Development of a multi-recombinase polymerase amplification assay for rapid identification of COVID-19, influenza A and B.

The coronavirus disease 2019 (COVID-19) pandemic caused extensive loss of life worldwide. Further, the COVID-19 and influenza mix-infection had caused great distress to the diagnosis of the disease. To control illness progression and limit viral spread within the population, a real-time reverse-transcription PCR (RT-PCR) assay for early diagnosis of COVID-19 was developed, but detection was time-consuming (4-6 h). To improve the diagnosis of COVID-19 and influenza, we herein developed a recombinase polymerase amplification (RPA) method for simple and rapid amplification of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), the causative agent of COVID-19 and Influenza A (H1N1, H3N2) and B (influenza B). Genes encoding the matrix protein (M) for H1N1, and the hemagglutinin (HA) for H3N2, and the polymerase A (PA) for Influenza B, and the nucleocapsid protein (N), the RNA-dependent-RNA polymerase (RdRP) in the open reading frame 1ab (ORF1ab) region, and the envelope protein (E) for SARS-CoV-2 were selected, and specific primers were designed. We validated our method using SARS-CoV-2, H1N1, H3N2 and influenza B plasmid standards and RNA samples extracted from COVID-19 and Influenza A/B (RT-PCR-verified) positive patients. The method could detect SARS-CoV-2 plasmid standard DNA quantitatively between 102 and 105 copies/ml with a log linearity of 0.99 in 22 min. And this method also be very effective in simultaneous detection of H1N1, H3N2 and influenza B. Clinical validation of 100 cases revealed a sensitivity of 100% for differentiating COVID-19 patients from healthy controls when the specificity was set at 90%. These results demonstrate that this nucleic acid testing method is advantageous compared with traditional PCR and other isothermal nucleic acid amplification methods in terms of time and portability. This method could potentially be used for detection of SARS-CoV-2, H1N1, H3N2 and influenza B, and adapted for point-of-care (POC) detection of a broad range of infectious pathogens in resource-limited settings.

Numerical study of transient indoor airflow and virus-laden droplet dispersion: Impact of interactive human movement.

Human movement affects indoor airflow and the airborne transmission of respiratory infectious diseases, which has attracted scholars. However, the interactive airflow between moving and stationary people has yet to be studied in detail. This study used the numerical method validated by experimental data to explore the transient indoor airflow and virus-laden droplet dispersion in scenes with interactive human movement. Human-shaped numerical models and the dynamic mesh method were adopted to realize human movement in scenes with different lateral distances (0.2-1.2 m) between a moving person and stationary (standing/sitting) persons. The interactive human movement obviously impacts other persons' respiratory airflow, and the lateral fusion ranged about 0.6 m. The interactive human movement strengthens the indoor airflow convection, and some exhaled virus-laden droplets were carried into wake flow and enhanced long-range airborne transmission. The impact of interactive human movement on sitting patients' exhalation airflow seems more evident than on standing patients. The impact might last over 2 min after movement stopped, and people in the affected area might be at a higher exposure. The results can provide a reference for epidemic control in indoor environments.

Epidemiological Characteristics of COVID-19 Outbreak in Yangzhou, China, 2021.

Objective: Epidemiological characteristics of COVID-19 outbreak in Yangzhou city caused by the highly contagious Delta variant strain of SARS-CoV-2 virus were investigated in this retrospective descriptive study to provide prevention and control guidelines for outbreaks in the future. Methods: All the epidemiological data used in this study were collected manually from the official website of the Yangzhou Municipal Health Committee from 28 July to 26 August 2021, and then were analyzed systematically and statistically in this study. Results: A total of 570 COVID-19 cases were reported during the short-term outbreak in Yangzhou City. The ages of infected individuals ranged from 1 to 90 years with the average age at 49.47 ± 22.69 years. As for gender distributions, the ratio of male- to-female patients was 1:1.36 (242:328). Geographic analysis showed that 377 patients (66.1%) were in Hanjiang District while 188 patients (33.0%) were in Guangling District. Clinical diagnosis showed that 175 people (30.7%) had mild symptoms, 385 people were in moderate conditions (67.5%), and 10 people were in severe situations (1.8%). Significant age differences were found among the three groups (P < 0.001). However, no significant difference was identified in terms of gender ratio (P > 0.05). Based on the transmission chain formed by 6 generations of infected persons with a clear transmission relationship, the age showed a gradually decreasing trend, while the median time of diagnosis in 2 adjacent generations was 3 days. In addition, the estimated basic reproduction number R 0 of the Delta variant was 3.3651 by the classical Susceptible, Infectious, and/or Recovered (SIR) model. Conclusion: The Delta variant of SARS-CoV-2 was highly infectious and has obvious clustering characteristics during the Yangzhou outbreak in China.

