Establishment and application of triple PCR method for CPV, CCoV and CRV

Canine parvovirus (CPV), canine coronavirus (CCoV) and canine rotavirus (CRV) are the three main causative viruses of diarrhea in dogs with similar clinical symptoms;thereby it is necessary to establish a high effective molecular detection method for differentiating the above pathogens. By optimizing the primer concentration and annealing temperature, a triple PCR method was established for simultaneous detection of CPV, CCoV and CRV, and then the specificity, sensitivity and repeatability of the method were tested. The results showed that the target fragments of CPV VP2 gene (253 bp), CCoV ORF-1b gene (379 bp) and CRV VP6 gene (852 bp) could be accurately amplified by the triple PCR method with high specificity, the detection limits of CPV, CCOV and CRV were 6.44x10-1 pg/L, 8.72x10-1 pg/L and 8.35x10-1 pg/L respectively with high sensitivity, and the method had good stability. Using this triple PCR method, 135 canine diarrhea fecal samples collected in Chengdu region from 2019 to 2020 were detected, and compared with those of single PCR method. The detection rates of CPV, CCoV and CRV were 16.30%, 20.74% and 4.44%, respectively, and the total infection rate was 51.11% (65/135) with 20.00% (13/65) co-infection rate. The detection results were consistent with three single PCR methods. In conclusion, CPV/CCoV/CRV triple PCR method successfully established in this paper can be applied as an effective molecular method to detection of related pathogens and to the epidemiological investigation.

Vaccine-induced protection against SARS-CoV-2 requires IFN-γ-driven cellular immune response.

The overall success of worldwide mass vaccination in limiting the negative effect of the COVID-19 pandemics is inevitable, however, recent SARS-CoV-2 variants of concern, especially Omicron and its sub-lineages, efficiently evade humoral immunity mounted upon vaccination or previous infection. Thus, it is an important question whether these variants, or vaccines against them, induce anti-viral cellular immunity. Here we show that the mRNA vaccine BNT162b2 induces robust protective immunity in K18-hACE2 transgenic B-cell deficient (µMT) mice. We further demonstrate that the protection is attributed to cellular immunity depending on robust IFN-γ production. Viral challenge with SARS-CoV-2 Omicron BA.1 and BA.5.2 sub-variants induce boosted cellular responses in vaccinated µMT mice, which highlights the significance of cellular immunity against the ever-emerging SARS-CoV-2 variants evading antibody-mediated immunity. Our work, by providing evidence that BNT162b2 can induce significant protective immunity in mice that are unable to produce antibodies, thus highlights the importance of cellular immunity in the protection against SARS-CoV-2.

A real-world data validation of the value of early-stage SIR modelling to public health.

Performance of Susceptible-Infected-Recovered (SIR) model in the early stage of a novel epidemic may be hindered by data availability. Additionally, the traditional SIR model may oversimplify the disease progress, and knowledge about the virus and transmission is limited early in the epidemic, resulting in a greater uncertainty of such modelling. We aimed to investigate the impact of model inputs on the early-stage SIR projection using COVID-19 as an illustration to evaluate the application of early infection models. We constructed a modified SIR model using discrete-time Markov chain to simulate daily epidemic dynamics and estimate the number of beds needed in Wuhan in the early stage of COVID-19 epidemic. We compared eight scenarios of SIR projection to the real-world data (RWD) and used root mean square error (RMSE) to assess model performance. According to the National Health Commission, the number of beds occupied in isolation wards and ICUs due to COVID-19 in Wuhan peaked at 37,746. In our model, as the epidemic developed, we observed an increasing daily new case rate, and decreasing daily removal rate and ICU rate. This change in rates contributed to the growth in the needs of bed in both isolation wards and ICUs. Assuming a 50% diagnosis rate and 70% public health efficacy, the model based on parameters estimated using data from the day reaching 3200 to the day reaching 6400 cases returned a lowest RMSE. This model predicted 22,613 beds needed in isolation ward and ICU as on the day of RWD peak. Very early SIR model predictions based on early cumulative case data initially underestimated the number of beds needed, but the RMSEs tended to decline as more updated data were used. Very-early-stage SIR model, although simple but convenient and relatively accurate, is a useful tool to provide decisive information for the public health system and predict the trend of an epidemic of novel infectious disease in the very early stage, thus, avoiding the issue of delay-decision and extra deaths.

