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2.
Eur Radiol ; 2022 Feb 19.
Article in English | MEDLINE | ID: covidwho-1707890

ABSTRACT

OBJECTIVES: We aimed to develop deep learning models using longitudinal chest X-rays (CXRs) and clinical data to predict in-hospital mortality of COVID-19 patients in the intensive care unit (ICU). METHODS: Six hundred fifty-four patients (212 deceased, 442 alive, 5645 total CXRs) were identified across two institutions. Imaging and clinical data from one institution were used to train five longitudinal transformer-based networks applying five-fold cross-validation. The models were tested on data from the other institution, and pairwise comparisons were used to determine the best-performing models. RESULTS: A higher proportion of deceased patients had elevated white blood cell count, decreased absolute lymphocyte count, elevated creatine concentration, and incidence of cardiovascular and chronic kidney disease. A model based on pre-ICU CXRs achieved an AUC of 0.632 and an accuracy of 0.593, and a model based on ICU CXRs achieved an AUC of 0.697 and an accuracy of 0.657. A model based on all longitudinal CXRs (both pre-ICU and ICU) achieved an AUC of 0.702 and an accuracy of 0.694. A model based on clinical data alone achieved an AUC of 0.653 and an accuracy of 0.657. The addition of longitudinal imaging to clinical data in a combined model significantly improved performance, reaching an AUC of 0.727 (p = 0.039) and an accuracy of 0.732. CONCLUSIONS: The addition of longitudinal CXRs to clinical data significantly improves mortality prediction with deep learning for COVID-19 patients in the ICU. KEY POINTS: • Deep learning was used to predict mortality in COVID-19 ICU patients. • Serial radiographs and clinical data were used. • The models could inform clinical decision-making and resource allocation.

3.
NPJ Digit Med ; 5(1): 5, 2022 Jan 14.
Article in English | MEDLINE | ID: covidwho-1625359

ABSTRACT

While COVID-19 diagnosis and prognosis artificial intelligence models exist, very few can be implemented for practical use given their high risk of bias. We aimed to develop a diagnosis model that addresses notable shortcomings of prior studies, integrating it into a fully automated triage pipeline that examines chest radiographs for the presence, severity, and progression of COVID-19 pneumonia. Scans were collected using the DICOM Image Analysis and Archive, a system that communicates with a hospital's image repository. The authors collected over 6,500 non-public chest X-rays comprising diverse COVID-19 severities, along with radiology reports and RT-PCR data. The authors provisioned one internally held-out and two external test sets to assess model generalizability and compare performance to traditional radiologist interpretation. The pipeline was evaluated on a prospective cohort of 80 radiographs, reporting a 95% diagnostic accuracy. The study mitigates bias in AI model development and demonstrates the value of an end-to-end COVID-19 triage platform.

4.
Front Microbiol ; 12: 723818, 2021.
Article in English | MEDLINE | ID: covidwho-1581279

ABSTRACT

COVID-19 is a severe disease in humans, as highlighted by the current global pandemic. Several studies about the metabolome of COVID-19 patients have revealed metabolic disorders and some potential diagnostic markers during disease progression. However, the longitudinal changes of metabolomics in COVID-19 patients, especially their association with disease progression, are still unclear. Here, we systematically analyzed the dynamic changes of the serum metabolome of COVID-19 patients, demonstrating that most of the metabolites did not recover by 1-3 days before discharge. A prominent signature in COVID-19 patients comprised metabolites of amino acids, peptides, and analogs, involving nine essential amino acids, 10 dipeptides, and four N-acetylated amino acids. The levels of 12 metabolites in amino acid metabolism, especially three metabolites of the ornithine cycle, were significantly higher in severe patients than in mild ones, mainly on days 1-3 or 4-6 since onset. Integrating blood metabolomic, biochemical, and cytokine data, we uncovered a highly correlated network, including 6 cytokines, 13 biochemical parameters, and 49 metabolites. Significantly, five ornithine cycle-related metabolites (ornithine, N-acetylornithine, 3-amino-2-piperidone, aspartic acid, and asparagine) highly correlated with "cytokine storms" and coagulation index. We discovered that the ornithine cycle dysregulation significantly correlated with inflammation and coagulation in severe patients, which may be a potential mechanism of COVID-19 pathogenicity. Our study provided a valuable resource for detailed exploration of metabolic factors in COVID-19 patients, guiding metabolic recovery, understanding the pathogenic mechanisms, and creating drugs against SARS-CoV-2 infection.