A Feasibility Study of Developing eLCV Shared Architecture in Taiwan

Vehicle electrification has become an important strategy adopted worldwide, including in Taiwan, as a means to achieving net zero emissions. Taiwan is capable of building a whole light commercial vehicle and has technological strength in producing critical EV parts. This study applies the Bass diffusion model to assess the feasibility of developing eLCV shared architecture in Taiwan and estimates that the annual replacement demand for eLCVs could reach 20,221 units. This exceeds the threshold number of 5000 units, which could motivate the automakers to develop eLCV shared architecture. The simulation result shows that achieving full market penetration would take at least 13 years and would be highly correlated with policy support, the vehicle selling price and the battery pack price. The B2B model is a suitable way of introducing eLCVs into the logistics fleets. In the initial promotion phase, policy support and complementary measures would be needed, e.g., public sectors’ purchases, financial incentives and constructing a user-friendly environment. The simulation results further indicate that Taiwan’s eLCV market has a high price elasticity, and in the future, a tendency for the battery pack price to decline may speed up the replacement process.

Clinical and inflammatory features based machine learning model for fatal risk prediction of hospitalized COVID-19 patients: results from a retrospective cohort study.

OBJECTIVES: To appraise effective predictors for COVID-19 mortality in a retrospective cohort study. METHODS: A total of 1270 COVID-19 patients, including 984 admitted in Sino French New City Branch (training and internal validation sets randomly split at 7:3 ratio) and 286 admitted in Optical Valley Branch (external validation set) of Wuhan Tongji hospital, were included in this study. Forty-eight clinical and laboratory features were screened with LASSO method. Further multi-tree extreme gradient boosting (XGBoost) machine learning-based model was used to rank importance of features selected from LASSO and subsequently constructed death risk prediction model with simple-tree XGBoost model. Performances of models were evaluated by AUC, prediction accuracy, precision, and F1 scores. RESULTS: Six features, including disease severity, age, levels of high-sensitivity C-reactive protein (hs-CRP), lactate dehydrogenase (LDH), ferritin, and interleukin-10 (IL-10), were selected as predictors for COVID-19 mortality. Simple-tree XGBoost model conducted by these features can predict death risk accurately with >90% precision and >85% sensitivity, as well as F1 scores >0.90 in training and validation sets. CONCLUSION: We proposed the disease severity, age, serum levels of hs-CRP, LDH, ferritin, and IL-10 as significant predictors for death risk of COVID-19, which may help to identify the high-risk COVID-19 cases. KEY MESSAGES A machine learning method is used to build death risk model for COVID-19 patients. Disease severity, age, hs-CRP, LDH, ferritin, and IL-10 are death risk factors. These findings may help to identify the high-risk COVID-19 cases.

A potent bispecific nanobody protects hACE2 mice against SARS-CoV-2 infection via intranasal administration.

The dramatically expanding coronavirus disease 2019 (COVID-19) needs multiple effective countermeasures. Neutralizing nanobodies (Nbs) are a potential therapeutic strategy for treating COVID-19. Here, we characterize several receptor binding domain (RBD)-specific Nbs isolated from an Nb library derived from an alpaca immunized with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike glycoprotein (S); among them, three Nbs exhibit picomolar potency against SARS-CoV-2 live virus, pseudotyped viruses, and circulating SARS-CoV-2 variants. To improve their efficacy, various configurations of Nbs are engineered. Nb15-NbH-Nb15, a trimer constituted of three Nbs, is constructed to be bispecific for human serum albumin (HSA) and RBD of SARS-CoV-2. Nb15-NbH-Nb15 exhibits single-digit ng/ml neutralization potency against the wild-type and Delta variants of SARS-CoV-2 with a long half-life in vivo. In addition, we show that intranasal administration of Nb15-NbH-Nb15 provides effective protection for both prophylactic and therapeutic purposes against SARS-CoV-2 infection in transgenic hACE2 mice. Nb15-NbH-Nb15 is a potential candidate for both the prevention and treatment of SARS-CoV-2 through respiratory administration.

