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2.
IEEE Rev Biomed Eng ; 14: 16-29, 2021.
Article in English | MEDLINE | ID: covidwho-1501334

ABSTRACT

Coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is spreading rapidly around the world, resulting in a massive death toll. Lung infection or pneumonia is the common complication of COVID-19, and imaging techniques, especially computed tomography (CT), have played an important role in diagnosis and treatment assessment of the disease. Herein, we review the imaging characteristics and computing models that have been applied for the management of COVID-19. CT, positron emission tomography - CT (PET/CT), lung ultrasound, and magnetic resonance imaging (MRI) have been used for detection, treatment, and follow-up. The quantitative analysis of imaging data using artificial intelligence (AI) is also explored. Our findings indicate that typical imaging characteristics and their changes can play crucial roles in the detection and management of COVID-19. In addition, AI or other quantitative image analysis methods are urgently needed to maximize the value of imaging in the management of COVID-19.


Subject(s)
COVID-19/diagnosis , Artificial Intelligence , COVID-19/diagnostic imaging , Humans , Lung/diagnostic imaging , Lung/virology , Positron Emission Tomography Computed Tomography/methods , SARS-CoV-2/pathogenicity , Tomography, X-Ray Computed/methods , Ultrasonography/methods
3.
J Infect Dis ; 224(4): 736, 2021 08 16.
Article in English | MEDLINE | ID: covidwho-1467349
4.
J Infect Dis ; 2021 Apr 05.
Article in English | MEDLINE | ID: covidwho-1467346
5.
Int J Nurs Stud Adv ; 3: 100026, 2021 Nov.
Article in English | MEDLINE | ID: covidwho-1454193

ABSTRACT

Background: The Coronavirus disease (COVID-19) pandemic is an ongoing pandemic all over the world, leading to 126, 372, 442 people diagnosed and 2, 769, 696 deaths globally as of March 28, 2021. Nurses are providing care to patients with COVID-19 who require hospitalization. To ensure adequate response capacity and to maintain the health of nurses, it is important to analyse the actual work hours and the nurses reported preferred work hours per shift among frontline nurses. Objective: To analyse the actual work hours and preferred work hours per shift of nurses reports among frontline nurses fighting the COVID-19 epidemic and to explore the influencing factors on the nurses reported preferred work hours. Design: Cross-sectional survey. Settings: This study was conducted in 10 designated hospitals providing treatments to patients with COVID-19 in China. Participants: Nurses providing care to patients with COVID-19 in designated hospitals in China. Methods: A questionnaire with open-ended questions was used to assess frontline nurses caring for COVID-19 cases in 10 designated hospitals. Quantitative and qualitative methods were used to analyse the actual work hours, the nurses reported preferred work hours and factors influencing nurses reported preferred work hours among the frontline nurses. Results: A total of 109 nurses responded to the survey. The shift length exceeded the nurses' preferred work hours [Median (interquartile range): 5.00 (2.00) h vs 4.00 (2.00) h; Minimum-Maximum: 4-12 h vs 4-8 h], and 60.55% (66/109) of the nurses regarded 4 h as the preferred number of work hours per shift. Five key themes associated with the influencing factors emerged, including circumstances; personal preventable equipment; the nurses' physical and emotional needs of nurse; and the nurses' safety needs and work intensity. Conclusions: These findings suggest that there is a gap between the actual work hours and the nurses preferred work hours among frontline nurses in different units and different posts. The main influencing factors were circumstances, personal protective equipment, the nurses' physical and emotional needs, and the nurses' safety needs and work intensity.

