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1.
CCS Chemistry ; 4(1):112-121, 2022.
Article in English | Scopus | ID: covidwho-1644130

ABSTRACT

Currently, there is no effective antiviral medication for coronavirus disease 2019 (COVID-19) and the knowledge on the potential therapeutic target is in great need. Guided by a time-course transmission electron microscope (TEM) imaging, we analyzed early phosphorylation dynamics within the first 15 min during severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral entry. Based on alterations in the phosphorylation events, we found that kinase activities such as protein kinase C (PKC), interleukin-1 receptor-associated kinase 4 (IRAK4), MAP/microtubule affinity-regulating kinase 3 (MARK3), and TANK-binding kinase 1 (TBK1) were affected within 15 min of infection. Application of the corresponding kinase inhibitors of PKC, IRAK4, and p38 showed significant inhibition of SARS-CoV-2 replication. Additionally, proinflammatory cytokine production was reduced by applying PKC and p38 inhibitors. By an acquisition of a combined image data using positiveand negative-sense RNA probes, as well as pseudovirus entry assay, we demonstrated that PKC contributed to viral entry into the host cell, and therefore, could be a potential COVID-19 therapeutic target. © 2022 Chinese Chemical Society. All right reserved.

2.
TMR Integrative Medicine ; 5, 2021.
Article in English | EMBASE | ID: covidwho-1573209

ABSTRACT

Patients with novel coronavirus disease-19 (COVID-19) pneumonia continue to have problems with respiratory function, physical and psychological function, the ability to perform activities of daily living, and social participation after discharge from the hospital. As such, strengthening rehabilitation treatments for discharged patients and relapse prevention after recovery are important aspects of the prevention and control of COVID-19. This paper combined the principles and practices of in Chinese and Western medicine and compiled the recommendations of both for home rehabilitation in the post-COVID-19 epidemic stage. The purpose of this paper is to facilitate the self-rehabilitation of patients with COVID-19 and to promote the prevention and control of COVID-19 at this current stage.

3.
Chinese Journal of Disease Control and Prevention ; 25(7):802-805, 2021.
Article in Chinese | Scopus | ID: covidwho-1566866

ABSTRACT

Objective To study the safety of inactivated SARS-CoV-2 vaccine (Vero cells) (SARS-CoV-2 vaccine) in Hefei during the emergency vaccination period, and to provide reference for the promotion and vaccination of this vaccine in the whole population in the later period. Methods A total of 19 vaccination clinics in Hefei were selected as active monitoring sites for immunization safety. Focus groups aged 18-59 years who were vaccinated with SARS-CoV-2 vaccine in Hefei from 15 December 2020 to 10 February 2021 were observed. The incidence, types and severity of adverse reactions after vaccination were descriptively analyzed. Results A total of 18 574 people were effectively observed, and 33 433 doses of SARS-CoV-2 vaccine were inoculated. 713 cases of general adverse reactions occurred, with an incidence of 2.13%. The incidence of adverse reactions in the first dose was 2.57%, which was higher than that in the second dose (1.58%, χ2=38.92, P < 0.001). There was no statistical significance in the incidence of general adverse reactions of inactivated vaccine produced by the two companies (χ2=3.08, P=0.082). Among the adverse events such as redness and swelling at the injection site, induration at the injection site, and fever, the incidence of grade 1, 2 and 3 adverse events were 0.65%, 1.42%, and 0.06%, but there were no ≥ grade 4, rare and extremely rare adverse events. Conclusion Domestic inactivated SARS-CoV-2 vaccine (Vero cells) is great safe. © 2021, Publication Centre of Anhui Medical University. All rights reserved.

