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1.
Innov Aging ; 6(4): igac004, 2022.
Article in English | MEDLINE | ID: covidwho-1908808

ABSTRACT

Background and Objectives: Nursing staff turnover is a substantial concern for nursing homes that care for millions of older individuals, especially during the coronavirus disease 2019 pandemic. Low pay is considered as one of the key reasons for high turnover. However, we do not know whether increasing wages can lead to lower turnover. In this study, we fill this gap in our understanding by analyzing the relationship between wages and nursing staff turnover. Research Design and Methods: We obtained data on hourly wages (Medicare Cost Reports), turnover (Iowa Department of Human Services), and nursing home and resident characteristics (Nursing Home Compare and LTCFocus) from 2013 to 2017. We summarized the characteristics of nursing homes as well as turnover trends over time. Next, we used pooled ordinary least squares (OLS) and facility fixed effects regressions to examine the relationship between wages and turnover adjusting for nursing home and resident characteristics. Results: Among the 396 nursing homes in Iowa, average hourly wage was $27.0 for registered nurses (RNs), $21.6 for licensed practical nurses (LPNs), and $14.1 for certified nurse aides (CNAs) during 2013-2017. Average turnover rates were increasing over time for all staff types and in 2017, turnover rates were 46.0% for RNs, 44.4% for LPNs, and 64.7% for CNAs. In both pooled OLS and facility fixed effects regressions, higher wages were associated with lower turnover of CNAs but not LPNs or RNs. The magnitude of the effect of wages on turnover for CNAs was lower in facility fixed effects regressions. Discussion and Implications: We found a significant relationship between hourly wages and turnover for CNAs but not for LPNs or RNs. Focusing on higher wages alone may not lead to lower turnover of all types of nursing staff in nursing homes. We should also focus on nonwage factors related to turnover.

2.
J Big Data ; 8(1): 99, 2021.
Article in English | MEDLINE | ID: covidwho-1808391

ABSTRACT

The early detection of the coronavirus disease 2019 (COVID-19) outbreak is important to save people's lives and restart the economy quickly and safely. People's social behavior, reflected in their mobility data, plays a major role in spreading the disease. Therefore, we used the daily mobility data aggregated at the county level beside COVID-19 statistics and demographic information for short-term forecasting of COVID-19 outbreaks in the United States. The daily data are fed to a deep learning model based on Long Short-Term Memory (LSTM) to predict the accumulated number of COVID-19 cases in the next two weeks. A significant average correlation was achieved (r=0.83 (p = 0.005)) between the model predicted and actual accumulated cases in the interval from August 1, 2020 until January 22, 2021. The model predictions had r > 0.7 for 87% of the counties across the United States. A lower correlation was reported for the counties with total cases of <1000 during the test interval. The average mean absolute error (MAE) was 605.4 and decreased with a decrease in the total number of cases during the testing interval. The model was able to capture the effect of government responses on COVID-19 cases. Also, it was able to capture the effect of age demographics on the COVID-19 spread. It showed that the average daily cases decreased with a decrease in the retiree percentage and increased with an increase in the young percentage. Lessons learned from this study not only can help with managing the COVID-19 pandemic but also can help with early and effective management of possible future pandemics. The code used for this study was made publicly available on https://github.com/Murtadha44/covid-19-spread-risk. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40537-021-00491-1.