Development of an amplicon-based sequencing approach in response to the global emergence of mpox.

The 2022 multicountry mpox outbreak concurrent with the ongoing Coronavirus Disease 2019 (COVID-19) pandemic further highlighted the need for genomic surveillance and rapid pathogen whole-genome sequencing. While metagenomic sequencing approaches have been used to sequence many of the early mpox infections, these methods are resource intensive and require samples with high viral DNA concentrations. Given the atypical clinical presentation of cases associated with the outbreak and uncertainty regarding viral load across both the course of infection and anatomical body sites, there was an urgent need for a more sensitive and broadly applicable sequencing approach. Highly multiplexed amplicon-based sequencing (PrimalSeq) was initially developed for sequencing of Zika virus, and later adapted as the main sequencing approach for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Here, we used PrimalScheme to develop a primer scheme for human monkeypox virus that can be used with many sequencing and bioinformatics pipelines implemented in public health laboratories during the COVID-19 pandemic. We sequenced clinical specimens that tested presumptively positive for human monkeypox virus with amplicon-based and metagenomic sequencing approaches. We found notably higher genome coverage across the virus genome, with minimal amplicon drop-outs, in using the amplicon-based sequencing approach, particularly in higher PCR cycle threshold (Ct) (lower DNA titer) samples. Further testing demonstrated that Ct value correlated with the number of sequencing reads and influenced the percent genome coverage. To maximize genome coverage when resources are limited, we recommend selecting samples with a PCR Ct below 31 Ct and generating 1 million sequencing reads per sample. To support national and international public health genomic surveillance efforts, we sent out primer pool aliquots to 10 laboratories across the United States, United Kingdom, Brazil, and Portugal. These public health laboratories successfully implemented the human monkeypox virus primer scheme in various amplicon sequencing workflows and with different sample types across a range of Ct values. Thus, we show that amplicon-based sequencing can provide a rapidly deployable, cost-effective, and flexible approach to pathogen whole-genome sequencing in response to newly emerging pathogens. Importantly, through the implementation of our primer scheme into existing SARS-CoV-2 workflows and across a range of sample types and sequencing platforms, we further demonstrate the potential of this approach for rapid outbreak response.

RT-nestRPA is a new technology for the rapid and sensitive detection of nucleic acid detection of pathogens used for a variety of medical application scenarios

Background The effective detection of pathogens is of great importance for the diagnosis and treatment of infectious diseases. We have proposed the novel RT-nestRPA technique for SARS-CoV-2 detection, which is a rapid RNA detection technique with ultra-high sensitivity. Results The RT-nestRPA technology has a sensitivity of 0.5 copies/uL of synthetic RNA targeting the ORF7a/7b/8 gene or 1 copy/uL synthetic RNA targeting the N gene of SARS-CoV-2. The entire detection process of RT-nestRPA only takes only 20 min, which is significantly shorter than RT-qPCR (nearly 100 min). Additionally, RT-nestRPA is capable of detecting dual genes of SARS-CoV-2 and human RPP30 simultaneously in one reaction tube. The excellent specificity of RT-nestRPA was verified by analyzing twenty-two SARS-CoV-2 unrelated pathogens. Furthermore, RT-nestRPA had great performance in detecting samples treated with cell lysis buffer without RNA extraction. The innovative double-layer reaction tube for RT-nestRPA can prevent aerosol contamination and simplify the reaction operation. Moreover, the ROC analysis revealed that RT-nestRPA had high diagnostic value (AUC = 0.98), while the AUC of RT-qPCR was 0.75. Significance Our current findings suggested that RT-nestRPA could serve as a novel technology for nucleic acid detection of pathogens with rapid and ultrahigh sensitive features used in various medical application scenarios. Graphical Image 1