4.
Chinese Pharmaceutical Journal ; 56(20):1690-1693, 2021.
Article in Chinese | Scopus | ID: covidwho-1566820

ABSTRACT

OBJECTIVE: To study the dose-based BCS (biopharmaceutical classification system) classification of dexamethasone for different indications. METHODS: Saturation shake-flask method was utilized with the conditions of shaking water bath at 37℃ and 120 r•min-1, somewhat excess solids added into saturate systems at pH 1.2, 4.5 and 6.8 buffers respectively. And high-performance liquid chromatography was used for saturation concentration measurement. In this study, the dosages of dexamethasone for both classic and new indications were collected, such as covid-19, which were divided into low-dose, intermediate-dose and high-dose. Then the dissolution volumes (DSVs) were calculated and the indication-based BCS classifications of dexamethasone was studied. RESULTS: At the low-dose, the BCS classification of dexamethasone was high solubility;at the high-dose, the BCS classification of dexamethasone was low;and at the intermediate-dose, the BCS classification of dexamethasone was on the edge of low solubility and high solubility. CONCLUSION: This study provides basic data for the BCS classification of dexamethasone;dose-related solubility classification has guiding significance for the BCS classification of dexamethasone for new indications, and provides refine reference for the rationality of the BE wavier for solid oral dosage forms in the consistency evaluation of generic drugs. Copyright 2021 by the Chinese Pharmaceutical Association.

5.
Aerosol and Air Quality Research ; 21(11), 2021.
Article in English | Scopus | ID: covidwho-1551719

ABSTRACT

We studied the impact of COVID-19 (coronavirus disease 2019) lockdown on the air quality over the Atlanta area using satellite and ground-based observations, meteorological reanalysis data and traffic information. Unlike other cities, we found the air quality has improved slightly over the Atlanta area during the 2020 COVID-19 lockdown period (March 14–April 30, 2020), compared to the analogous period of 2019 (March 14–April 30, 2019). Ground NO2 concentrations have decreased slightly 10.8% and 8.2% over the near-road (NR) and urban ambient (UA) stations, respectively. Tropospheric NO2 columns have reduced 13%–49% over the Atlanta area from space-borne observations of TROPOspheric Monitoring Instrument (TROPOMI). Ground ozone and PM2.5 have decreased 15.7% and ~5%, respectively. This slight air quality improvement is primarily caused by the reduced human activities, as COVID-19 lockdowns have reduced ~50% human activities, measured by traffic volume. Higher wind speed and precipitations also make the meteorological conditions favorable to this slight air quality improvement. We have not found a significant improvement in air quality over Atlanta amid the lockdown when human activities have reduced ~50%. Further studies are needed to understand the impacts of reduced human activities on atmospheric chemistry. We also found TROPOMI and ground measurements have disagreements on NO2 reductions, as collocated TROPOMI observations revealed ~23% and ~21% reductions of tropospheric NO2 columns over NR and UA stations, respectively. Several factors may explain this disagreement: First, tropospheric NO2 columns and ground NO2 concentrations are not necessarily the same, although they are highly correlated in the afternoon;Second, meteorological conditions may have different impacts on TROPMI and ground measurements. Third, TROPOMI may underestimate tropospheric NO2 due to uncertainties from air mass factors. Fourth, the uncertainties of chemiluminescence NO2 measurements used by ground stations. Consequently, studies using space-borne tropospheric NO2 column and ground NO2 measurements should take these factors into account. © The Author(s).

6.
Iet Communications ; : 13, 2021.
Article in English | Web of Science | ID: covidwho-1537348

ABSTRACT

With the 5G worldwide deployment, the scale of vertical applications is innovated benefit from 5G technologies including MEC (Multi-access Edge Computing), network slicing, etc. Especially for healthcare, 5G had been used for COVID-19 protection and intelligent medical processing. However, limited by the hospital's traditional information infrastructures, those 5G-based healthcare applications are hard to be deployed and most only for demonstration, also isolated from the existing medical systems. So what is the next generation of smart healthcare information infrastructures is the key issue for the long-term development of 5G healthcare applications. Even though the standardized 5G MEC framework has been widely used in many vertical scenarios, it is also hard to satisfy hospital-specific requirements such as hospital-dedicated deployment, medical data security, and various network connections, etc. This paper proposes a 5G-based architecture for smart healthcare information infrastructure, a new network element iGW (industry gateway) is defined, and the smart healthcare dedicated cloud platform iMEP (industry multi-access edge platform) is also introduced here, making it possible to satisfy both the hospital-specific requirements and the long-term evolution. Meanwhile, the implementation methodology and the corresponding field test results are presented, which show the significant network performance gain achieved by the proposed new system structure.