3.
Chin Med J (Engl) ; 135(6): 691-696, 2022 Mar 20.
Article in English | MEDLINE | ID: covidwho-1806655

ABSTRACT

BACKGROUND: Coronavirus disease 2019 (Covid-19) remains a serious health threat worldwide. We aimed to investigate whether low molecular weight heparin (LMWH) can promote organ function recovery in moderate Covid-19 pneumonia patients. METHODS: We initiated an LMWH protocol in Covid-19 patients with increased D-dimer, body mass index >30 kg/m2 or a history of diabetes from January 18, 2020 at Shanghai Public Health Clinical Center. In this retrospective study, we assigned moderate Covid- 19 pneumonia patients admitted between January 18th and April 18, 2020 receiving the LMWH protocol to the LMWH group. Moderate patients who met the inclusion criteria but did not receive LMWH protocol were included in the control group by 1:2 propensity score matching. General clinical information, indicators for renal function, arterial blood gas analyses, arterial blood lactic acid content (mmol/L), and coagulation indexes at 0 day, 3 days, 7 days, and 11 days after admission were recorded and compared between the two groups. RESULTS: There were 41 patients in the LMWH group and 82 patients in the control group. General information in both groups were similar. Compared to the control group, the arterial blood lactic acid content (mmol/L) at day 11 (1.3 [1.1, 1.7] vs. 1.2 [0.9, 1.3], P = 0.016) was reduced in the LMWH group. The estimated glomerular filtration rate (eGFR) in the LMWH group was higher than that in the control group at day 7 (108.54 [89.11, 128.17] vs. 116.85 [103.39, 133.47], P = 0.039) and day 11 (113.74 [94.49, 126.34] vs. 128.31 [112.75, 144, 12], P  = 0.003). The serum creatinine levels (Scr) in the LMWH group were lower than that in the control group at day 7 (62.13 [51.47, 77.64] vs. 55.49 [49.50, 65.75], P = 0.038) and day 11 (63.35 [50.17, 75.73] vs. 51.62 [44.62, 61.24], P = 0.005). CONCLUSIONS: LMWH treatment can reduce arterial blood lactic acid levels and improve eGFR in moderate Covid-19 pneumonia patients. Randomized controlled trials are warranted to further investigate this issue. TRIAL REGISTRATION: ChiCTR.org.cn, ChiCTR2000034796.


Subject(s)
COVID-19 , Heparin, Low-Molecular-Weight , China , Glomerular Filtration Rate , Heparin, Low-Molecular-Weight/therapeutic use , Humans , Lactic Acid , Retrospective Studies
4.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-316025

ABSTRACT

Background: Coronavirus disease 2019 (Covid-19) remains a serious health threat worldwide. It is crucial to explore effective treatment measures that reduce mortality. Our aim was to investigate whether low molecular weight heparin (LMWH) can reduce organ injury in patients with Covid-19 pneumonia. Methods: A retrospective study was conducted at the Shanghai Public Health Clinical Center. We initiated a LMWH protocol from January 18th 2020. LMWH was injected subcutaneously at 4100U per day until the D-dimer(DD) level returned to normal, or 5-7 days after admission, whichever occurred first. Admitted patients who received LMWH between January 18th and February 17th 2020 were assigned to the LMWH group. Patients admitted between January 18th and February 17th who did not receive LMWH anticoagulant therapy were the control group. All patients in both groups were aged >18 years, were not pregnant, had no tumors and were in accordance with the following inclusion criteria: 1) DD increased on admission;2) Body mass index(BMI) >30;3) History of diabetes. The exclusion criteria were: 1) Platelets <30x10 9 /L or fibrinogen <150 mg/dL;2) Pregnancy and lactation;3) Presence of blood system diseases;4) Immunosuppression;5) Diseases with a potential risk of bleeding;6) Receiving anticoagulant drugs or antiplatelet drugs during treatment. General clinical information, indicators for renal function, arterial blood gas analyses and blood lactic acid content were recorded in the two groups 0 (Day 0), 3 (Day 3), 7 (Day 7), and 11 (Day 11) and 15 (Day 15) days after admission. Results: There were 48 patients in the LMWH group and 74 patients in the control group. General information, including age, gender, co-existing diseases and onset-to-admission time in both groups was similar. Compared to the control group, LMWH treatment improved the estimated glomerular filtration rate (eGFR) reduced the serum creatinine level (Scr), blood urea nitrogen (BUN),arterial blood carbon dioxide partial pressure (PaCO2) and arterial blood lactic acid content. However, LMWH treatment reduced arterial oxygen partial pressure (PaO2) and arterial oxygen saturation (SaO2). Conclusion: LMWH might be beneficial to improve renal function, CO2 discharge and microcirculation during the early phase of Covid-19 patients . Further randomized controlled trials(RCTs) are warranted in order to further investigate this issue. Trial registration ChiCTR, ChiCTR2000034796. Registered 19 July 2020 - Retrospectively registered, http:// www. chictr.org.cn/listbycreater.aspx.

