Quantitative evaluation of the role of Fangcang shelter hospitals in the control of Omicron transmission: A case study of the outbreak in Shanghai, China in 2022.

Since Omicron began to spread in China, Shanghai has become one of the cities with more severe outbreaks. Under the comprehensive consideration of the vaccine coverage rate, the number of Fangcang shelter hospital beds and the number of designated hospital beds in Shanghai, this paper established a deterministic compartmental model and used the Nelder-Mead Simplex Direct Search Algorithm and chi-square values to estimate the model parameters. we calculate ℛ0 = 3.6429 when the number of beds in the Fangcang shelter hospital is relatively tight in the second stage and ℛ0 = 0.4974 in the fifth stage when there are enough beds in both Fangcang shelter hospital and designated hospital. Then we perform a sensitivity analysis on ℛ0 by using perturbation of fixed point estimation of model parameters in the fifth stage, and obtain three parameters that are more sensitive to ℛ0, which are transmission rate (ß 1d ), proportion of the infectious (η) and the hospitalization rate of asymptomatic infected cases (δ 1). Through simulation, we obtain that if the hospitalization rate of asymptomatic infections δ 2 > 0.9373 or the transmission rate ß 1b  < 0.0467, the second stage of Omicron transmission in Shanghai can be well controlled. Finally, we find the measure that converting the National Convention and Exhibition Center (NECC) into a Fangcang shelter hospital has played an important role in curbing the epidemic. Whether this temporary Fangcang shelter hospital is not built or delayed, the cumulative number of confirmed cases will both exceed 100,000, and the cumulative asymptomatic infections will both exceed 1 million. In addition, for a city of 10 million people, we obtain that if a permanent Fangcang shelter hospital with 17,784 beds is built ahead of epidemic, there will be no shortage of beds during the outbreak of Omicron. Our findings enrich the content of the impact of Fangcang shelter hospital beds on the spread of Omicron and confirm the correct policy adopted by the Chinese government.

Precise Detection on Cell-Cell Fusion by a Facile Molecular Beacon-Based Method.

Cell-cell fusion studies provide an experimental platform for evaluating disease progression and investigating cell infection. However, to realize sensitive and quantitative detection on cell-cell fusion is still a challenge. Herein, we report a facile molecular beacon (MB)-based method for precise detection on cell-cell fusion. By transfection of the spike protein (S protein) and enhanced green fluorescent protein (EGFP) in HEK 293 cells, the virus-mimicking fusogenic effector cells 293-S-EGFP cells were constructed to interact with target cells. Before mixing the effector cells with the target cells, the glyceraldehyde-3-phosphate dehydrogenase (GAPDH) expression in 293-S-EGFP cells was silenced, and the MB for GAPDH mRNA detection was delivered into the GAPDH silenced 293-S-EGFP cells. Once cell-cell fusion occurred, MB migrated from the GAPDH silenced effector cells to the target cells and hybridized with GAPDH mRNA in the target cells to induce fluorescence emission. The cell-cell fusion can be easily visualized and quantitated by fluorescence microscopy and flow cytometry. The fluorescence intensity is strongly dependent on the number of fused target cells. This MB-based method can easily identify the differences in the cell fusions for various target cells with different angiotensin-converting enzyme 2 (ACE2) and transmembrane serine protease 2 (TMPRSS2) expression levels, resulting in dramatically different fluorescence intensities in fused target cells. Our study provides a convenient and efficient quantitative detection approach to study cell-cell fusion.

A Deep Language Model for Symptom Extraction From Clinical Text and its Application to Extract COVID-19 Symptoms From Social Media.

Patients experience various symptoms when they haveeither acute or chronic diseases or undergo some treatments for diseases. Symptoms are often indicators of the severity of the disease and the need for hospitalization. Symptoms are often described in free text written as clinical notes in the Electronic Health Records (EHR) and are not integrated with other clinical factors for disease prediction and healthcare outcome management. In this research, we propose a novel deep language model to extract patient-reported symptoms from clinical text. The deep language model integrates syntactic and semantic analysis for symptom extraction and identifies the actual symptoms reported by patients and conditional or negation symptoms. The deep language model can extract both complex and straightforward symptom expressions. We used a real-world clinical notes dataset to evaluate our model and demonstrated that our model achieves superior performance compared to three other state-of-the-art symptom extraction models. We extensively analyzed our model to illustrate its effectiveness by examining each component's contribution to the model. Finally, we applied our model on a COVID-19 tweets data set to extract COVID-19 symptoms. The results show that our model can identify all the symptoms suggested by the Center for Disease Control (CDC) ahead of their timeline and many rare symptoms.