Mortality risk analysis for patients with severe coronavirus disease 2019 pneumonia

BACKGROUND: Coronavirus Disease 2019 (COVID-19) is currently a global pandemic. Information about predicting mortality in severe COVID-19 remains unclear. METHODS: A total of 151 COVID-19 in-patients from January 23 to March 8, 2020, were divided into severe and critically severe groups and survival and mortality groups. Differences in the clinical and imaging data between the groups were analyzed. Factors associated with COVID-19 mortality were analyzed by logistic regression, and a mortality prediction model was developed. RESULTS: Many clinical and imaging indices were significantly different between groups, including age, epidemic history, medical history, duration of symptoms before admission, routine blood parameters, inflammatory-related factors, Na+, myocardial zymogram, liver and renal function, coagulation function, fraction of inspired oxygen and complications. The proportions of patients with imaging Stage III and a comprehensive computed tomography score were significantly increased in the mortality group. Factors in the prediction model included patient age, cardiac injury, acute kidney injury, and acute respiratory distress syndrome. The area under the receiver operating characteristic curve of the prediction model was 0.9593. CONCLUSIONS: The clinical and imaging data reflected the severity of COVID-19 pneumonia. The mortality prediction model might be a promising method to help clinicians quickly identify COVID-19 patients who are at high risk of death.

Anatomically Designed Triboelectric Wristbands with Adaptive Accelerated Learning for Human-Machine Interfaces.

Recent advances in flexible wearable devices have boosted the remarkable development of devices for human-machine interfaces, which are of great value to emerging cybernetics, robotics, and Metaverse systems. However, the effectiveness of existing approaches is limited by the quality of sensor data and classification models with high computational costs. Here, a novel gesture recognition system with triboelectric smart wristbands and an adaptive accelerated learning (AAL) model is proposed. The sensor array is well deployed according to the wrist anatomy and retrieves hand motions from a distance, exhibiting highly sensitive and high-quality sensing capabilities beyond existing methods. Importantly, the anatomical design leads to the close correspondence between the actions of dominant muscle/tendon groups and gestures, and the resulting distinctive features in sensor signals are very valuable for differentiating gestures with data from 7 sensors. The AAL model realizes a 97.56% identification accuracy in training 21 classes with only one-third operands of the original neural network. The applications of the system are further exploited in real-time somatosensory teleoperations with a low latency of <1 s, revealing a new possibility for endowing cyber-human interactions with disruptive innovation and immersive experience.

Virological features and pathogenicity of SARS-CoV-2 Omicron BA.2.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron BA.2 was a dominant circulating SARS-CoV-2 variant worldwide. Recent reports hint that BA.2 is similarly potent regarding antibody evasion but may be more transmissible than BA.1. The pathogenicity of BA.2 remains unclear and is of critical public health significance. Here we investigated the virological features and pathogenicity of BA.2 with in vitro and in vivo models. We show that BA.2 is less dependent on transmembrane protease serine 2 (TMPRSS2) for virus entry in comparison with BA.1 in vitro. In K18-hACE2 mice, BA.2 replicates more efficiently than BA.1 in the nasal turbinates and replicates marginally less efficiently in the lungs, leading to decreased body weight loss and improved survival. Our study indicates that BA.2 is similarly attenuated in lungs compared with BA.1 but is potentially more transmissible because of its better replication at the nasal turbinates.

Anti-SARS-CoV-2 spike immunoglobulin G and immunoglobulin M titers decline as interval from the second inactivated vaccine dose to the onset of illness is prolonged in breakthrough infection patients.

BACKGROUND: Understanding of the early immune response in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) breakthrough infections is limited. METHODS: Ninety-eight patients with coronavirus disease 2019 (COVID-19) breakthrough infections were divided into two groups, with intervals from receiving the second dose of inactivated vaccine to the onset of illness <60 or ≥60 days. RESULTS: The median lymphocyte count and the median anti-SARS-CoV-2 spike immunoglobulin G (IgG) and immunoglobulin M (IgM) titers were higher in the <60-day interval group compared with the corresponding medians in the ≥60-day interval group (p = 0.005, p = 0.001, and p = 0.001, respectively). The median interleukin-6 (IL-6) level in the <60-day interval group was significantly lower than the median IL-6 level in the ≥60-day interval group (p < 0.001). CONCLUSIONS: Our results highlight the different anti-SARS-CoV-2 spike IgG and IgM antibody titers among patients with different intervals from receiving the second dose of inactivated vaccine to the onset of illness.