5.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-312615

ABSTRACT

The early detection of the coronavirus disease 2019 (COVID-19) outbreak is important to save people's lives and restart the economy quickly and safely. People’s social behavior as captured by their mobility data plays a role in spreading the disease. Therefore, we used the daily mobility data aggregated at the county level beside COVID-19 statistics and demographic information for short-term forecasting of COVID-19 outbreak in the United States. The daily data are fed to a deep model based on Long Short-Term Memory (LSTM) to predict the accumulated number of COVID-19 cases in the next two weeks. A significant average correlation was achieved (r=0.83 (p=0.005)) between the model prediction and the actual accumulated cases in the interval from August 1, 2020 until January 22, 2021. The model predictions had r > 0.7 for 87% of the counties across the United States. Lower correlation was reported for the counties with a total cases of <1,000 during the test interval. The average mean absolute error (MAE) was 605.4, and it was decreasing with the decrease in the total number of cases during the testing interval. The model was able to capture the effect of government responses on COVID-19 cases. Also, it was able to capture the effect of age demographics on the COVID-19 spread where average daily cases decrease with the decrease in retires percentage, and increase with the increase in young percentage. Lessons learned from this study not only can help with managing the COVID-19 pandemic but also could also help with early and effective management of possible future pandemics.

6.
Innovation in aging ; 5(Suppl 1):570-571, 2021.
Article in English | EuropePMC | ID: covidwho-1624212

ABSTRACT

Employee turnover is a huge concern for nursing homes that care for millions of older individuals whose physical and cognitive impairments make them vulnerable, especially in the middle of a pandemic like COVID-19. Existing research has shown that high turnover of employees can lead to poorer quality of care. Low pay is often cited as one of the key reasons for high turnover of employees in nursing homes. For-profit nursing homes may try to maximize profits by limiting wages paid to their employees. In this study, we examine whether profit-status of a facility is associated with high turnover of its employees. We obtain data on 415 nursing homes operating in Iowa between 2013-2017. We descriptively examine the turnover trends in nurse employees and all employees over time by profit status. We evaluate whether profit status is associated with high turnover using pooled linear regressions controlling for nursing home and resident characteristics. Descriptive results show that for-profit facilities had higher turnover of nurse employees (61.1% vs. 49.6%) and all employees (56.6% vs. 45.4%). Results from multivariate regressions show that, compared to non-profit facilities, for-profit facilities had 6.93 percentage points higher (p<0.01) turnover of all employees, and 7.76 percentage points higher (p<0.01) turnover of nurse employees after controlling for facility and resident characteristics. Given existing evidence on the adverse impact of high employee turnover on nursing home quality, we need policies aimed at lowering employee turnover, targeting for-profit nursing homes.

7.
Am J Chin Med ; 50(1): 33-51, 2022.
Article in English | MEDLINE | ID: covidwho-1608685

ABSTRACT

Qingfei Paidu decoction (QFPD) has been repeatedly recommended for the clinical treatment of novel coronavirus disease 2019 (COVID-19) in multiple provinces throughout China. A possible complication of COVID-19 lung involvement is pulmonary fibrosis, which causes chronic breathing difficulties and affects the patient's quality of life. Therefore, there is an important question regarding whether QFPD can alleviate the process of pulmonary fibrosis and its potential mechanisms. To explore this issue, this study demonstrated the anti-pulmonary fibrosis activity and mode of action of QFPD in vivo and in vitro pulmonary fibrosis models and network pharmacology. The results showed that QFPD effectively ameliorated the bleomycin-induced inflammation and collagen deposition in mice and significantly improved the epithelial-mesenchymal transition in pulmonary fibrosis in mice. In addition, QFPD inhibited bleomycin-induced M2 polarization of macrophages in pulmonary tissues. An in-depth study of the mechanism of QFPD in the treatment of pulmonary fibrosis based on network pharmacology and molecular simulation revealed that SRC was the main target of QFPD and sitosterol (a key compound in QFPD). QFPD and sitosterol regulate the EMT process and M2 polarization of macrophages by inhibiting the activation of SRC, thereby alleviating pulmonary fibrosis in mice. COVID-19 infection might produce severe fibrosis, and antifibrotic therapy with QFPD may be valuable in preventing severe neocoronavirus disease in patients with IPF, which could be a key factor explaining the role of QFPD in the treatment of COVID-19.