The Impact of Quarantine and Medical Resources on the Control of COVID-19 in Wuhan based on a Household Model.

In order to understand how Wuhan curbed the COVID-19 outbreak in 2020, we build a network transmission model of 123 dimensions incorporating the impact of quarantine and medical resources as well as household transmission. Using our new model, the final infection size of Wuhan is predicted to be 50,662 (95%CI: 46,234, 55,493), and the epidemic would last until April 25 (95%CI: April 23, April 29), which are consistent with the actual situation. It is shown that quarantining close contacts greatly reduces the final size and shorten the epidemic duration. The opening of Fangcang shelter hospitals reduces the final size by about 17,000. Had the number of hospital beds been sufficient when the lockdown started, the number of deaths would have been reduced by at least 54.26%. We also investigate the distribution of infectious individuals in unquarantined households of different sizes. The high-risk households are those with size from two to four before the peak time, while the households with only one member have the highest risk after the peak time. Our findings provide a reference for the prevention, mitigation and control of COVID-19 in other cities of the world.

Prognostic Value of Coronary CT Angiography-Derived Fractional Flow Reserve in Non-obstructive Coronary Artery Disease: A Prospective Multicenter Observational Study

Coronary artery disease (CAD) is a major contributor to morbidity and mortality worldwide. Myocardial ischemia may occur in patients with normal or non-obstructive CAD on invasive coronary angiography (ICA). The comprehensive evaluation of coronary CT angiography (CCTA) integrated with fractional flow reserve derived from CCTA (CT-FFR) to CAD may be essential to improve the outcomes of patients with non-obstructive CAD. China CT-FFR Study-2 (ChiCTR2000031410) is a large-scale prospective, observational study in 29 medical centers in China. The primary purpose is to uncover the relationship between the CCTA findings (including CT-FFR) and the outcome of patients with non-obstructive CAD. At least 10,000 patients with non-obstructive CAD but without previous revascularization will be enrolled. A 5-year follow-up will be performed. The primary endpoint is the occurrence of major adverse cardiovascular events (MACE), including all-cause mortality, non-fatal myocardial infarct, unplanned revascularization, and hospitalization for unstable angina. Clinical characteristics, laboratory and imaging examination results will be collected to analyze their prognostic value.

COVID-19 Information Overload and Cyber Aggression during the Pandemic Lockdown: The Mediating Role of Depression/Anxiety and the Moderating Role of Confucian Responsibility Thinking.

Many countries adopted lockdown measures to curb the spread of the outbreak in 2020, while information about COVID-19 has dominated various media outlets, which has led to information overload for people. However, previous research has mainly focused on cancer information overload and the corresponding consequence, and failed to examine its adverse effects in the context of major public health events. Based on the Frustrate Aggression Theory and the Scapegoat Theory, the present study established a moderated mediation model to investigate the emotional and behavioral outcomes of COVID-19 information overload. The mediating role of depression/anxiety in the association between COVID-19 information overload and cyber aggression, as well as the moderating role of Confucian responsibility thinking, were tested. This model was examined with 1005 Chinese people (mean age = 26.91 years, SD = 9.94) during the COVID-19 outbreak. Mediation analyses revealed that COVID-19 information overload was positively related to cyber aggression, depression, and anxiety, parallelly and partially mediated this relationship. Moderated mediation analyses further indicated that Confucian responsibility thinking not only moderated the direct link between COVID-19 information overload and cyber aggression, with the effect being significant only for people with a low level of Confucian responsibility thinking, but also moderated the relationship between COVID-19 information overload and depression/anxiety respectively, with the associations being much more potent for individuals with low levels of Confucian responsibility thinking. These findings have the potential to inform the development of prevention and intervention programs designed to reduce the negative emotions and cyber aggression associated with information overload in public health events.