7.
BMC Infect Dis ; 21(1): 737, 2021 Aug 03.
Article in English | MEDLINE | ID: covidwho-1435227

ABSTRACT

BACKGROUND: The serum surfactant protein D (SP-D) level is suggested to be a useful biomarker for acute lung injuries and acute respiratory distress syndrome. Whether the serum SP-D level could identify the severity of coronavirus disease 2019 (COVID-19) in the early stage has not been elucidated. METHODS: We performed an observational study on 39 laboratory-confirmed COVID-19 patients from The Fourth People's Hospital of Yiyang, Hunan, China. Receiver operating characteristic (ROC) curve analysis, correlation analysis, and multivariate logistic regression model analysis were performed. RESULTS: In the acute phase, the serum levels of SP-D were elevated significantly in severe COVID-19 patients than in mild cases (mean value ± standard deviation (SD), 449.7 ± 125.8 vs 245.9 ± 90.0 ng/mL, P<0.001), while the serum levels of SP-D in the recovery period were decreased dramatically than that in the acute phase (mean value ± SD, 129.5 ± 51.7 vs 292.9 ± 130.7 ng/ml, P<0.001), and so were for the stratified patients. The chest CT imaging scores were considerably higher in the severe group compared with those in the mild group (median value, 10.0 vs 9.0, P = 0.011), while markedly lower in the recovery period than those in the acute phase (median value, 2.0 vs 9.0, P<0.001), and so were for the stratified patients. ROC curve analysis revealed that areas under the curve of lymphocyte counts (LYM), C-reaction protein (CRP), erythrocyte sedimentation rate (ESR), interleukin-6 (IL-6), and SP-D for severe COVID-19 were 0.719, 0.833, 0.817, 0.837, and 0.922, respectively. Correlation analysis showed that the SP-D levels were negatively correlated with LYM (r = - 0.320, P = 0.047), while positively correlated with CRP (r = 0.658, P<0.001), IL-6 (r = 0.471, P = 0.002), the duration of nucleic acid of throat swab turning negative (r = 0.668, P<0.001), chest CT imaging score on admission (r = 0.695, P<0.001) and length of stay (r = 0.420, P = 0.008). Multivariate logistic regression model analysis showed that age (P = 0.041, OR = 1.093) and SP-D (P = 0.008, OR = 1.018) were risk factors for severe COVID-19. CONCLUSIONS: Elevated serum SP-D level was a potential biomarker for the severity of COVID-19; this may be useful in identifying patients whose condition worsens at an early stage.


Subject(s)
COVID-19 , Pulmonary Surfactant-Associated Protein D , Humans , Prognosis , ROC Curve , Retrospective Studies , SARS-CoV-2 , Severity of Illness Index
9.
Vaccines (Basel) ; 9(9)2021 Sep 16.
Article in English | MEDLINE | ID: covidwho-1411058

ABSTRACT

Clarifying changes in the immune microenvironment caused by vaccination is crucial for the development and application of vaccines. In this study, we analyzed seroconversion of antibodies, 12 key cytokines, and 34 lymphocyte subsets at three time points (D-1, D14, and D42) around vaccination and differences between two inactivated vaccines (Sinopharm and Sinovas) to understand the immune response induced by inactivated vaccines in the real world. The results showed that 62.5% and 75% of the participants achieved neutralizing antibody seroconversion on D14 and D42, respectively. After vaccination, IL-5 and IL-6 increased, and INF-γ decreased. IL6, IL-1B, INF-γ, IL-8, and IL-12p70 showed statistical significance in the comparison of different groups. In terms of lymphocyte subsets, CD3 +, CD56 +, CD3 + CD8 +, CD8 + CD71 +, and CD56 + CD71 + showed upward trend, while CD19 +, CD4 + CD8 +, CD8 + CD45RA +, CD4 + HLA-DR +, CD8 + HLA-DR +, and CD8 + CD38 + showed downward trend. Additionally, we found certain differences between the two vaccines in neutralizing antibodies, cytokines, and lymphocyte subsets. This research is a clinical observation on the immune response after vaccination through detecting various immune indicators, which showed that the inactivated vaccines induced both humoral immunity by producing neutralizing antibodies and cellular immunity. The cellular immunity induced by these two vaccines was a Th2-biased response, and it may also lead to a mild Th1-type response.

10.
J Formos Med Assoc ; 2021 Jul 09.
Article in English | MEDLINE | ID: covidwho-1313232
11.
Sci Rep ; 11(1): 13971, 2021 07 07.
Article in English | MEDLINE | ID: covidwho-1301179

ABSTRACT

To unravel the source of SARS-CoV-2 introduction and the pattern of its spreading and evolution in the United Arab Emirates, we conducted meta-transcriptome sequencing of 1067 nasopharyngeal swab samples collected between May 9th and Jun 29th, 2020 during the first peak of the local COVID-19 epidemic. We identified global clade distribution and eleven novel genetic variants that were almost absent in the rest of the world and that defined five subclades specific to the UAE viral population. Cross-settlement human-to-human transmission was related to the local business activity. Perhaps surprisingly, at least 5% of the population were co-infected by SARS-CoV-2 of multiple clades within the same host. We also discovered an enrichment of cytosine-to-uracil mutation among the viral population collected from the nasopharynx, that is different from the adenosine-to-inosine change previously reported in the bronchoalveolar lavage fluid samples and a previously unidentified upregulation of APOBEC4 expression in nasopharynx among infected patients, indicating the innate immune host response mediated by ADAR and APOBEC gene families could be tissue-specific. The genomic epidemiological and molecular biological knowledge reported here provides new insights for the SARS-CoV-2 evolution and transmission and points out future direction on host-pathogen interaction investigation.