7.
29th ACM International Conference on Multimedia, MM 2021 ; : 3024-3033, 2021.
Article in English | Scopus | ID: covidwho-1533095

ABSTRACT

Deep learning has made a tremendous impact on various applications in multimedia, such as media interpretation and multimodal retrieval. However, deep learning models usually require a large amount of labeled data to achieve satisfactory performance. In multimedia analysis, domain adaptation studies the problem of cross-domain knowledge transfer from a label rich source domain to a label scarce target domain, thus potentially alleviates the annotation requirement for deep learning models. However, we find that contemporary domain adaptation methods for cross-domain image understanding perform poorly when source domain is noisy. Weakly Supervised Domain Adaptation (WSDA) studies the domain adaptation problem under the scenario where source data can be noisy. Prior methods on WSDA remove noisy source data and align the marginal distribution across domains without considering the fine-grained semantic structure in the embedding space, which have the problem of class misalignment, e.g., features of cats in the target domain might be mapped near features of dogs in the source domain. In this paper, we propose a novel method, termed Noise Tolerant Domain Adaptation (NTDA), for WSDA. Specifically, we adopt the cluster assumption and learn cluster discriminatively with class prototypes (centroids) in the embedding space. We propose to leverage the location information of the data points in the embedding space and model the location information with a Gaussian mixture model to identify noisy source data. We then design a network which incorporates the Gaussian mixture noise model as a sub-module for unsupervised noise removal and propose a novel cluster-level adversarial adaptation method based on the Generative Adversarial Network (GAN) framework which aligns unlabeled target data with the less noisy class prototypes for mapping the semantic structure across domains. Finally, we devise a simple and effective algorithm to train the network from end to end. We conduct extensive experiments to evaluate the effectiveness of our method on both general images and medical images from COVID-19 and e-commerce datasets. The results show that our method significantly outperforms state-of-the-art WSDA methods. © 2021 Owner/Author.

8.
30th ACM International Conference on Information and Knowledge Management, CIKM 2021 ; : 4273-4282, 2021.
Article in English | Scopus | ID: covidwho-1528565

ABSTRACT

The ability to infer an individual's expertise for a given skill has proven to be crucial in creating economic opportunity for every talent of the global workforce. Applications ranging from recommending relevant job opportunities to talents to providing better candidate suggestions to recruiters, all benefit from deep understanding of the skill "proficiency"of the talent pool. LinkedIn's "Skill"profile section can be leveraged in this expert finding task. Whereas it is easy to incentivize members to put skills on their profile, estimating members' expertise is much more challenging for several reasons. First, the collection of ground-truth data at scale can be expensive and challenging. Second, "being proficient at a certain skill"can have very different meaning in different contexts - a professor in machine learning having deep theoretical knowledge might lack the practical skill for implementing a large-scale recommendation system unlike experienced ML practitioners. We present our proposed framework to infer a member's expertise in a certain skill based upon a multi-view, multi-task learning scheme that incorporates signals from multiple contexts. We show the efficacy of the proposed framework with offline evaluation results as well as online A/B testing in multiple products, from finding experts among friends, to recommending jobs to qualified members. We also show that our estimated proficiency can help alleviate the cold-start problem when applied to a new context (i.e., through transfer learning) where only a small amount of labeled data is needed to achieve reasonable performance. Finally, we share the insights that demonstrate the talent market is shocked disproportionately among members with different skill proficiency levels by COVID-19. © 2021 ACM.

9.
IEEE Sensors Journal ; 2020.
Article in English | Scopus | ID: covidwho-1515164

ABSTRACT

(Aim) COVID-19 pandemic causes numerous death tolls till now. Chest CT is an effective imaging sensor system to make accurate diagnosis. (Method) This paper proposed a novel seven layer convolutional neural network based smart diagnosis model for COVID-19 diagnosis (7L-CNN-CD). We proposed a 14-way data augmentation to enhance the training set, and introduced stochastic pooling to replace traditional pooling methods. (Results) The 10 runs of 10-fold cross validation experiment show that our 7L-CNN-CD approach achieves a sensitivity of 94.44±0.73, a specificity of 93.63±1.60, and an accuracy of 94.03±0.80. (Conclusion) Our proposed 7L-CNN-CD is effective in diagnosing COVID-19 in chest CT images. It gives better performance than several state-of-the-art algorithms. The data augmentation and stochastic pooling methods are proven to be effective. IEEE