Subject(s)
COVID-19 , Pulmonary Fibrosis , Animals , Drugs, Chinese Herbal , Epithelial-Mesenchymal Transition , Humans , Mice , Mice, Inbred C57BL , Pulmonary Fibrosis/drug therapy , Pulmonary Fibrosis/etiology , Quality of Life , SARS-CoV-2
8.
Med Care ; 59(12): 1099-1106, 2021 12 01.
Article in English | MEDLINE | ID: covidwho-1447673

ABSTRACT

BACKGROUND: The Skilled Nursing Facility Value-based Purchasing Program (SNF-VBP) incentivizes facilities to coordinate care, improve quality, and lower hospital readmissions. However, SNF-VBP may unintentionally punish facilities with lower profit margins struggling to invest resources to lower readmissions. OBJECTIVE: The objective of this study was to estimate the SNF-VBP penalty amounts by skilled nursing facility (SNF) profit margin quintiles and examine whether facilities with lower profit margins are more likely to be penalized by SNF-VBP. RESEARCH DESIGN: We combined the first round of SNF-VBP performance data with SNF profit margins and characteristics data. Our outcome variables included estimated penalty amount and a binary measure for whether facilities were penalized by the SNF-VBP. We categorized SNFs into 5 profit margin quintiles and examined the relationship between profit margins and SNF-VBP performance using descriptive and regression analysis. RESULTS: The average profit margins for SNFs in the lowest profit margin quintile was -14.4% compared with the average profit margin of 11.1% for SNFs in the highest profit margin quintile. In adjusted regressions, SNFs in the lowest profit margin quintile had 17% higher odds of being penalized under SNF-VBP compared with facilities in the highest profit margin quintile. The average penalty for SNFs in the lowest profit margin quintile was $22,312. CONCLUSIONS: SNFs in the lowest profit margins are more likely to be penalized by the SNF-VBP, and these losses can exacerbate quality problems in SNFs with lower quality. Alternative approaches to measuring and rewarding SNFs under SNF-VBP or programs to assist struggling SNFs is warranted, particularly considering the coronavirus disease 2019 pandemic, which requires resources for prevention and management.


Subject(s)
Skilled Nursing Facilities/economics , Skilled Nursing Facilities/statistics & numerical data , Value-Based Purchasing/economics , Value-Based Purchasing/statistics & numerical data , Medicare/organization & administration , Reimbursement, Incentive/organization & administration , United States
9.
Cytokine Growth Factor Rev ; 61: 2-15, 2021 10.
Article in English | MEDLINE | ID: covidwho-1275255

ABSTRACT

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), exhibits a wide spectrum of clinical presentations, ranging from asymptomatic cases to severe pneumonia or even death. In severe COVID-19 cases, an increased level of proinflammatory cytokines has been observed in the bloodstream, forming the so-called "cytokine storm". Generally, nucleotide-binding oligomerization domain-like receptor containing pyrin domain 3 (NLRP3) inflammasome activation intensely induces cytokine production as an inflammatory response to viral infection. Therefore, the NLRP3 inflammasome can be a potential target for the treatment of COVID-19. Hence, this review first introduces the canonical NLRP3 inflammasome activation pathway. Second, we review the cellular/molecular mechanisms of NLRP3 inflammasome activation by SARS-CoV-2 infection (e.g., viroporins, ion flux and the complement cascade). Furthermore, we describe the involvement of the NLRP3 inflammasome in the pathogenesis of COVID-19 (e.g., cytokine storm, respiratory manifestations, cardiovascular comorbidity and neurological symptoms). Finally, we also propose several promising inhibitors targeting the NLRP3 inflammasome, cytokine products and neutrophils to provide novel therapeutic strategies for COVID-19.


Subject(s)
COVID-19/drug therapy , COVID-19/metabolism , Inflammasomes/metabolism , NLR Family, Pyrin Domain-Containing 3 Protein/metabolism , SARS-CoV-2/pathogenicity , Cytokine Release Syndrome/drug therapy , Cytokine Release Syndrome/metabolism , Humans , Inflammasomes/drug effects
10.
Front Pharmacol ; 12: 638556, 2021.
Article in English | MEDLINE | ID: covidwho-1221963