Subject(s)
COVID-19/epidemiology , COVID-19/immunology , Coinfection/epidemiology , Genomics , Immunity, Innate , Mutation , SARS-CoV-2/genetics , Adult , COVID-19/transmission , Cytidine Deaminase/genetics , Female , Gene Expression Profiling , Genome, Viral/genetics , Humans , Male , Middle Aged , Nasopharynx/virology , Organ Specificity , SARS-CoV-2/immunology
12.
Int J Clin Pract ; 75(7): e14026, 2021 Jul.
Article in English | MEDLINE | ID: covidwho-1299143
13.
Front Genet ; 12: 663098, 2021.
Article in English | MEDLINE | ID: covidwho-1268247

ABSTRACT

Symptoms of coronavirus disease 2019 (COVID-19) range from asymptomatic to severe pneumonia and death. A deep understanding of the variation of biological characteristics in severe COVID-19 patients is crucial for the detection of individuals at high risk of critical condition for the clinical management of the disease. Herein, by profiling the gene expression spectrum deduced from DNA coverage in regions surrounding transcriptional start site in plasma cell-free DNA (cfDNA) of COVID-19 patients, we deciphered the altered biological processes in the severe cases and demonstrated the feasibility of cfDNA in measuring the COVID-19 progression. The up- and downregulated genes in the plasma of severe patient were found to be closely related to the biological processes and functions affected by COVID-19 progression. More importantly, with the analysis of transcriptome data of blood cells and lung cells from control group and cases with severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) infection, we revealed that the upregulated genes were predominantly involved in the viral and antiviral activity in blood cells, reflecting the intense viral replication and the active reaction of immune system in the severe patients. Pathway analysis of downregulated genes in plasma DNA and lung cells also demonstrated the diminished adenosine triphosphate synthesis function in lung cells, which was evidenced to correlate with the severe COVID-19 symptoms, such as a cytokine storm and acute respiratory distress. Overall, this study revealed tissue involvement, provided insights into the mechanism of COVID-19 progression, and highlighted the utility of cfDNA as a noninvasive biomarker for disease severity inspections.

15.
J Infect Dis ; 223(8): 1499-1500, 2021 Apr 23.
Article in English | MEDLINE | ID: covidwho-1244883
16.
Int J Infect Dis ; 2021 May 10.
Article in English | MEDLINE | ID: covidwho-1225254

ABSTRACT

BACKGROUND: COVID-19 has spread worldwide and become a pandemic. We report the epidemiological and clinical characteristics of cluster infections. METHODS: Data of clustered cases were retrieved from the public health emergency monitoring information system of China. We analyzed the incubation period, generation gap, secondary attack rate, and viral load in various grouped cases. RESULTS: A total of 60 COVID-19 infection clusters including 226 patients and 19 asymptomatic cases involving four generations were analyzed. With the increase of transmission generations, secondary attack rate decreased (P<0.001) and severity alleviated (P = 0.008). The median incubation period and intergenerational interval were 9 and 6 days, respectively. The secondary attack rate was 7.1% in the index cases, 5.0% in the first generation, 1.0% in the second generation, and 4.7% overall. Severe cases were seen more in the index (13, 65%) and first generation (7, 35%) ones, who had a significantly higher viral load than the mild and moderate ones. CONCLUSIONS: With the increase of transmission generation, secondary infection rate and severity decreased. Severe patients had a higher virus load. Patients in the incubation period and asymptomatic carriers were potential infection sources who might play an important role in transmission.

18.
SN Comput Sci ; 2(3): 201, 2021.
Article in English | MEDLINE | ID: covidwho-1174059

ABSTRACT

The outbreak of the novel Coronavirus Disease 2019 (COVID-19) has caused unprecedented impacts to people's daily life around the world. Various measures and policies such as lockdown and social-distancing are implemented by governments to combat the disease during the pandemic period. These measures and policies as well as virus itself may cause different mental health issues to people such as depression, anxiety, sadness, etc. In this paper, we exploit the massive text data posted by Twitter users to analyse the sentiment dynamics of people living in the state of New South Wales (NSW) in Australia during the pandemic period. Different from the existing work that mostly focuses on the country-level and static sentiment analysis, we analyse the sentiment dynamics at the fine-grained local government areas (LGAs). Based on the analysis of around 94 million tweets that posted by around 183 thousand users located at different LGAs in NSW in 5 months, we found that people in NSW showed an overall positive sentimental polarity and the COVID-19 pandemic decreased the overall positive sentimental polarity during the pandemic period. The fine-grained analysis of sentiment in LGAs found that despite the dominant positive sentiment most of days during the study period, some LGAs experienced significant sentiment changes from positive to negative. This study also analysed the sentimental dynamics delivered by the hot topics in Twitter such as government policies (e.g. the Australia's JobKeeper program, lockdown, social-distancing) as well as the focused social events (e.g. the Ruby Princess Cruise). The results showed that the policies and events did affect people's overall sentiment, and they affected people's overall sentiment differently at different stages.

