Intranasal delivery of a chimpanzee adenovirus vector expressing a pre-fusion spike (BV-AdCoV-1) protects golden Syrian hamsters against SARS-CoV-2 infection.

We evaluated the immunogenicity and protective ability of a chimpanzee replication-deficient adenovirus vectored COVID-19 vaccine (BV-AdCoV-1) expressing a stabilized pre-fusion SARS-CoV-2 spike glycoprotein in golden Syrian hamsters. Intranasal administration of BV-AdCoV-1 elicited strong humoral and cellular immunity in the animals. Furthermore, vaccination prevented weight loss, reduced SARS-CoV-2 infectious virus titers in the lungs as well as lung pathology and provided protection against SARS-CoV-2 live challenge. In addition, there was no vaccine-induced enhanced disease nor immunopathological exacerbation in BV-AdCoV-1-vaccinated animals. Furthermore, the vaccine induced cross-neutralizing antibody responses against the ancestral strain and the B.1.617.2, Omicron(BA.1), Omicron(BA.2.75) and Omicron(BA.4/5) variants of concern. These results demonstrate that BV-AdCoV-1 is potentially a promising candidate vaccine to prevent SARS-CoV-2 infection, and to curtail pandemic spread in humans.

A Research Publication and Grant Preparation Program for Native American Faculty in STEM: Implementation of the Six R's Indigenous Framework.

Faculty members in science, technology, engineering, and mathematics (STEM) disciplines are typically expected to pursue grant funding and publish to support their research or teaching agendas. Providing effective professional development programs on grant preparation and management and on research publications is crucial. This study shares the design and implementation of such a program for Native STEM faculty (NAF-STEM) from two tribal colleges and one public, non-tribal, Ph.D. granting institution during a 3-year period. The overall development and implementation of the program is centered on the six R's Indigenous framework - Respect, Relationship, Representation, Relevance, Responsibility, and Reciprocity. The role of NAF-STEM and their interactions with the program, as members of the community formed by their participation, impacted the program. Their practices and the program co-emerged over time, each providing structure and meaning for the other. Through such reciprocity, NAF-STEM and the program research team continually refined the program through their mutual engagement. They took on the shared responsibility of the program while they participated in and shaped its practices. The process and results of formative and summative assessment and the impact of COVID-19 on the program are reported. Results of the program offer lessons on the implementation of six R's framework in professional development at institutions of higher education.

Mentoring From Within: Developing a Virtual Mentoring Curriculum for a Network of Culturally Diverse Women Faculty

Nine women faculty, who are members of a global mentoring network, collaboratively designed a professional development project to explore their mentoring relationships and practices. Using a Learning Management System (LMS), they designed six modules with supplementary learning activities. Project findings highlight the need for a mentoring curriculum that: (a) helps members meet research and publication expectations;(b) addresses network tensions;(c) creates stronger network ties;(d) values each other 's cultural histories and identities;and (e) recognizes their humanity as women academics who must balance life challenges and work expectations.

Prevalence and Factors Associated With Burnout of Frontline Healthcare Workers in Fighting Against the COVID-19 Pandemic: Evidence From China.