ABSTRACT

Background: Coronavirus disease 2019 (COVID-19) pandemic is continuing to impact multiple countries worldwide and effective treatment options are still being developed. In this study, we investigate the potential of high-dose intravenous vitamin C (HDIVC) in the prevention of moderate COVID-19 disease aggravation. Methods: In this retrospective before-after case-matched clinical study, we compare the outcome and clinical courses of patients with moderate COVID-19 patients who were treated with an HDIVC protocol (intravenous injection of vitamin C, 100 mg/kg/day, 1 g/h, for 7 days from admission) during a one-month period (between March 18 and april 18, 2020, HDIVC group) with a control group treated without the HDIVC protocol during the preceding two months (January 18 to March 18, 2020). Patients in the two groups were matched in a 1:1 ratio according to age and gender. Results: The HDIVC and control groups each comprised 55 patients. For the primary outcomes, there was a significant difference in the number of patients that evolved from moderate to severe type between the two groups (HDIVC: 4/55 vs. control: 12/55, relative risk [RR] = 0.28 [0.08, 0.93], P = 0.03). Compared to the control group, there was a shorter duration of systemic inflammatory response syndrome (SIRS) (P = 0.0004) during the first week and lower SIRS occurrence (2/21 vs 10/22, P = 0.0086) on Day 7 (6-7 days after admission). In addition, HDIVC group had lower C-reactive protein levels (P = 0.005) and higher number of CD4+ T cells from Day 0 (on admission) to Day 7 (P = 0.04)." The levels of coagulation indicators, including activated partial thromboplastin time and D-dimer were also improved in the HDIVC compared to the control group on Day 7. Conclusion: HDIVC may be beneficial in limiting disease aggravation in the early stage of COVID-19 pneumonia, which may be related to its improvements on the inflammatory response, immune function and coagulation function. Further randomized controlled trials are required to augment these findings.

11.
J Am Med Dir Assoc ; 22(11): 2378-2383.e2, 2021 Nov.
Article in English | MEDLINE | ID: covidwho-1209643

ABSTRACT

OBJECTIVES: Recent rampant spread of COVID-19 cases in nursing homes has highlighted the concerns around nursing homes' ability to contain the spread of infections. The ability of nursing homes to invest in quality improvement initiatives may depend on resource availability. In this study, we sought to examine whether lower profit margins, as a proxy for lack of resources, are associated with persistent infection control citations. DESIGN: We conducted a retrospective study. SETTING AND PARTICIPANTS: Medicare-certified nursing homes in the US with financial and facility characteristics data (n = 12,194). METHODS: We combined facility-level data on nursing home profit margins from Medicare Cost Reports with deficiency citation data from Nursing Home Compare (2017-2019) and facility characteristics data from LTCFocus.org. We descriptively analyzed infection control citations by profit margins quintiles. We used logistic regressions to examine the relationship between profit margin quintiles and citations for infection prevention and control, adjusting for facility and market characteristics. RESULTS: About three-fourths of all facilities received deficiency citations for infection prevention and control during 1 or more years from 2017 to 2019 with about 10% of facilities cited in all 3 years. Facilities in the highest profit margin quintile had 7.6% of facilities with citations for infection prevention and control in each of the 3 years compared with 8.1%, 10.0%, 10.7%, and 13.7% for facilities in the fourth, third, second, and first quintiles of profit margins, respectively. Multivariable regressions showed that facilities with the lowest profit margins (first quintile) had 54.3% higher odds of being cited in at least 1 year and 87.6% higher odds of being cited in each of the 3 years compared with facilities with the highest profit margins (fifth quintile). CONCLUSIONS AND IMPLICATIONS: Our findings indicate that nursing homes may need more resources to prevent citations for infection prevention and control.


Subject(s)
COVID-19 , Medicare , Aged , Humans , Nursing Homes , Quality of Health Care , Retrospective Studies , SARS-CoV-2 , United States
12.
J Big Data ; 8(1): 33, 2021.
Article in English | MEDLINE | ID: covidwho-1105746