19.
Information ; 12(3):109, 2021.
Article in English | MDPI | ID: covidwho-1125736

ABSTRACT

The novel coronavirus disease, also known as COVID-19, is a disease outbreak that was first identified in Wuhan, a Central Chinese city. In this report, a short analysis focusing on Australia, Italy, and the United Kingdom is conducted. The analysis includes confirmed and recovered cases and deaths, the growth rate in Australia compared with that in Italy and the United Kingdom, and the trend of the disease in different Australian regions. Mathematical approaches based on susceptible, infected, and recovered (SIR) cases and susceptible, exposed, infected, quarantined, and recovered (SEIQR) cases models are proposed to predict epidemiology in the above-mentioned countries. Since the performance of the classic forms of SIR and SEIQR depends on parameter settings, some optimization algorithms, namely Broyden–Fletcher–Goldfarb–Shanno (BFGS), conjugate gradients (CG), limited memory bound constrained BFGS ( L-BFGS-B), and Nelder–Mead, are proposed to optimize the parameters and the predictive capabilities of the SIR and SEIQR models. The results of the optimized SIR and SEIQR models were compared with those of two well-known machine learning algorithms, i.e., the Prophet algorithm and logistic function. The results demonstrate the different behaviors of these algorithms in different countries as well as the better performance of the improved SIR and SEIQR models. Moreover, the Prophet algorithm was found to provide better prediction performance than the logistic function, as well as better prediction performance for Italy and UK cases than for Australian cases. Therefore, it seems that the Prophet algorithm is suitable for data with an increasing trend in the context of a pandemic. Optimization of SIR and SEIQR model parameters yielded a significant improvement in the prediction accuracy of the models. Despite the availability of several algorithms for trend predictions in this pandemic, there is no single algorithm that would be optimal for all cases.

20.
Ren Fail ; 42(1): 950-957, 2020 Nov.
Article in English | MEDLINE | ID: covidwho-1124758

ABSTRACT

BACKGROUND: Novel coronavirus disease (COVID-19) is spreading rapidly, which poses great challenges to patients on maintenance hemodialysis. Here we report the clinical features of 66 hemodialysis patients with laboratory confirmed COVID-19 infection. DESIGN, SETTING, PARTICIPANTS, AND MEASUREMENTS: Retrospective, single-center case series of the 66 hemodialysis patients with confirmed COVID-19 from 1 January to 5 March 2020; the final date of follow-up was 25 March 2020. RESULTS: The clinical data were collected from 66 hemodialysis patients with confirmed COVID-19. The incidence of COVID-19 in our center was 11.0% (66/602), of which 18 patients died. According to different prognosis, hemodialysis patients with COVID-19 were divided into the survival and death group. A higher incidence of fever and dyspnea was found in the death group compared with the survival group. Meanwhile, patients in the death group were often accompanied by higher white blood cell count, prolonged PT time, increased D-dimer (p < .05). More patients in the death group showed hepatocytes and cardiomyocytes damage. Furthermore, logistic regression analysis suggested that fever, dyspnea, and elevated D-dimer were independent risk factors for death in hemodialysis patients with COVID-19 (OR, 1.077; 95% CI, 1.014 to 1.439; p = .044; OR, 1.146; 95% CI, 1.026 to 1.875; p = .034, OR, 4.974; 95% CI, 3.315 to 6.263; p = .007, respectively). CONCLUSIONS: The potential risk factors of fever, dyspnea, and elevated D-dimer could help clinicians to identify hemodialysis patients with poor prognosis at an early stage of COVID-19 infection.


Subject(s)
Coronavirus Infections , Dyspnea , Fever , Fibrin Fibrinogen Degradation Products/analysis , Kidney Failure, Chronic , Pandemics , Pneumonia, Viral , Risk Assessment/methods , Betacoronavirus/isolation & purification , COVID-19 , China/epidemiology , Comorbidity , Coronavirus Infections/diagnosis , Coronavirus Infections/mortality , Coronavirus Infections/physiopathology , Dyspnea/diagnosis , Dyspnea/epidemiology , Female , Fever/diagnosis , Fever/epidemiology , Hemodialysis Units, Hospital/statistics & numerical data , Humans , Kidney Failure, Chronic/epidemiology , Kidney Failure, Chronic/therapy , Male , Middle Aged , Mortality , Pneumonia, Viral/diagnosis , Pneumonia, Viral/mortality , Pneumonia, Viral/physiopathology , Prognosis , Renal Dialysis/methods , Retrospective Studies , Risk Factors , SARS-CoV-2
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