5.
WIREs Nanomedicine and Nanobiotechnology ; n/a(n/a):e1754, 2021.
Article in English | Wiley | ID: covidwho-1411114

ABSTRACT

Abstract Viruses are infectious agents that pose significant threats to plants, animals, and humans. The current coronavirus disease 2019 pandemic, which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has spread globally and resulted in over 2 million deaths and immeasurable financial losses. Rapid and sensitive virus diagnostics become crucially important in controlling the spread of a pandemic before effective treatment and vaccines are available. Gold nanoparticle (AuNP)-based testing holds great potential for this urgent unmet biomedical need. In this review, we describe the most recent advances in AuNP-based viral detection applications. In addition, we discuss considerations for the design of AuNP-based SARS-CoV-2 testings. Finally, we highlight and propose important parameters to consider for the future development of effective AuNP-based testings that would be critical for not only this COVID-19 pandemic, but also potential future outbreaks. This article is categorized under: Diagnostic Tools > Biosensing Diagnostic Tools > In Vitro Nanoparticle-Based Sensing

6.
IEEE/ACM Trans Comput Biol Bioinform ; PP2021 Aug 05.
Article in English | MEDLINE | ID: covidwho-1343786

ABSTRACT

Accurate and rapid diagnosis of coronavirus disease 2019 (COVID-19) from chest CT scans is of great importance and urgency. However, radiologists have to distinguish COVID-19 pneumonia from other pneumonia in a large number of CT scans, which is tedious and inefficient. Thus, it is urgently and clinically needed to develop an efficient and accurate diagnostic tool to help radiologists to fulfill the difficult task. In this study, we proposed a deep supervised autoencoder (DSAE) framework to automatically identify COVID-19 using multi-view features extracted from CT images. To fully explore features characterizing CT images from different frequency domains, DSAE was proposed to learn the latent representation by multi-task learning. The proposal was designed to both encode valuable information from different frequency features and construct a compact class structure for separability. To achieve this, we designed a multi-task loss function, which consists of a supervised loss and a reconstruction loss. Our proposed method was evaluated on a newly collected dataset of 787 subjects including COVID-19 pneumonia patients, other pneumonia patients, and normal subjects without abnormal CT findings. Extensive experimental results demonstrated that our proposed method achieved encouraging diagnostic performance and may have potential clinical application for the diagnosis of COVID-19.

7.
Brief Bioinform ; 22(2): 963-975, 2021 03 22.
Article in English | MEDLINE | ID: covidwho-1343638

ABSTRACT

The Novel Coronavirus Disease 2019 (COVID-19) has become an international public health emergency, which poses the most serious threat to the human health around the world. Accumulating evidences have shown that the new coronavirus could not only infect human beings, but also can infect other species which might result in the cross-species infections. In this research, 1056 ACE2 protein sequences are collected from the NCBI database, and 173 species with >60% sequence identity compared with that of human beings are selected for further analysis. We find 14 polar residues forming the binding interface of ACE2/2019-nCoV-Spike complex play an important role in maintaining protein-protein stability. Among them, 8 polar residues at the same positions with that of human ACE2 are highly conserved, which ensure its basic binding affinity with the novel coronavirus. 5 of other 6 unconserved polar residues (positions at human ACE2: Q24, D30, K31, H34 and E35) are proved to have an effect on the binding patterns among species. We select 21 species keeping close contacts with human beings, construct their ACE2 three-dimensional structures by Homology Modeling method and calculate the binding free energies of their ACE2/2019-nCoV-Spike complexes. We find the ACE2 from all the 21 species possess the capabilities to bind with the novel coronavirus. Compared with the human beings, 8 species (cow, deer, cynomys, chimpanzee, monkey, sheep, dolphin and whale) present almost the same binding abilities, and 3 species (bat, pig and dog) show significant improvements in binding affinities. We hope this research could provide significant help for the future epidemic detection, drug and vaccine development and even the global eco-system protections.


Subject(s)
Angiotensin-Converting Enzyme 2/metabolism , SARS-CoV-2/metabolism , Animals , Humans , Protein Binding , Species Specificity , Spike Glycoprotein, Coronavirus/metabolism
8.
Journal of Advanced Research ; 2021.
Article in English | ScienceDirect | ID: covidwho-1330938