Understanding Health Communication Through Google Trends and News Coverage for COVID-19: Multinational Study in Eight Countries.

BACKGROUND: Due to the COVID-19 pandemic, health information related to COVID-19 has spread across news media worldwide. Google is among the most used internet search engines, and the Google Trends tool can reflect how the public seeks COVID-19-related health information during the pandemic. OBJECTIVE: The aim of this study was to understand health communication through Google Trends and news coverage and to explore their relationship with prevention and control of COVID-19 at the early epidemic stage. METHODS: To achieve the study objectives, we analyzed the public's information-seeking behaviors on Google and news media coverage on COVID-19. We collected data on COVID-19 news coverage and Google search queries from eight countries (ie, the United States, the United Kingdom, Canada, Singapore, Ireland, Australia, South Africa, and New Zealand) between January 1 and April 29, 2020. We depicted the characteristics of the COVID-19 news coverage trends over time, as well as the search query trends for the topics of COVID-19-related "diseases," "treatments and medical resources," "symptoms and signs," and "public measures." The search query trends provided the relative search volume (RSV) as an indicator to represent the popularity of a specific search term in a specific geographic area over time. Also, time-lag correlation analysis was used to further explore the relationship between search terms trends and the number of new daily cases, as well as the relationship between search terms trends and news coverage. RESULTS: Across all search trends in eight countries, almost all search peaks appeared between March and April 2020, and declined in April 2020. Regarding COVID-19-related "diseases," in most countries, the RSV of the term "coronavirus" increased earlier than that of "covid-19"; however, around April 2020, the search volume of the term "covid-19" surpassed that of "coronavirus." Regarding the topic "treatments and medical resources," the most and least searched terms were "mask" and "ventilator," respectively. Regarding the topic "symptoms and signs," "fever" and "cough" were the most searched terms. The RSV for the term "lockdown" was significantly higher than that for "social distancing" under the topic "public health measures." In addition, when combining search trends with news coverage, there were three main patterns: (1) the pattern for Singapore, (2) the pattern for the United States, and (3) the pattern for the other countries. In the time-lag correlation analysis between the RSV for the topic "treatments and medical resources" and the number of new daily cases, the RSV for all countries except Singapore was positively correlated with new daily cases, with a maximum correlation of 0.8 for the United States. In addition, in the time-lag correlation analysis between the overall RSV for the topic "diseases" and the number of daily news items, the overall RSV was positively correlated with the number of daily news items, the maximum correlation coefficient was more than 0.8, and the search behavior occurred 0 to 17 days earlier than the news coverage. CONCLUSIONS: Our findings revealed public interest in masks, disease control, and public measures, and revealed the potential value of Google Trends in the face of the emergence of new infectious diseases. Also, Google Trends combined with news media can achieve more efficient health communication. Therefore, both news media and Google Trends can contribute to the early prevention and control of epidemics.

Variational deep embedding-based active learning for the diagnosis of pneumonia.