The influence of mask use on the spread of COVID-19 during pandemic in New York City.

In New York City, the situation of COVID-19 is so serious that it has caused hundreds of thousands of people to be infected due to its strong infectivity. The desired effect of wearing masks by the public is not ideal, though increasingly recommended by the WHO. In order to reveal the potential effect of mask use, we posed a dynamical model with the effective coverage of wearing face masks to assess the impact of mask use on the COVID-19 transmission. We obtained the basic reproduction number R 0 which determined the global dynamics. According to the implement of policies in New York City, we divided the transmission of COVID-19 in three stages. Based on mathematical model and data, we obtain the mean value R 0 = 1 . 822 in the first stage of New York City, while R 0 = 0 . 6483 in the second stage due to that the US Centers for Disease Control and Prevention (CDC) recommended the public wear masks on April 3, 2020, R 0 = 1 . 024 in the third stage after reopening. It was found that if the effective coverage rate of mask use α exceed a certain value α c = 0 . 182 , COVID-19 can be well controlled in the second stage of New York City. Additionally, when the effective coverage of masks reaches a certain level α = 0 . 5 , the benefits are not obvious with the increased coverage rate compared to the cost of medical resources. Moreover, if the effective coverage of mask use in public reaches 20% in the first stage, then the cumulative confirmed cases will be reduced about 50% by 03 April, 2020. Our results demonstrated a new insight on the effect of mask use in controlling the transmission of COVID-19.

Antibody responses to SARS-CoV-2 in patients with COVID-19.

We report acute antibody responses to SARS-CoV-2 in 285 patients with COVID-19. Within 19 days after symptom onset, 100% of patients tested positive for antiviral immunoglobulin-G (IgG). Seroconversion for IgG and IgM occurred simultaneously or sequentially. Both IgG and IgM titers plateaued within 6 days after seroconversion. Serological testing may be helpful for the diagnosis of suspected patients with negative RT-PCR results and for the identification of asymptomatic infections.

Modeling the early transmission of COVID-19 in New York and San Francisco using a pairwise network model.

Classical epidemiological models assume mass action. However, this assumption is violated when interactions are not random. With the recent COVID-19 pandemic, and resulting shelter in place social distancing directives, mass action models must be modified to account for limited social interactions. In this paper we apply a pairwise network model with moment closure to study the early transmission of COVID-19 in New York and San Francisco and to investigate the factors determining the severity and duration of outbreak in these two cities. In particular, we consider the role of population density, transmission rates and social distancing on the disease dynamics and outcomes. Sensitivity analysis shows that there is a strongly negative correlation between the clustering coefficient in the pairwise model and the basic reproduction number and the effective reproduction number. The shelter in place policy makes the clustering coefficient increase thereby reducing the basic reproduction number and the effective reproduction number. By switching population densities in New York and San Francisco we demonstrate how the outbreak would progress if New York had the same density as San Francisco and vice-versa. The results underscore the crucial role that population density has in the epidemic outcomes. We also show that under the assumption of no further changes in policy or transmission dynamics not lifting the shelter in place policy would have little effect on final outbreak size in New York, but would reduce the final size in San Francisco by 97%.

How the clinical research community responded to the COVID-19 pandemic: an analysis of the COVID-19 clinical studies in ClinicalTrials.gov.

OBJECTIVE: In the past few months, a large number of clinical studies on the novel coronavirus disease (COVID-19) have been initiated worldwide to find effective therapeutics, vaccines, and preventive strategies for COVID-19. In this study, we aim to understand the landscape of COVID-19 clinical research and identify the issues that may cause recruitment difficulty or reduce study generalizability. METHODS: We analyzed 3765 COVID-19 studies registered in the largest public registry-ClinicalTrials.gov, leveraging natural language processing (NLP) and using descriptive, association, and clustering analyses. We first characterized COVID-19 studies by study features such as phase and tested intervention. We then took a deep dive and analyzed their eligibility criteria to understand whether these studies: (1) considered the reported underlying health conditions that may lead to severe illnesses, and (2) excluded older adults, either explicitly or implicitly, which may reduce the generalizability of these studies to the older adults population. RESULTS: Our analysis included 2295 interventional studies and 1470 observational studies. Most trials did not explicitly exclude older adults with common chronic conditions. However, known risk factors such as diabetes and hypertension were considered by less than 5% of trials based on their trial description. Pregnant women were excluded by 34.9% of the studies. CONCLUSIONS: Most COVID-19 clinical studies included both genders and older adults. However, risk factors such as diabetes, hypertension, and pregnancy were under-represented, likely skewing the population that was sampled. A careful examination of existing COVID-19 studies can inform future COVID-19 trial design towards balanced internal validity and generalizability.