ABSTRACT

This project is funded by the US National Science Foundation (NSF) through their NSF RAPID program under the title "Modeling Corona Spread Using Big Data Analytics." The project is a joint effort between the Department of Computer & Electrical Engineering and Computer Science at FAU and a research group from LexisNexis Risk Solutions. The novel coronavirus Covid-19 originated in China in early December 2019 and has rapidly spread to many countries around the globe, with the number of confirmed cases increasing every day. Covid-19 is officially a pandemic. It is a novel infection with serious clinical manifestations, including death, and it has reached at least 124 countries and territories. Although the ultimate course and impact of Covid-19 are uncertain, it is not merely possible but likely that the disease will produce enough severe illness to overwhelm the worldwide health care infrastructure. Emerging viral pandemics can place extraordinary and sustained demands on public health and health systems and on providers of essential community services. Modeling the Covid-19 pandemic spread is challenging. But there are data that can be used to project resource demands. Estimates of the reproductive number (R) of SARS-CoV-2 show that at the beginning of the epidemic, each infected person spreads the virus to at least two others, on average (Emanuel et al. in N Engl J Med. 2020, Livingston and Bucher in JAMA 323(14):1335, 2020). A conservatively low estimate is that 5 % of the population could become infected within 3 months. Preliminary data from China and Italy regarding the distribution of case severity and fatality vary widely (Wu and McGoogan in JAMA 323(13):1239-42, 2020). A recent large-scale analysis from China suggests that 80 % of those infected either are asymptomatic or have mild symptoms; a finding that implies that demand for advanced medical services might apply to only 20 % of the total infected. Of patients infected with Covid-19, about 15 % have severe illness and 5 % have critical illness (Emanuel et al. in N Engl J Med. 2020). Overall, mortality ranges from 0.25 % to as high as 3.0 % (Emanuel et al. in N Engl J Med. 2020, Wilson et al. in Emerg Infect Dis 26(6):1339, 2020). Case fatality rates are much higher for vulnerable populations, such as persons over the age of 80 years (> 14 %) and those with coexisting conditions (10 % for those with cardiovascular disease and 7 % for those with diabetes) (Emanuel et al. in N Engl J Med. 2020). Overall, Covid-19 is substantially deadlier than seasonal influenza, which has a mortality of roughly 0.1 %. Public health efforts depend heavily on predicting how diseases such as those caused by Covid-19 spread across the globe. During the early days of a new outbreak, when reliable data are still scarce, researchers turn to mathematical models that can predict where people who could be infected are going and how likely they are to bring the disease with them. These computational methods use known statistical equations that calculate the probability of individuals transmitting the illness. Modern computational power allows these models to quickly incorporate multiple inputs, such as a given disease's ability to pass from person to person and the movement patterns of potentially infected people traveling by air and land. This process sometimes involves making assumptions about unknown factors, such as an individual's exact travel pattern. By plugging in different possible versions of each input, however, researchers can update the models as new information becomes available and compare their results to observed patterns for the illness. In this paper we describe the development a model of Corona spread by using innovative big data analytics techniques and tools. We leveraged our experience from research in modeling Ebola spread (Shaw et al. Modeling Ebola Spread and Using HPCC/KEL System. In: Big Data Technologies and Applications 2016 (pp. 347-385). Springer, Cham) to successfully model Corona spread, we will obtain new results, and help in reducing the number of Corona patients. We closely collaborated with LexisNexis, which is a leading US data analytics company and a member of our NSF I/UCRC for Advanced Knowledge Enablement. The lack of a comprehensive view and informative analysis of the status of the pandemic can also cause panic and instability within society. Our work proposes the HPCC Systems Covid-19 tracker, which provides a multi-level view of the pandemic with the informative virus spreading indicators in a timely manner. The system embeds a classical epidemiological model known as SIR and spreading indicators based on causal model. The data solution of the tracker is built on top of the Big Data processing platform HPCC Systems, from ingesting and tracking of various data sources to fast delivery of the data to the public. The HPCC Systems Covid-19 tracker presents the Covid-19 data on a daily, weekly, and cumulative basis up to global-level and down to the county-level. It also provides statistical analysis for each level such as new cases per 100,000 population. The primary analysis such as Contagion Risk and Infection State is based on causal model with a seven-day sliding window. Our work has been released as a publicly available website to the world and attracted a great volume of traffic. The project is open-sourced and available on GitHub. The system was developed on the LexisNexis HPCC Systems, which is briefly described in the paper.