OBJECTIVES: The sudden outbreak of the novel coronavirus disease (COVID-19) plunged healthcare workers (HCWs) into warfare. This study aimed to determine the prevalence of burnout and the factors associated with it among frontline HCWs fighting COVID-19. METHODS: A cross-sectional survey was conducted among frontline HCWs fighting against the COVID-19 in Wuhan, Harbin, and Shenzhen during the period from February 18 to March 4. Finally, HCWs were recruited using cluster sampling, 1,163 HCWs were included in the final analysis. Burnout was measured using a 22-item Maslach Burnout Inventory scale (MBI scale). RESULTS: Of the participants, 48.6% suffered from burnout, and 21.8% showed a high degree of burnout. Doctors (b = 3.954, P = 0.011) and nurses (b = 3.067, P = 0.042) showed higher emotional exhaustion (EE) than administrators. Participants who worked continuously for more than 8 h a day (b = 3.392, P = 0.000), those who were unable to eat three regular daily meals (b = 2.225, P = 0.008), whose daily water intake was no more than 800 ml (b = 3.007, P = 0.000), who slept for no more than 6 h (b = 1.609, P = 0.036), and who were infected or had colleagues who were infected with COVID-19 (b = 4.182, P = 0.000) experienced much higher levels of EE, while those who could adhere to infection control procedures (b = -5.992, P = 0.000), who were satisfied with their hospital's infection control measures(b = -3.709, P = 0.001), and who could receive sufficient psychological crisis intervention (b = -1.588, P = 0.039) reported lower levels of EE. CONCLUSION: The study reveals that burnout is prevalent among frontline HCWs and that the known factors associated with burnout, such as workload, and the factors directly associated with COVID-19, such as having insufficient protection, can affect burnout symptoms in frontline HCWs. Synergized and comprehensive interventions should be targeted at reducing its occurrence among frontline HCWs fighting COVID-19.

Rapid Acquisition of High-Quality SARS-CoV-2 Genome via Amplicon-Oxford Nanopore Sequencing.

Genome sequencing has shown strong capabilities in the initial stages of the COVID-19 pandemic such as pathogen identification and virus preliminary tracing. While the rapid acquisition of SARS-CoV-2 genome from clinical specimens is limited by their low nucleic acid load and the complexity of the nucleic acid background. To address this issue, we modified and evaluated an approach by utilizing SARS-CoV-2-specific amplicon amplification and Oxford Nanopore PromethION platform. This workflow started with the throat swab of the COVID-19 patient, combined reverse transcript PCR, and multi-amplification in one-step to shorten the experiment time, then can quickly and steadily obtain high-quality SARS-CoV-2 genome within 24 h. A comprehensive evaluation of the method was conducted in 42 samples: the sequencing quality of the method was correlated well with the viral load of the samples; high-quality SARS-CoV-2 genome could be obtained stably in the samples with Ct value up to 39.14; data yielding for different Ct values were assessed and the recommended sequencing time was 8 h for samples with Ct value of less than 20; variation analysis indicated that the method can detect the existing and emerging genomic mutations as well; Illumina sequencing verified that ultra-deep sequencing can greatly improve the single read error rate of Nanopore sequencing, making it as low as 0.4/10,000 bp. In summary, high-quality SARS-CoV-2 genome can be acquired by utilizing the amplicon amplification and it is an effective method in accelerating the acquisition of genetic resources and tracking the genome diversity of SARS-CoV-2.

Emerging manufacturers engagements in the COVID -19 vaccine research, development and supply.

The World Health Organization declared the COVID-19 disease as a pandemic requiring a rapid response. Through online search, direct communication with network members and an internal survey, engagements of developing countries' vaccine manufacturers' network members in the research and development of COVID-19 vaccines and their capacities in the manufacturing, fill-finish and distribution of vaccines were assessed. Currently, 19 network members engaged in research and development of COVID-19 vaccines, using six principal technology platforms. In addition, an internal survey showed that the number of vaccines supplied collectively by 37 members, in 2018-19, was about 3.5 billion doses annually. Almost a third of network members having vaccines prequalified by the World Health Organization comply with international regulations and mechanisms to distribute vaccines across borders. The use of existing manufacturing, fill-finish and distribution capabilities can support an efficient roll-out of vaccines against COVID-19, while maintaining supply security of existing vaccines for on-going immunization programmes.

Construction and validation of a machine learning-based nomogram: A tool to predict the risk of getting severe coronavirus disease 2019 (COVID-19).