ABSTRACT

Introduction The SARS-CoV-2 pandemic has endangered global health, the world economy, and societal values. Despite intensive measures taken around the world, morbidity and mortality remain high as many countries face new waves of infection and the spread of new variants. Worryingly, more and more variants are now being identified, such as 501Y.V1 (B.1.1.7) in the UK, 501Y.V2 (B.1.351) in South Africa, 501Y.V3 in Manaus, Brazil, and B.1.617/B.1.618 in India, which could lead to a severe epidemic rebound. Moreover, some variants have a stronger immune escape ability. To control the new SARS-CoV-2 variant, we may need to develop and redesign new vaccines repeatedly. So it is important to investigate how our immune system combats and responds to SARS-CoV-2 infection to develop safe and effective medical interventions. Objectives In this study, we performed a longitudinal and proteome-wide analysis of antibodies in the COVID-19 patients to revealed some immune processes of COVID-19 patients against SARS-CoV-2 and found some dominant epitopes of a potential vaccine. Methods Microarray assay, Antibody depletion assays, Neutralization assay Results We profiled a B-cell linear epitope landscape of SARS-CoV-2 and identified the epitopes specifically recognized by either IgM, IgG, or IgA. We found that epitopes more frequently recognized by IgM are enriched in non-structural proteins. We further identified epitopes with different immune responses in severe and mild patients. Moreover, we identified 12 dominant epitopes eliciting antibodies in most COVID-19 patients and identified five key amino acids of epitopes. Furthermore, we found epitope S-82 and S-15 are perfect immunogenic peptides and should be considered in vaccine design. Conclusion This data provide useful information and rich resources for improving our understanding of viral infection and developing a novel vaccine/neutralizing antibodies for the treatment of SARS-CoV-2.

9.
Eur Radiol ; 32(1): 205-212, 2022 Jan.
Article in English | MEDLINE | ID: covidwho-1293361

ABSTRACT

OBJECTIVES: Early recognition of coronavirus disease 2019 (COVID-19) severity can guide patient management. However, it is challenging to predict when COVID-19 patients will progress to critical illness. This study aimed to develop an artificial intelligence system to predict future deterioration to critical illness in COVID-19 patients. METHODS: An artificial intelligence (AI) system in a time-to-event analysis framework was developed to integrate chest CT and clinical data for risk prediction of future deterioration to critical illness in patients with COVID-19. RESULTS: A multi-institutional international cohort of 1,051 patients with RT-PCR confirmed COVID-19 and chest CT was included in this study. Of them, 282 patients developed critical illness, which was defined as requiring ICU admission and/or mechanical ventilation and/or reaching death during their hospital stay. The AI system achieved a C-index of 0.80 for predicting individual COVID-19 patients' to critical illness. The AI system successfully stratified the patients into high-risk and low-risk groups with distinct progression risks (p < 0.0001). CONCLUSIONS: Using CT imaging and clinical data, the AI system successfully predicted time to critical illness for individual patients and identified patients with high risk. AI has the potential to accurately triage patients and facilitate personalized treatment. KEY POINT: • AI system can predict time to critical illness for patients with COVID-19 by using CT imaging and clinical data.


Subject(s)
COVID-19 , Artificial Intelligence , Humans , Retrospective Studies , SARS-CoV-2 , Tomography, X-Ray Computed
10.
Lancet Digit Health ; 3(5): e286-e294, 2021 05.
Article in English | MEDLINE | ID: covidwho-1152741

ABSTRACT

BACKGROUND: Chest x-ray is a relatively accessible, inexpensive, fast imaging modality that might be valuable in the prognostication of patients with COVID-19. We aimed to develop and evaluate an artificial intelligence system using chest x-rays and clinical data to predict disease severity and progression in patients with COVID-19. METHODS: We did a retrospective study in multiple hospitals in the University of Pennsylvania Health System in Philadelphia, PA, USA, and Brown University affiliated hospitals in Providence, RI, USA. Patients who presented to a hospital in the University of Pennsylvania Health System via the emergency department, with a diagnosis of COVID-19 confirmed by RT-PCR and with an available chest x-ray from their initial presentation or admission, were retrospectively identified and randomly divided into training, validation, and test sets (7:1:2). Using the chest x-rays as input to an EfficientNet deep neural network and clinical data, models were trained to predict the binary outcome of disease severity (ie, critical or non-critical). The deep-learning features extracted from the model and clinical data were used to build time-to-event models to predict the risk of disease progression. The models were externally tested on patients who presented to an independent multicentre institution, Brown University affiliated hospitals, and compared with severity scores provided by radiologists. FINDINGS: 1834 patients who presented via the University of Pennsylvania Health System between March 9 and July 20, 2020, were identified and assigned to the model training (n=1285), validation (n=183), or testing (n=366) sets. 475 patients who presented via the Brown University affiliated hospitals between March 1 and July 18, 2020, were identified for external testing of the models. When chest x-rays were added to clinical data for severity prediction, area under the receiver operating characteristic curve (ROC-AUC) increased from 0·821 (95% CI 0·796-0·828) to 0·846 (0·815-0·852; p<0·0001) on internal testing and 0·731 (0·712-0·738) to 0·792 (0·780-0 ·803; p<0·0001) on external testing. When deep-learning features were added to clinical data for progression prediction, the concordance index (C-index) increased from 0·769 (0·755-0·786) to 0·805 (0·800-0·820; p<0·0001) on internal testing and 0·707 (0·695-0·729) to 0·752 (0·739-0·764; p<0·0001) on external testing. The image and clinical data combined model had significantly better prognostic performance than combined severity scores and clinical data on internal testing (C-index 0·805 vs 0·781; p=0·0002) and external testing (C-index 0·752 vs 0·715; p<0·0001). INTERPRETATION: In patients with COVID-19, artificial intelligence based on chest x-rays had better prognostic performance than clinical data or radiologist-derived severity scores. Using artificial intelligence, chest x-rays can augment clinical data in predicting the risk of progression to critical illness in patients with COVID-19. FUNDING: Brown University, Amazon Web Services Diagnostic Development Initiative, Radiological Society of North America, National Cancer Institute and National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health.