Uncovering physical and attitudinal barriers to adherence to precautions for preventing the transmission of covid-19 and anxiety level of people in wuhan: 2 months after the lockdown

BACKGROUND: Wuhan, the epicenter of the coronavirus diseases 2019 outbreak, was locked down on January 23, 2020. We aimed to investigate the barriers to the physical prevention, negative attitudes, and anxiety levels. METHODS: A online cross-sectional survey was conducted with the people living in Wuhan between March 12th and 23rd, 2020. RESULTS: Of a total of 2411 complete responses, the mean and standard deviation for the total physical prevention barriers score was 19.73 (standard deviation +/- 5.3;range 12-45) out of a possible score of 48. Using a cut-off score of 44 for the State-Trait Inventory score, 79.9% (95% confidence interval [CI]: 78.2-81.5) of the participants reported moderate to severe anxiety during the early phase of the outbreak, and 51.3% (95% CI 49.2-53.3) reported moderate to severe anxiety after the peak of coronavirus diseases 2019 was over (during the study period). Comparing anxiety levels in the early phase of the outbreak and after the peak of the outbreak, 58.5% (95% CI 56.5-60.5) recorded a decreased anxiety. Females reported a higher likelihood of having decreased levels of anxiety than males (odds ratio = 1.78, 95% CI 1.48-2.14). Low negative attitudes score were associated with a higher decrease in anxiety (odds ratio = 1.59, 95% CI 1.33-1.89). CONCLUSIONS: The attitudinal barriers to prevention of transmission of coronavirus diseases 2019 are more prominent than physical prevention barriers after the peak of coronavirus diseases 2019. High anxiety levels even after the peak warrant serious attention. (PsycInfo Database Record (c) 2021 APA, all rights reserved)

Nonpharmaceutical interventions contribute to the control of COVID-19 in China based on a pairwise model.

Nonpharmaceutical interventions (NPIs), particularly contact tracing isolation and household quarantine, play a vital role in effectively bringing the Coronavirus Disease 2019 (COVID-19) under control in China. The pairwise model, has an inherent advantage in characterizing those two NPIs than the classical well-mixed models. Therefore, in this paper, we devised a pairwise epidemic model with NPIs to analyze COVID-19 outbreak in China by using confirmed cases during February 3rd-22nd, 2020. By explicitly incorporating contact tracing isolation and family clusters caused by household quarantine, our model provided a good fit to the trajectory of COVID-19 infections. We calculated the reproduction number R = 1.345 (95% CI: 1.230 - 1.460) for Hubei province and R = 1.217 (95% CI: 1.207 - 1.227) for China (except Hubei). We also estimated the peak time of infections, the epidemic duration and the final size, which are basically consistent with real observation. We indicated by simulation that the traced high-risk contacts from incubated to susceptible decrease under NPIs, regardless of infected cases. The sensitivity analysis showed that reducing the exposure of the susceptible and increasing the clustering coefficient bolster COVID-19 control. With the enforcement of household quarantine, the reproduction number R and the epidemic prevalence declined effectively. Furthermore, we obtained the resumption time of work and production in China (except Hubei) on 10th March and in Hubei at the end of April 2020, respectively, which is broadly in line with the actual time. Our results may provide some potential lessons from China on the control of COVID-19 for other parts of the world.

Hematologic changes predict clinical outcome in recovered patients with COVID-19.