BACKGROUND: Identifying patients who may develop severe coronavirus disease 2019 (COVID-19) will facilitate personalized treatment and optimize the distribution of medical resources. METHODS: In this study, 590 COVID-19 patients during hospitalization were enrolled (Training set: n = 285; Internal validation set: n = 127; Prospective set: n = 178). After filtered by two machine learning methods in the training set, 5 out of 31 clinical features were selected into the model building to predict the risk of developing severe COVID-19 disease. Multivariate logistic regression was applied to build the prediction nomogram and validated in two different sets. Receiver operating characteristic (ROC) analysis and decision curve analysis (DCA) were used to evaluate its performance. RESULTS: From 31 potential predictors in the training set, 5 independent predictive factors were identified and included in the risk score: C-reactive protein (CRP), lactate dehydrogenase (LDH), Age, Charlson/Deyo comorbidity score (CDCS), and erythrocyte sedimentation rate (ESR). Subsequently, we generated the nomogram based on the above features for predicting severe COVID-19. In the training cohort, the area under curves (AUCs) were 0.822 (95% CI, 0.765-0.875) and the internal validation cohort was 0.762 (95% CI, 0.768-0.844). Further, we validated it in a prospective cohort with the AUCs of 0.705 (95% CI, 0.627-0.778). The internally bootstrapped calibration curve showed favorable consistency between prediction by nomogram and the actual situation. And DCA analysis also conferred high clinical net benefit. CONCLUSION: In this study, our predicting model based on five clinical characteristics of COVID-19 patients will enable clinicians to predict the potential risk of developing critical illness and thus optimize medical management.

Interpreting, analysing and modelling COVID-19 mortality data.

We present results on the mortality statistics of the COVID-19 epidemic in a number of countries. Our data analysis suggests classifying countries in five groups, (1) Western countries, (2) East Block, (3) developed Southeast Asian countries, (4) Northern Hemisphere developing countries and (5) Southern Hemisphere countries. Comparing the number of deaths per million inhabitants, a pattern emerges in which the Western countries exhibit the largest mortality rate. Furthermore, comparing the running cumulative death tolls as the same level of outbreak progress in different countries reveals several subgroups within the Western countries and further emphasises the difference between the five groups. Analysing the relationship between deaths per million and life expectancy in different countries, taken as a proxy of the preponderance of elderly people in the population, a main reason behind the relatively more severe COVID-19 epidemic in the Western countries is found to be their larger population of elderly people, with exceptions such as Norway and Japan, for which other factors seem to dominate. Our comparison between countries at the same level of outbreak progress allows us to identify and quantify a measure of efficiency of the level of stringency of confinement measures. We find that increasing the stringency from 20 to 60 decreases the death count by about 50 lives per million in a time window of 20  days. Finally, we perform logistic equation analyses of deaths as a means of tracking the dynamics of outbreaks in the "first wave" and estimating the associated ultimate mortality, using four different models to identify model error and robustness of results. This quantitative analysis allows us to assess the outbreak progress in different countries, differentiating between those that are at a quite advanced stage and close to the end of the epidemic from those that are still in the middle of it. This raises many questions in terms of organisation, preparedness, governance structure and so on.

The prediction for development of COVID-19 in global major epidemic areas through empirical trends in China by utilizing state transition matrix model.

BACKGROUND: Since pneumonia caused by coronavirus disease 2019 (COVID-19) broke out in Wuhan, Hubei province, China, tremendous infected cases has risen all over the world attributed to its high transmissibility. We aimed to mathematically forecast the inflection point (IFP) of new cases in South Korea, Italy, and Iran, utilizing the transcendental model from China. METHODS: Data from reports released by the National Health Commission of the People's Republic of China (Dec 31, 2019 to Mar 5, 2020) and the World Health Organization (Jan 20, 2020 to Mar 5, 2020) were extracted as the training set and the data from Mar 6 to 9 as the validation set. New close contacts, newly confirmed cases, cumulative confirmed cases, non-severe cases, severe cases, critical cases, cured cases, and death were collected and analyzed. We analyzed the data above through the State Transition Matrix model. RESULTS: The optimistic scenario (non-Hubei model, daily increment rate of - 3.87%), the cautiously optimistic scenario (Hubei model, daily increment rate of - 2.20%), and the relatively pessimistic scenario (adjustment, daily increment rate of - 1.50%) were inferred and modeling from data in China. The IFP of time in South Korea would be Mar 6 to 12, Italy Mar 10 to 24, and Iran Mar 10 to 24. The numbers of cumulative confirmed patients will reach approximately 20 k in South Korea, 209 k in Italy, and 226 k in Iran under fitting scenarios, respectively. However, with the adoption of different diagnosis criteria, the variation of new cases could impose various influences in the predictive model. If that happens, the IFP of increment will be earlier than predicted above. CONCLUSION: The end of the pandemic is still inapproachable, and the number of confirmed cases is still escalating. With the augment of data, the world epidemic trend could be further predicted, and it is imperative to consummate the assignment of global medical resources to curb the development of COVID-19.