10.
Fudan University Journal of Medical Sciences ; 48(5):603-610, 2021.
Article in Chinese | Scopus | ID: covidwho-1471036

ABSTRACT

Objective: To evaluate the impact of COVID-19 epidemic on anxiety status of active psychological counselors in Shanghai, China. Methods: We collected the demographic information of participants, cognition and attention to COVID-19, attitude towards the disappearance of COVID-19 and answers to the State-Trait Anxiety Inventory (STAI) on the Shanghai online psychological counseling platform from Feb 4th, 2020 to Mar 11st, 2020.Multiple Logistic regression was used to analyze the associations between COVID-19 epidemic and State-anxiety and Trait-anxiety. Results: Of 704 participants with an average age of 33.24 years (ranging from 18 to 73 years), the mean State-scores and Trait-scores were 44.49±6.31 and 46.19±5.22, respectively.In multivariate Logistic regression analysis, after gender stratification and adjustment of related variables, we found that for males, engaging in relevant prevention and control work was associated with a lower risk for medium or high State-anxiety (OR=0.28, 95%CI:0.09-0.89), and holding a intensively positive attitude was associated with a lower risk for medium or high Trait-anxiety (OR=0.25, 95%CI:0.07-0.87);while for females, those with medical background had a lower risk for State-anxiety (OR=0.17, 95%CI:0.03-0.92), and those paying moderate attention to epidemic had a lower risk for Trait-anxiety (OR=0.22, 95%CI:0.07-0.69). Conclusion: COVID-19 epidemic had different effects on the anxiety status of psychological counselors with different characteristics.Psychological counselors who have poor cognition of the epidemic, excessive attention to the epidemic, low hope for the disappearance of the epidemic, and non-disease prevention and control profession are more susceptible to greater anxiety, which are the key objects of protection in the event of public health emergencies. © 2021, Editorial Department of Fudan University Journal of Medical Sciences. All right reserved.

11.
Journal of Chinese Mass Spectrometry Society ; 42(5):563-584, 2021.
Article in Chinese | Scopus | ID: covidwho-1449174

ABSTRACT

COVID-19 has caused a huge health crisis and incalculable damage worldwide. Emerging immune escaping mutants of the virus suggests that SARS-CoV-2 may be persistent in human society like the flu virus and become a long-lasting health threat. The control of SARS-CoV-2 transmission and the development of an effective treatment are imminent. Therefore, it is imperative to find appropriate biomarkers to indicate pathological and physiological. Proteins are performers of life functions and their abundance and modification status can directly reflect the immune status. Post-translational modifications such as glycosylation and phosphorylation have a great impact on the regulation of protein functions. In the studies of SARS, Zika, and H1N1, post-translational modified proteins have shown to be reliable biomarkers. In recent years, mass spectrometry-based proteomics has made great progress due to the development of mass spectrometry technology. A review of research strategies for mass spectrometry-based biomarkers, especially in the application of protein post-translational modifications, is important for the victory of human beings fighting the Covid-19 epidemic. This review summarized the current progress of mass spectrometry-based studies on the PTM status of SARS-CoV-2 viral proteins, particularly in glycosylation and phosphorylation aspect. The challenge and prospect of the application of mass spectrometry in this particular research area were outlined. © 2021, Editorial Board of Journal of Chinese Mass Spectrometry Society. All right reserved.

12.
10th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2021 ; : 953-958, 2021.
Article in English | Scopus | ID: covidwho-1402782

ABSTRACT

In this paper, an innovative SEIR(Susceptible-Exposed-Infective-Recovered) model is proposed to estimate the true infectivity and lethality of the COVID-19 epidemic in Wuhan, China. Segmented parameters are used in the model to prove the effectiveness of improved public health interventions such as city lockdown and extreme social distancing.And the generally polynomial chaos method is used to increase the reliability of the model results in the case of parameter estimation. The accuracy and validity of the proposed SEIR model are proved according to the official reported data.Also, according to the epidemic trend reflected by the model, the effectiveness and timeliness of the epidemic prevention policies formulated by the government can be reflected. © 2021 IEEE.