Transient and continuous effects of indoor human movement on nanoparticle concentrations in a sitting person's breathing zone.

Particle concentration in a sitting person's breathing zone can be influenced by human movement around the person, and the transient and continuous effects may differ. In this study, a set of full-scale experiments was conducted to sample the nanoparticle concentration in the breathing zone of a sitting thermal breathing manikin (STBM). The transient fluctuation of the nanoparticle concentration was recorded continuously and analyzed. The results showed that when a manikin moved (at 1 m/s) past the STBM, the nanoparticle concentration in the STBM's breathing zone decreased and reached its lowest after the standing manikin had passed, decreasing 37.6 (±5.7) % compared with the peak value. The average concentration in the STBM's breathing zone during influence periods was 5.18 (±0.99) % less than that during non-influence Periods (NP). This finding reflected the fact that the transient inhalation (over several seconds) of the STBM may be reduced by manikin movement. On the other hand, the exposure of the STBM increased 2.88 (±1.24) % when there was a continuously moving manikin compared with the stable state in a 10-min observation. This finding may be explained by the fuller mix of indoor air and nanoparticles caused by manikin movement, as well as the increase of nanoparticle suspension time. The difference in the transient and continuous effects of the manikin movement on the STBM's exposure shows the importance of considering these effects separately in different scenarios.

Emergency Optimization Decision-Making with Incomplete Probabilistic Information under the Background of COVID-19

At present, the whole world is facing the serious challenge of COVID-19, and it has reached a consensus that taking appropriate measures timely is the key to prevent and control infectious diseases. This paper proposes an algorithm to solve the problem of how to choose the most appropriate alternative from numerous alternatives in the limited time from the perspective of management. First of all, we have compared various data structures for keeping the comparison results of alternatives. After comparisons, we adopt the hesitant fuzzy incomplete probabilistic linguistic preference relation matrix to save the information which can keep the first-hand valuable collected data to the maximum extent;then, we can obtain the missing values with the help of the fault tree analysis method, which can consider both subjective evaluation data and objective historical data simultaneously. Meanwhile, the fault tree analysis method can find development laws with the help of similar infectious diseases that have occurred in the past. The definition of consistency index is also introduced which can measure whether there are contradictions and the degree of contradiction in the decision results. Only those data that meet the consistency requirements can be used for decision-making and then a method is proposed to effectively reduce the degree of inconsistency. The information aggregation method will be adopted subsequently, and we can obtain the ranking of alternatives. An instance with specific execution steps is also introduced to illustrate the feasibility and efficiency of the algorithm proposed in this paper;in the end, several types of comparisons with typical algorithms proposed by other scholars are carried out, and all the experimental results show that the algorithm proposed in this paper is effective and innovative in some aspects.

Emotional responses and coping strategies of nurses and nursing college students during COVID-19 outbreak (preprint)

Background: Affected by a Corona Virus Disease 2019 (COVID-19) outbreak, Since December 2019, there have been more than 76,000 cases of COVID-19 in China, causing more than 3,000 medical staff infections. Due to COVID-19 spreads quickly, is highly contagious, and can be fatal in severe cases, and there are no specific medicines, it poses a huge threat to the life and health of nurses and has a large impact on their emotional responses and coping strategies. Methods: This study conducted an online questionnaire survey from February 1 to 9, 2020 to investigate the current state of emotional responses and coping strategies of nurses and college nursing students in Anhui Province. This study used a modified Brief COPE (Carver, 1997) and a emotional responses scale. Results: The results found that women showed more severe anxiety and fear than men. Participants from cities showed more anxiety and fear than participants from rural, but rural participants showed more sadness than urban participants. The closer COVID-19 is to the participants, the stronger the anxiety and anger. Compared with Nursing college students, nurses have stronger emotional responses and are more willing to use Problem-focused coping. People may have a cycle of "the more fear, the more problem-focused coping". And people may "The more angry, the more emotion-focused coping", "the more problem-focused coping, the more anxious, the more angry, the more sadness". Conclusion: COVID-19 is a pressure source with great influence, both for individuals and for the social public groups. Different individuals and groups may experience different levels of psychological crisis, and those nurses at the core of the incident are affected. Hospitals should focus on providing psychological support to nurses and providing timely psychological assistance and training in coping strategies. Improving nurses' ability to regulate emotions and effective coping strategies, providing a strong guarantee for resolutely winning the battle against epidemic prevention and control.