Generalized logistic growth modeling of the COVID-19 outbreak: comparing the dynamics in the 29 provinces in China and in the rest of the world.

Started in Wuhan, China, the COVID-19 has been spreading all over the world. We calibrate the logistic growth model, the generalized logistic growth model, the generalized Richards model and the generalized growth model to the reported number of infected cases for the whole of China, 29 provinces in China, and 33 countries and regions that have been or are undergoing major outbreaks. We dissect the development of the epidemics in China and the impact of the drastic control measures both at the aggregate level and within each province. We quantitatively document four phases of the outbreak in China with a detailed analysis on the heterogeneous situations across provinces. The extreme containment measures implemented by China were very effective with some instructive variations across provinces. Borrowing from the experience of China, we made scenario projections on the development of the outbreak in other countries. We identified that outbreaks in 14 countries (mostly in western Europe) have ended, while resurgences of cases have been identified in several among them. The modeling results clearly show longer after-peak trajectories in western countries, in contrast to most provinces in China where the after-peak trajectory is characterized by a much faster decay. We identified three groups of countries in different level of outbreak progress, and provide informative implications for the current global pandemic.

The diagnosis of SARS-CoV2 pneumonia: A review of laboratory and radiological testing results.

The rapid emergence of coronavirus disease 2019 (COVID-19) has necessitated the implementation of diverse pandemic control strategies throughout the world. To effectively control the spread of this disease, it is essential that it be diagnosed at an early stage so that patients can be reliably quarantined such that disease spread will be slowed. At present, the diagnosis of this infectious form of coronavirus pneumonia is largely dependent upon a combination of laboratory testing and imaging analyses of variable diagnostic efficacy. In the present report, we reviewed prior literature pertaining to the diagnosis of different forms of pneumonia caused by coronaviruses (severe acute respiratory syndrome [SARS], Middle East respiratory syndrome, and SARS-CoV-2) and assessed two different potential diagnostic approaches. We ultimately found that computed tomography was associated with a higher rate of diagnostic accuracy than was a real-time quantitative polymerase chain reaction-based approach (P = .0041), and chest radiography (P = .0100). Even so, it is important that clinicians utilize a combination of laboratory and radiological testing where possible to ensure that this virus is reliably and quickly detected such that it may be treated and patients may be isolated in a timely fashion, thereby effectively curbing the further progression of this pandemic.

Patient follow-up after discharge after COVID-19 pneumonia: Considerations for infectious control.

Coronavirus disease 2019 (COVID-19) represents a significant global medical issue, with a growing number of cumulative confirmed cases. However, a large number of patients with COVID-19 have overcome the disease, meeting hospital discharge criteria, and are gradually returning to work and social life. Nonetheless, COVID-19 may cause further downstream issues in these patients, such as due to possible reactivation of the virus, long-term pulmonary defects, and posttraumatic stress disorder. In this study, we, therefore, queried relevant literature concerning severe acute respiratory syndrome, Middle East respiratory syndrome, and COVID-19 for reference to come to a consensus on follow-up strategies. We found that strategies, such as the implementation of polymerase chain reaction testing, imaging surveillance, and psychological assessments, starting at the time of discharge, were necessary for long-term follow-up. If close care is given to every aspect of coronavirus management, we expect that the pandemic outbreak will soon be overcome.