14.
Clin Cardiol ; 43(7): 796-802, 2020 Jul.
Article in English | MEDLINE | ID: covidwho-622252

ABSTRACT

BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) could cause virulent infection leading to Corona Virus Disease 2019 (COVID-19)-related pneumonia as well as multiple organ injuries. HYPOTHESIS: COVID-19 infection may result in cardiovascular manifestations leading to worse clinical outcome. METHODS: Fifty four severe and critical patients with confirmed COVID-19 were enrolled. Risk factors predicting the severity of COVID-19 were analyzed. RESULTS: Of the 54 patients (56.1 ± 13.5 years old, 66.7% male) with COVID-19, 39 were diagnosed as severe and 15 as critical cases. The occurrence of diabetes, the level of D-dimer, inflammatory and cardiac markers in critical cases were significantly higher. Troponin I (TnI) elevation occurred in 42.6% of all the severe and critical patients. Three patients experienced hypotension at admission and were all diagnosed as critical cases consequently. Hypotension was found in one severe case and seven critical cases during hospitalization. Sinus tachycardia is the most common type of arrythmia and was observed in 23 severe patients and all the critical patients. Atrioventricular block and ventricular tachycardia were observed in critical patients at end stage while bradycardia and atrial fibrillation were less common. Mild pericardial effusion was observed in one severe case and five critical cases. Three critical cases suffered new onset of heart failure. Hypotension during treatment, severe myocardial injury and pericardial effusion were independent risk factors predicting the critical status of COVID-19 infection. CONCLUSION: This study has systemically observed the impact of COVID-19 on cardiovascular system, including myocardial injury, blood pressure, arrythmia and cardiac function in severe and critical cases. Monitoring of vital signs and cardiac function of COVID-19 patients and applying potential interventions especially for those with hypotension during treatment, severe myocardial injury or pericardial effusion, is of vital importance.


Subject(s)
Cardiovascular Diseases/diagnosis , Cardiovascular Diseases/epidemiology , Cause of Death , Coronavirus Infections/diagnosis , Coronavirus Infections/epidemiology , Pneumonia, Viral/diagnosis , Pneumonia, Viral/epidemiology , Adult , Aged , COVID-19 , Cardiovascular Diseases/therapy , China/epidemiology , Cohort Studies , Comorbidity , Coronavirus Infections/therapy , Critical Illness/mortality , Critical Illness/therapy , Female , Humans , Incidence , Logistic Models , Male , Middle Aged , Monitoring, Physiologic , Multivariate Analysis , Pandemics , Pneumonia, Viral/therapy , Retrospective Studies , Risk Assessment , Severity of Illness Index , Survival Analysis
15.
Cell ; 182(3): 722-733.e11, 2020 08 06.
Article in English | MEDLINE | ID: covidwho-628738

ABSTRACT

Vaccines are urgently needed to control the ongoing pandemic COVID-19 and previously emerging MERS/SARS caused by coronavirus (CoV) infections. The CoV spike receptor-binding domain (RBD) is an attractive vaccine target but is undermined by limited immunogenicity. We describe a dimeric form of MERS-CoV RBD that overcomes this limitation. The RBD-dimer significantly increased neutralizing antibody (NAb) titers compared to conventional monomeric form and protected mice against MERS-CoV infection. Crystal structure showed RBD-dimer fully exposed dual receptor-binding motifs, the major target for NAbs. Structure-guided design further yielded a stable version of RBD-dimer as a tandem repeat single-chain (RBD-sc-dimer) which retained the vaccine potency. We generalized this strategy to design vaccines against COVID-19 and SARS, achieving 10- to 100-fold enhancement of NAb titers. RBD-sc-dimers in pilot scale production yielded high yields, supporting their scalability for further clinical development. The framework of immunogen design can be universally applied to other beta-CoV vaccines to counter emerging threats.


Subject(s)
Betacoronavirus/immunology , Coronavirus Infections/prevention & control , Middle East Respiratory Syndrome Coronavirus/immunology , Pandemics/prevention & control , Pneumonia, Viral/prevention & control , SARS Virus/immunology , Universal Design , Angiotensin-Converting Enzyme 2 , Animals , Antibodies, Neutralizing/immunology , Antibodies, Viral/immunology , Betacoronavirus/chemistry , COVID-19 , COVID-19 Vaccines , Cell Line, Tumor , Chlorocebus aethiops , Coronavirus Infections/virology , HEK293 Cells , Humans , Mice , Mice, Inbred BALB C , Middle East Respiratory Syndrome Coronavirus/chemistry , Peptidyl-Dipeptidase A/genetics , Peptidyl-Dipeptidase A/metabolism , Pneumonia, Viral/virology , Protein Binding , Protein Interaction Domains and Motifs/immunology , Receptors, Virus/metabolism , SARS Virus/chemistry , SARS-CoV-2 , Sf9 Cells , Specific Pathogen-Free Organisms , Spodoptera , Transfection , Vaccination/methods , Vero Cells , Viral Vaccines
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