Machine learning works similar to the way humans train their brains. In general, previous experiences prepared the brain by firing specific nerve cells in the brain and increasing the weight of the links between them. Machine learning also completes the classification task by constantly changing the weights in the model through training on the training set. It can conduct a much more significant amount of training and achieve higher recognition accuracy in specific fields than the human brain. In this paper, we proposed an active learning framework called variational deep embedding-based active learning (VaDEAL) as a human-centric computing method to improve the accuracy of diagnosing pneumonia. Because active learning (AL) realizes label-efficient learning by labeling the most valuable queries, we propose a new AL strategy that incorporates clustering to improve the sampling quality. Our framework consists of a VaDE module, a task learner, and a sampling calculator. First, the VaDE performs unsupervised reduction and clustering of dimension over the entire data set. The end-to-end task learner obtains the embedding representations of the VaDE-processed sample while training the target classifier of the model. The sampling calculator will calculate the representativeness of the samples by VaDE, the uncertainty of the samples through task learning, and ensure the overall diversity of the samples by calculating the similarity constraints between the current and previous samples. With our novel design, the combination of uncertainty, representativeness, and diversity scores allows us to select the most informative samples for labeling, thus improving overall performance. With extensive experiments and evaluations performed on a large dataset, we demonstrate that our proposed method is superior to the state-of-the-art methods and has the highest accuracy in the diagnosis of pneumonia.

Non-Neutralizing Epitopes Shade Neutralizing Epitopes against Omicron in a Multiple Epitope-Based Vaccine.

The ongoing coronavirus disease 2019 pandemic has raised concerns about the risk of re-infection. Non-neutralizing epitopes are one of the major reasons for antibody-dependent enhancement. Past studies on the ancestral severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have revealed an infectivity-enhancing site on the ancestral SARS-CoV-2 spike protein. However, infection enhancement associated with the SARS-CoV-2 Omicron strain remains elusive. In this study, we examined the antibodies induced by a multiple epitope-based vaccine, which showed infection enhancement for the Omicron strain but not for the ancestral SARS-CoV-2 or Delta strain. By examining the antibodies induced by single epitope-based vaccines, we identified a conserved epitope, IDf (450-469), with neutralizing activity against ancestral SARS-CoV-2, Delta, and Omicron. Although neutralizing epitopes are present in the multiple epitope-based vaccine, other immunodominant non-neutralizing epitopes such as IDg (480-499) can shade their neutralizing activity, leading to infection enhancement of Omicron. Our study provides up-to-date epitope information on SARS-CoV-2 variants to help design better vaccines or antibody-based therapeutics against future variants.

Comparison of the duration of viral RNA shedding and anti-SARS-CoV-2 spike IgG and IgM antibody titers in COVID-19 patients who were vaccinated with inactivated vaccines or not: a retrospective study.

BACKGROUND: At present, the role of inactivated vaccines in viral RNA shedding among Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) breakthrough infections is still unknown. METHODS: We collected data of 147 coronavirus disease 2019 (COVID-19) patients with mild-to-moderate illness who were hospitalized in the Third People's Hospital of Yangzhou from 7 to 20 August 2021 and analyzed the differences in symptoms and laboratory tests among fully vaccinated (FV), partially vaccinated (PV) and unvaccinated (UV) patients. RESULTS: The median duration of viral RNA shedding was shorter in the FV (12 [IQR, 9.5-14] days) and PV (13 [IQR, 9-16.75] days) groups than in the UV group (15 [IQR, 11.75-17.25] days) (adjusted P < 0.001 and adjusted P = 0.23, respectively). The median titers of SARS-CoV-2-specific IgG and IgM were significantly higher in the FV (12.29 S/co [IQR, 2.08-63.59] and 0.3 S/co [IQR, 0.05-2.29], respectively) and PV (0.68 S/co [IQR, 0.14-28.69] and 0.12 S/co [0.03-5.23], respectively) groups than in the UV group (0.06 S/co [IQR, 0.03-0.47] and 0.04 S/co [IQR, 0.02-0.07]) (adjusted P < 0.001 and adjusted P = 0.008, respectively). CONCLUSIONS: Inactivated vaccines may shorten viral RNA shedding in breakthrough infected patients who have mild-to-moderate illness and may improve the ability of the host to generate specific antibodies to infection.

Research on the Equity and Optimal Allocation of Basic Medical Services in Guangzhou in the Context of COVID-19.

