10 µg/ml and virus concentrations from 100 to 2500 PFU/ml were detectable for the sensor functionalized with both antibodies. The results show the potential of this approach for the development of a portable, low-cost, and disposable cartridge sensor system for point-of-care detection of viral diseases.

Ultraviolet germicidal irradiation (UVGI) for in-duct airborne bioaerosol disinfection: Review and analysis of design factors.

The rapid increase in global cases of COVID-19 illness and death requires the implementation of appropriate and efficient engineering controls to improve indoor air quality. This paper focuses on the use of the ultraviolet germicidal irradiation (UVGI) air purification technology in HVAC ducts, which is particularly applicable to buildings where fully shutting down air recirculation is not feasible. Given the poor understanding of the in-duct UVGI system regarding its working mechanisms, designs, and applications, this review has the following key research objectives:•Identifying the critical parameters for designing a UVGI system, including the characterization of lamp output, behavior of the target microbial UV dose-response, and evaluation of the inactivation performance and energy consumption.•Elucidating the effects of environmental factors (air velocity, air temperature, and humidity) on the UVGI system design parameters and optimization of the in-duct UVGI design.•Summarizing existing UVGI system designs in the literature and illustrating their germicidal and energy performance in light of COVID-19 mitigation.

Development and application of an indirect enzyme-linked immunosorbent assay using recombinant S1 for serological testing of porcine epidemic diarrhea virus

Porcine epidemic diarrhea virus (PEDV) causes severe infectious diseases in all ages of swine and leads to serious economic losses. Serologic tests are widely accepted and used to detect anti-PEDV antibodies that could indicate PEDV infection or vaccination. In this study, PEDV recombinant S1 protein (rS1) was expressed with the Bac-to-Bac system and purified by nickel-affinity chromatography. An indirect enzyme-linked immunosorbent assay based on rS1 (rS1-ELISA) was then developed and optimized by checkerboard assays with serial dilutions of antigen and serum. Serum samples from 453 domestic pigs and 42 vaccinated pigs were analyzed by the indirect fluorescent antibody (IFA) test and rS1-ELISA. Taking IFA as a gold standard, rS1-ELISA produced a high sensitivity (90.7%) and specificity (94.6%) by a receiver operating characteristic (ROC) curve. In addition, ROC analysis also revealed that rS1-ELISA was consistent with IFA (area under the curve 0.9583 +or- 0.0082). This rS1-ELISA was then applied to antibody detection in inactivated PEDV vaccinated pigs. The antibody could be detected 2-4 weeks after the first inoculation. These results indicated that the rS1-ELISA established in this study provides a promising and reliable tool for serologic detection of anti-PEDV IgG antibodies in infected or vaccinated pigs.

Prokaryotic expression of porcine type III interferon and studying on its antiviral activity

Normally, type III interferon (IFN-lambda) was highly expressed in intestinal mucosa and other mucosal systems, with relatively stronger broad-spectrum antiviral ability and immune regulating ability. Porcine epidemic diarrhea virus (PEDV) and porcine deltacorona virus (PDCoV) were two major intestinal pathogens causing diarrhea in piglets, which hindered the swine industry severely. In this study, the recombinant plasmid containing PoIFN-lambda3 fragment was constructed. The recombinant plasmid was then transformed into E.coli Rosaetta cells for recombinant expression. Analyzed by SDS-PAGE and western blotting, the results showed that the recombinant PoIFN-lambda3 (27 ku) was successfully expressed in the inclusion bodies of E. coli cells. After denaturation, purification and renaturation, the active PoIFN-lambda3 recombinant protein was obtained and was used to treat IPEC-J2 cells. Challenged by recombinant PoIFN-lambda3, the immune-stimulating genes (ISGs), such as ISG15-, OAS1, Mx-1, IFIT1, IFITM1 and IFITM3 were all up-regulated significantly and reached to the peak (P 0.001) at 12 h post challenge. For PEDV and PDCoV infection in IPEC-J2, the virus replication was detected after pretreatment, simultaneous treatment and post-infection treatment of recombinant PoIFN-lambda3. Compared with the control, the viral copy numbers of both PEDV and PDCoV were decreased significantly (P 0.05) after treatment of recombinant PoIFN-lambda3. The above results indicated that the purified and renatured PoIFN-lambda3 recombinant protein could have good biological activity. The recombinant PoIFN-lambda3 can induce high expression of different ISGs in IPEC-J2, and can also inhibit PEDV and PDCoV replication in the host cells. In summary, our study provided a basis for preventing and treating viral diarrhea in piglets.

Utility Evaluation of Porcine Enteroids as PDCoV Infection Model in vitro.

Porcine deltacoronavirus (PDCoV) is a novel emerging enteric coronavirus found in pigs. Intestinal enteroids, which partially recreate the structure and function of intestinal villi-crypts, have many physiological similarities to the intestinal tissues in vivo. Enteroids exhibit advantages in studying the interactions between intestines and enteric pathogens. To create a novel infection model for PDCoV, we developed an in vitro system to generate porcine intestinal enteroids from crypts of duodenum, jejunum, and ileum of pigs. Enterocytes, enteroendocrine cells, Paneth cells, stem cells, proliferating cells, and goblet cells were found in the differentiated enteroids. Replication of PDCoV was detected in the cultured enteroids by immunofluorescence and quantitative RT-PCR. Double immunofluorescence labeling demonstrated that PDCoV was present in Sox9-positive intestinal cells and Villin1-positive enterocytes. There were multiple cellular responses shown as changes of transcription of genes related to mucosal immunity, antiviral genes, and marker genes of stem cells and other cells in the enteroids infected with PDCoV. We conclude that the 2-D enteroids derived from porcine jejunum can be used as an in vitro multicellular model for the investigation of pathogenesis and host immune responses to porcine enteric pathogens, such as PDCoV.