2019 coronavirus disease (COVID-19) presents as a newly recognized pneumonia that has brought about a global pandemic and is increasingly considered as a systemic illness. We investigated the clinical and laboratory features of recovered COVID-19 patients without pre-existing hematologic diseases at Wuhan No. 1 Hospital. Fifty-nine male and 68 female Chinese patients were included with the median age at 64 years in the present study. Eosinopenia (37.80%), monocytosis (51.97%), lymphocytopenia (25.20%), and anemia (51.97%) were the most common hematologic findings in our cohort, particularly in severe or critically ill COVID-19. The levels of changes in leukocytes, neutrophils, lymphocytes, monocytes, eosinophils, basophils, platelets, hemoglobin levels, mean corpuscular volume (MCV), and mean cell hemoglobin concentration (MCHC) are overall associated with lung involvement, oxygen demand, and disease activity. However, changes of eosinophils (end hospitalization-baseline) (coefficients = 10.32; 95% CI = 1.03-19.60, P = 0.03) and basophils (Max - Min) (coefficients = 71.43; 95% CI = 8.55-134.31, P = 0.03) were independent predictors of delayed recovery in the hospital by the multivariate analysis in this recovered population. A variety of hematologic changes are associated with the severity and clinical outcome of recovered COVID-19 patients, which warrants further exploration of their underlying mechanisms.

Kindergartens Reopening in the Period of Regular Epidemic Prevention and Control, Benefitial or Harmful?

In the period of regular epidemic prevention and control of Coronavirus disease 2019 (COVID-19) in our country, work resumption has been fully advanced. But there are still new sporadic local cases and imported cases across the country. In this situation, whether kindergartens reopening will increase the risk of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spread still remains uncertain. We reviewed two pediatric patients with moderate COVID-19, collected the epidemiologic information and monitored the cycle threshold value of rectal specimen and the viral loads, and discussed the transmission of SARS-CoV-2 in pediatric patients and the virulence of feces in children with moderate COVID-19, in order to analyze the risk of kindergartens reopening.

T cell immunobiology and cytokine storm of COVID-19.

2019 coronavirus disease (COVID-19) presents as a newly recognized pneumonia and could rapidly progress into acute respiratory distress syndrome which has brought about a global pandemic. Until now, no curative therapy has been strongly recommended for COVID-19 except for personalized supportive care. T cells and virus-specific T cells are essential to protect against virus infection, including COVID-19. Delayed immune reconstitution (IR) and cytokine storm (CS) remain serious obstacles for the cure of COVID-19. Most COVID-19 patients, especially among elderly patients, had marked lymphopenia and increased neutrophils, but T cell counts in severe COVID-19 patients surviving the disease gradually restored later. Elevated pro-inflammatory cytokines, particularly IL-6, IL-10, IL-2 and IL-17, and exhausted T cells are found in peripheral blood and the lungs. It suggests that Thymosin α1 and adoptive COVID-19-specific T cells could improve IR, while convalescent plasma, IL-6 blockade, mesenchymal stem cells and corticosteroids could suppress CS. More clinical studies in this field worldwide are urgently warranted to pave the way for therapy of COVID-19 in the future.

How the clinical research community responded to the COVID-19 pandemic: An analysis of the COVID-19 clinical studies in ClinicalTrials.gov.

OBJECTIVE: The novel coronavirus disease (COVID-19), broke out in December 2019, and is now a global pandemic. In the past few months, a large number of clinical studies have been initiated worldwide to find effective therapeutics, vaccines, and preventive strategies for COVID-19. In this study, we aim to understand the landscape of COVID-19 clinical research and identify the gaps such as the lack of population representativeness and issues that may cause recruitment difficulty. MATERIALS AND METHODS: We analyzed 3,765 COVID-19 studies registered in the largest public registry - ClinicalTrials.gov, leveraging natural language processing and using descriptive, association, and clustering analyses. We first characterized COVID-19 studies by study features such as phase and tested intervention. We then took a deep dive and analyzed their eligibility criteria to understand whether these studies: (1) considered the reported underlying health conditions that may lead to severe illnesses, and (2) excluded older adults, either explicitly or implicitly, which may reduce the generalizability of these studies to the older adults population. RESULTS: Most trials did not have an upper age limit and did not exclude patients with common chronic conditions such as hypertension and diabetes that are more prevalent in older adults. However, known risk factors that may lead to severe illnesses have not been adequately considered. CONCLUSIONS: A careful examination of existing COVID-19 studies can inform future COVID-19 trial design towards balanced internal validity and generalizability.