Immune environment modulation in pneumonia patients caused by coronavirus: SARS-CoV, MERS-CoV and SARS-CoV-2.

Currently, we are on a global pandemic of Coronavirus disease-2019 (COVID-19) which causes fever, dry cough, fatigue and acute respiratory distress syndrome (ARDS) that may ultimately lead to the death of the infected. Current researches on COVID-19 continue to highlight the necessity for further understanding the virus-host synergies. In this study, we have highlighted the key cytokines induced by coronavirus infections. We have demonstrated that genes coding interleukins (Il-1α, Il-1ß, Il-6, Il-10), chemokine (Ccl2, Ccl3, Ccl5, Ccl10), and interferon (Ifn-α2, Ifn-ß1, Ifn2) upsurge significantly which in line with the elevated infiltration of T cells, NK cells and monocytes in SARS-Cov treated group at 24 hours. Also, interleukins (IL-6, IL-23α, IL-10, IL-7, IL-1α, IL-1ß) and interferon (IFN-α2, IFN2, IFN-γ) have increased dramatically in MERS-Cov at 24 hours. A similar cytokine profile showed the cytokine storm served a critical role in the infection process. Subsequent investigation of 463 patients with COVID-19 disease revealed the decreased amount of total lymphocytes, CD3+, CD4+, and CD8+ T lymphocytes in the severe type patients which indicated COVID-19 can impose hard blows on human lymphocyte resulting in lethal pneumonia. Thus, taking control of changes in immune factors could be critical in the treatment of COVID-19.

The diagnosis of pandemic coronavirus pneumonia: A review of radiology examination and laboratory test.

Since the outbreak of novel coronavirus disease 2019 (COVID-19), epidemic prevention strategies have been implemented worldwide. For the sake of controlling the infectious coronavirus pneumonia, early diagnosis and quarantine play an imperative role. Currently, the mainstream diagnostic methods are imaging and laboratory diagnosis, which differ in their efficacy of diagnosis. To compare the detection rate, we reviewed numerous literature on pneumonia caused by coronaviruses (SARS, MERS, and SARS-CoV-2) and analyzed two different ways of diagnosis. The results showed that the detection rate of computed tomography (CT) diagnosis was significantly higher than that of real-time quantitative polymerase chain reaction (qPCR) (P = 0.00697). Still, clinicians should combine radiology and laboratory methods to achieve a higher detection rate, so that instant isolation and treatment could be effectively conducted to curb the rampant spread of the epidemic.

Prediction of Number of Cases of 2019 Novel Coronavirus (COVID-19) Using Social Media Search Index.

Predicting the number of new suspected or confirmed cases of novel coronavirus disease 2019 (COVID-19) is crucial in the prevention and control of the COVID-19 outbreak. Social media search indexes (SMSI) for dry cough, fever, chest distress, coronavirus, and pneumonia were collected from 31 December 2019 to 9 February 2020. The new suspected cases of COVID-19 data were collected from 20 January 2020 to 9 February 2020. We used the lagged series of SMSI to predict new suspected COVID-19 case numbers during this period. To avoid overfitting, five methods, namely subset selection, forward selection, lasso regression, ridge regression, and elastic net, were used to estimate coefficients. We selected the optimal method to predict new suspected COVID-19 case numbers from 20 January 2020 to 9 February 2020. We further validated the optimal method for new confirmed cases of COVID-19 from 31 December 2019 to 17 February 2020. The new suspected COVID-19 case numbers correlated significantly with the lagged series of SMSI. SMSI could be detected 6-9 days earlier than new suspected cases of COVID-19. The optimal method was the subset selection method, which had the lowest estimation error and a moderate number of predictors. The subset selection method also significantly correlated with the new confirmed COVID-19 cases after validation. SMSI findings on lag day 10 were significantly correlated with new confirmed COVID-19 cases. SMSI could be a significant predictor of the number of COVID-19 infections. SMSI could be an effective early predictor, which would enable governments' health departments to locate potential and high-risk outbreak areas.