Optimizing the allocation of basic medical services and ensuring their equity are necessary to improve the ability to respond to public health emergencies and promote health equity in the context of COVID-19. This study aims to analyze the equity of Guangzhou's basic medical service and identify areas where health resources are relatively scarce. The spatial distribution and patterns of basic medical services were analyzed using kernel density analysis and standard deviation ellipse. The equity was analyzed using the Gini coefficient and Lorenz curve in terms of population and geographical area, respectively. Considering the medical demand and supply sides, the Gaussian two-step floating catchment area method was used to analyze the accessibility to different levels of medical institutions. The kernel density analysis and standard deviation ellipse showed that the spatial distribution of medical and health resources in Guangzhou is unevenly distributed, and high-level hospitals and medical resources are mainly concentrated in the centrum. From the perspective of population, Guangzhou's medical equity is generally reasonable. The accessibility of medical institutions differs with different levels, and the tertiary medical institutions have the best accessibility, while the unclassified, primary, and secondary medical institutions generally have lower accessibility. The accessibility of districts in Guangzhou varies greatly. Areas in the center are most accessible to basic medical services, while accessibility in outskirt areas has gradually decreased. Conclusion: The quantity of per capita medical and health resources in Guangzhou, as evidenced by basic medical services, is sufficient, but the spatial distribution is unequal. The developed city center enjoys more adequate healthcare resources than the distant suburbs. Primary healthcare should be built, especially in distant suburbs, to strengthen basic medical service equity in Guangzhou.

Development and application of ribonucleic acid therapy strategies against COVID-19.

The Coronavirus disease 2019 (COVID-19) pandemic is caused by the severe acute respiratory syndrome 2 coronavirus (SARS-CoV-2), remaining a global health crisis since its outbreak until now. Advanced biotechnology and research findings have revealed many suitable viral and host targets for a wide range of therapeutic strategies. The emerging ribonucleic acid therapy can modulate gene expression by post-transcriptional gene silencing (PTGS) based on Watson-Crick base pairing. RNA therapies, including antisense oligonucleotides (ASO), ribozymes, RNA interference (RNAi), aptamers, etc., were used to treat SARS-CoV whose genome is similar to SARV-CoV-2, and the past experience also applies for the treatment of COVID-19. Several studies against SARS-CoV-2 based on RNA therapeutic strategy have been reported, and a dozen of relevant preclinical or clinical trials are in process globally. RNA therapy has been a very active and important part of COVID-19 treatment. In this review, we focus on the progress of ribonucleic acid therapeutic strategies development and application, discuss corresponding problems and challenges, and suggest new strategies and solutions.

The antiviral properties of tea

Tea (Camellia sinensis) is known as a global health beverage, and global tea consumption increases due to its biological activities. In the last 30 years. antiviral activities of tea and its components, especially tea polyphenols. with different modes of action were demonstrated on diverse families of viruses. such as influenza virus, coronavirus, hepatitis virus, and human immunodeficiency virus. etc. This review summarized the current knowledge on the antiviral activities of tea and its components. Most of these studies demonstrated antiviral properties of tea and its components by in vitro biochemical or cell experiments with little rodent and clinical studies. Therefore, it is still unclear whether the antiviral effects of daily tea consumption are available. More large-scale randomized intervention and epidemiological/clinical studies are needed to confirm clinical efficacy of tea and its components.

A New Feature Based Deep Attention Sales Forecasting Model for Enterprise Sustainable Development

In recent years, with the rise of the Internet, e-commerce has become an important field of commodity sales. However, e-commerce is affected by many factors, and the wrong judgment of supply and marketing relationships will bring huge losses to operators. Therefore, it is of great significance to establish a model that can effectively achieve high precision sales prediction for ensuring the sustainable development of e-commerce enterprises. In this paper, we propose an e-commerce sales forecasting model that considers the features of many aspects of correlation. In the first layer of the model, the temporal convolutional network (TCN) is used to extract the deep temporal characteristics of univariate sales historical data, which ensures the integrity of temporal information of sales characteristics. In the second layer, the feature selection method based on reinforcement learning is used to filter the effective correlation feature set and combine it with the temporal feature after processing, which not only improves the amount of effective information input by the model, but also avoids the high feature dimension. The third layer of the reformer model learns all the features and pays different attention to the features with different degrees of importance, ensuring the stability of the sales forecast. In the experimental part, we compare the proposed model with the current advanced sales forecasting model, and we can find that the proposed model has higher stability and accuracy.