13.
Pediatric Medicine ; 4, 2021.
Article in English | Scopus | ID: covidwho-1399731

ABSTRACT

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become a global pandemic with major concerns on finding effective ways for clinical management. In this review, we searched available publications on the pharmacological treatment options for children with COVID-19. In total, 104 case reports and 15 cohort studies were included for analysis. For children, commonly applied medications were categorized into five main types: antivirals, antibacterials and antifungals, anti-inflammatories, anticoagulants, and vasopressors. Inhaled interferon was the most used antiviral in cohort studies, while hydroxychloroquine (HCQ) or chloroquine (CQ) was the most in case reports. Different from adult patients, special consideration should be given to COVID-19 children diagnosed with multisystem inflammatory syndrome (MIS-C). Besides direct antiviral treatment, pharmacological care managing the inflammatory process comprises a great part of the treatment protocol. In addition to commonly used glucocorticoids, intravenous immunoglobulin (IVIG), and aspirin, some biologics could be considered as potential treatment. Anakinra, an interleukin-1 (IL-1) receptor antagonist, is highly recommended by the American College of Rheumatology as a safe treatment for children with MIS-C. The IL-6 receptor antagonist, tocilizumab, is also a potential treatment option. This review offers a comprehensive overview of the common medications used in clinical settings all over the world, but should be referred to with caution and flexibility depending on the actual condition of a specific patient. © Pediatric Medicine. All rights reserved.

14.
Industrial Crops and Products ; 170:9, 2021.
Article in English | Web of Science | ID: covidwho-1397391

ABSTRACT

Biomass fast pyrolysis was performed in a bubbling fluidized bed reactor that incorporated two crucial innovations. A fractional condensation train provided dry bio-oils with only 1% of moisture and much reduced acidity. Autothermal pyrolysis with partial oxidation enhanced dry bio-oil quality while reducing the capital and operating costs by eliminating the need for external heating. Both endothermic pyrolysis with pure N2 and autothermal pyrolysis with oxidant fluidization gases were carried out at 500 degrees C. Dry bio-oils of birch wood, birch bark or hydrolysis lignin, from both endothermic and autothermal pyrolysis, could substitute up to 65 % of phenol in reacting with formaldehyde to produce wood adhesives that met the international standard specifications on mechanical strength and formaldehyde emissions. Therefore, autothermal pyrolysis did not harm the bio-oil quality for phenol substitution. Dry bio-oil from autothermal pyrolysis of kraft lignin was able to replace 80 % of the phenol.

15.
35th AAAI Conference on Artificial Intelligence / 33rd Conference on Innovative Applications of Artificial Intelligence / 11th Symposium on Educational Advances in Artificial Intelligence ; 35:4804-4811, 2021.
Article in English | Web of Science | ID: covidwho-1381651

ABSTRACT

Coronavirus Disease 2019 (COVID-19) causes a sudden turnover to bad at some checkpoints and thus needs the intervention of intensive care unit (ICU). This resulted in urgent and large needs of ICUs posed great risks to the medical system. Estimating the mortality of critical in-patients who were not admitted into the ICU will be valuable to optimize the management and assignment of ICU. Retrospective, 733 in-patients diagnosed with COVID-19 at a local hospital (Wuhan, China), as of March 18, 2020. Demographic, clinical and laboratory results were collected and analyzed using machine learning to build a predictive model. Considering the shortage of ICU beds at the beginning of disease emergence, we defined the mortality for those patients who were predicted to be in needing ICU care yet they did not as Missing-ICU (MI)-mortality. To estimate MI-mortality, a prognostic classification model was built to identify the in-patients who may need ICU care. Its predictive accuracy was 0.8288, with an AUC of 0.9119. On our cohort of 733 patients, 25 in-patients who have been predicted by our model that they should need ICU, yet they did not enter ICU due to lack of shorting ICU wards. Our analysis had shown that the MI-mortality is 41%, yet the mortality of ICU is 32%, implying that enough bed of ICU in treating patients in critical conditions.