Preferences for Attributes of Initial COVID-19 Diagnosis in the United States and China During the Pandemic: Discrete Choice Experiment With Propensity Score Matching.

BACKGROUND: China and the United States play critical leading roles in the global effort to contain the COVID-19 virus. Therefore, their population's preferences for initial diagnosis were compared to provide policy and clinical insights. OBJECTIVE: We aim to quantify and compare the public's preferences for medical management of fever and the attributes of initial diagnosis in the case of presenting symptoms during the COVID-19 pandemic in China and the United States. METHODS: We conducted a cross-sectional study from January to March 2021 in China and the United States using an online discrete choice experiment (DCE) questionnaire distributed through Amazon Mechanical Turk (MTurk; in the United States) and recruited volunteers (in China). Propensity score matching (PSM) was used to match the 2 groups of respondents from China and the United States to minimize confounding effects. In addition, the respondents' preferences for different diagnosis options were evaluated using a mixed logit model (MXL) and latent class models (LCMs). Moreover, demographic data were collected and compared using the chi-square test, Fisher test, and Mann-Whitney U test. RESULTS: A total of 9112 respondents (5411, 59.4%, from China and 3701, 40.6%, from the United States) who completed our survey were included in our analysis. After PSM, 1240 (22.9%) respondents from China and 1240 (33.5%) from the United States were matched for sex, age, educational level, occupation, and annual salary levels. The segmented sizes of 3 classes of respondents from China were 870 (70.2%), 270 (21.8%), and 100 (8.0%), respectively. Meanwhile, the US respondents' segmented sizes were 269 (21.7%), 139 (11.2%), and 832 (67.1%), respectively. Respondents from China attached the greatest importance to the type of medical institution (weighted importance=40.0%), while those from the United States valued the waiting time (weighted importance=31.5%) the most. Respondents from China preferred the emergency department (coefficient=0.973, reference level: online consultation) and fever clinic (a special clinic for the treatment of fever patients for the prevention and control of acute infectious diseases in China; coefficient=0.974, reference level: online consultation), while those from the United States preferred private clinics (general practices; coefficient=0.543, reference level: online consultation). Additionally, shorter waiting times, COVID-19 nucleic acid testing arrangements, higher reimbursement rates, and lower costs were always preferred. CONCLUSIONS: Improvements in the availability of COVID-19 testing and medical professional skills and increased designated health care facilities may help boost potential health care seeking during COVID-19 and prevent unrecognized community spreading of SARS-CoV-2 in China and the United States. Moreover, to better prevent future waves of pandemics, identify undiagnosed patients, and encourage those undiagnosed to seek health care services to curb the pandemic, the hierarchical diagnosis and treatment system needs improvement in China, and the United States should focus on reducing diagnosis costs and raising the reimbursement rate of medical insurance.

Development and application of ribonucleic acid therapy strategies against COVID-19

The Coronavirus disease 2019 (COVID-19) pandemic is caused by the severe acute respiratory syndrome 2 coronavirus (SARS-CoV-2), remaining a global health crisis since its outbreak until now. Advanced biotechnology and research findings have revealed many suitable viral and host targets for a wide range of therapeutic strategies. The emerging ribonucleic acid therapy can modulate gene expression by post-transcriptional gene silencing (PTGS) based on Watson-Crick base pairing. RNA therapies, including antisense oligonucleotides (ASO), ribozymes, RNA interference (RNAi), aptamers, etc., were used to treat SARS-CoV whose genome is similar to SARV-CoV-2, and the past experience also applies for the treatment of COVID-19. Several studies against SARS-CoV-2 based on RNA therapeutic strategy have been reported, and a dozen of relevant preclinical or clinical trials are in process globally. RNA therapy has been a very active and important part of COVID-19 treatment. In this review, we focus on the progress of ribonucleic acid therapeutic strategies development and application, discuss corresponding problems and challenges, and suggest new strategies and solutions.