18.
2020 Ieee International Conference on Bioinformatics and Biomedicine ; : 555-561, 2020.
Article in English | Web of Science | ID: covidwho-1354409

ABSTRACT

COVID-19 causes burdens to the ICU. Evidence-based planning and optimal allocation of the scarce ICU resources is urgently needed but remains unaddressed. This study aims to identify variables and test the accuracy to predict the need for ICU admission, death despite ICU care, and among survivors, length of ICU stay, before patients were admitted to ICU. Retrospective data from 733 in-patients confirmed with COVD-19 in Wuhan, China, as of March 18, 2020. Demographic, clinical and laboratory were collected and analyzed using machine learning to build the predictive models. The built machine learning model can accurately assess ICU admission, length of ICU stay, and mortality in COVID-19 patients toward optimal allocation of ICU resources. The prediction can be done by using the clinical data collected within 1-15 days before the actual ICU admission. Lymphocyte absolute value involved in all prediction tasks with a higher AUC.

19.
Stroke ; 52(SUPPL 1), 2021.
Article in English | EMBASE | ID: covidwho-1234396

ABSTRACT

Stroke care has been shown to be worse for patients presenting overnight/weekends (off-hours) to centers compare to those presenting during business hours (on-hours).Telemedicine (TM) helps provide safe management of stroke patients. The UT Teleneurology (UTT) hub provides acute neurological coverage by stroke specialists to 18 spoke centers. To our knowledge, the effect of the Covid-19 pandemic on the “weekend effect” has not been studied. The objective is to compare TM consult volumes and time metrics of stroke patients who received tPA via TM off-hours with those on-hours during the pandemic. In a retrospective query of the UTT registry from 3/20 - 6/20, we identified 122 stroke patients who received tPA - 109 were included in our analysis - 2 were excluded after quality check, 11 were excluded as inpatient strokes. We compared baseline characteristics and time metrics between the off-hours (5pm-7:59am, weekends) and on-hours (weekdays 8am-4:59pm) patients (Table 1). We also compared the number of TM consults between the height of the pandemic (3/20 - 6/20) and the previous year (3/19 - 6/19). Of 109 patients, 72 were managed via TM during off-hours, 37 during on-hours. Baseline characteristics were similar between groups. There were no differences in time metrics including door to needle time. Of note, there was no difference in the number of acute TM consults or patients receiving tPA. There were fewer routine TM consults during the pandemic, and a trend toward fewer phone consults. There was no difference in time metrics between the patients treated off-hours vs on-hours in the pandemic period. TM may be advantageous over in-person neurology coverage during crises, and is consistent regardless of the hour/day. Contrary to other studies, the number of acute TM consults and patients receiving tPA did not differ between the study periods. Routine consults decreased during the pandemic - perhaps coinciding with state closure mandates/fewer hospitalized stroke patients. (Figure Presented).

20.
Stroke ; 52(SUPPL 1), 2021.
Article in English | EMBASE | ID: covidwho-1234394

ABSTRACT

Stroke is a devastating disease with high morbidity/mortality. Many studies have shown lower stroke volumes during the Covid 19 pandemic, with possible causes including fear of contracting the virus, limited hospital capacity, etc. Telemedicine (TM) helps provide safe management of stroke patients, and may be advantageous to in-person coverage during crises. The UT Teleneurology (UTT) hub provides acute neurological coverage by stroke specialists to 18 spoke centers. The impact of the pandemic on acute stroke volumes and care is ongoing and its effects should be studied further. The purpose of this study is to compare TM acute stroke volumes and time metrics between the Covid 19 era (March-June 2020) and the previous year (March-June 2019). In a retrospective query of the UTT registry from 3/19 - 6/19 and 3/20 - 6/20, we identified 294 stroke patients who received tPA - 273 were included in our analysis - 4 were excluded after quality check, 17 were excluded as inpatient strokes. We compared baseline and clinical characteristics, volumes, and time metrics between the periods (table 1). Of the 273 patients, 172 received tPA via TM during the 2019 period and 109 received tPA via TM during the 2020 period. Baseline and clinical characteristics were similar between the groups except for race. Of note, there were no differences in acute TM volumes or the number of patients receiving tPA. There was no difference in most metrics, including door to needle time. During the pandemic, camera to needle time was longer (3 minutes), and there was a trend towards longer last well to door time. There were no differences in the volume of acute TM consults, the number of patients receiving tPA, or door to needle time between the pandemic period and the previous year. Camera to needle time was slightly longer during the pandemic, perhaps representing more demands on hospital staff. The trend towards longer last well to door time could be due to public fear of presenting to the hospital during a deadly pandemic. (Figure Presented).

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