Subject(s)
Artificial Intelligence , COVID-19/physiopathology , Prognosis , Radiography, Thoracic , Adult , Aged , Aged, 80 and over , Female , Humans , Male , Middle Aged , Retrospective Studies , SARS-CoV-2 , Severity of Illness Index , Tomography, X-Ray Computed , United States , Young Adult
11.
IEEE/ACM Trans Comput Biol Bioinform ; PP2021 Feb 09.
Article in English | MEDLINE | ID: covidwho-1075747

ABSTRACT

The 2019-nCoV coronavirus protein was confirmed to be highly susceptible to various mutations, which can trigger apparent changes of virus' transmission capacity and even the pathogenic mechanism. In this article, the binding interface was obtained by analyzing the interaction modes between 2019-nCoV coronavirus and the human specific target protein ACE2. Based on the "SIFT server" and the "bubble" identification mechanism, 9 amino acid sites were selected as potential mutation-sites from the 2019-nCoV-S1-ACE2 binding interface. Subsequently, one total number of 171 mutant systems for 9 mutation-sites were optimized for binding-pattern comparsion analysis, and 14 mutations that may improve the binding capacity of 2019-nCoV-S1 to ACE2 were selected. The Molecular Dynamic Simulations were conducted to calculate the binding free energies of all 14 mutant systems. Finally, we found that most of the 14 mutations on the 2019-nCoV-S1 protein could enhance the binding ability between the 2019-nCoV coronavirus and the human protein ACE2. Among which, the binding capacities for G446R, Y449R and F486Y mutations could be increased by 20%, and that for S494R mutant increased even by 38.98%. We hope this research could provide significant help for the future epidemic detection, drug development research, and vaccine development and administration.

12.
Advances in Climate Change Research ; 2020.
Article in English | ScienceDirect | ID: covidwho-956836

ABSTRACT

Reductions in the transportation sector’s carbon dioxide emissions are increasingly of global concern. As one of the first low-carbon pilot and carbon trading pilot cities, and as one of the largest automobile production bases in China, Chongqing has multiple low-carbon transportation policies that are coupled. In this study, three policy scenarios are set, including: 1) improving the fuel economy of newly sold gasoline passenger cars to 5.7 l per 100 km by 2020, 2) promoting pure electric private cars to increase the share to 7% of private car sales by 2020, and 3) the policy mix scenario of the above two policies. Simulations are undertaken with the Chinese Academy of Sciences general equilibrium (CAS-GE) model, a type of computable GE model, to assess the macro-economic impact and the industrial impact of the three policy scenarios. Through the policy impact mechanism analysis and data-mapping process, the micro-economic impact analysis results, including costs and fuel savings, for the two policies from the bottom-up model are taken as the shock variables and inputs for the CAS-GE model. The results show that: 1) the two policies will both have a slightly negative impact (−0.09% and −0.30%) on Chongqing’s GDP in 2020;2) the employment rate will decrease by 0.12% and 0.47%, but the inflation rate will be restrained to a certain extent (−0.21% and −0.17%);and 3) the complementarity of the mixed policy can weaken the negative impact of the two policies when implemented separately. The mixed policy will reduce the GDP slightly by 0.37%, compared with the cumulative effect of the two policies implemented separately, resulting in cost-effective synergies at the macro-economic impact level;and 4) the COVID-19 pandemic in 2020 has an uncertain impact on the results. The method and results can provide a reference for the formulation and adjustment of low-carbon transportation policies in other large cities.

13.
Chin. J. Pharm. Biotechnol. ; 1(27): 8-13, 20200201.
Article in Chinese | WHO COVID, ELSEVIER | ID: covidwho-677734

ABSTRACT

With the rapid alleviation of the new coronavirus epidemic, China has progressed into a stage of low or no viral transmission.Although local and sporadic outbreaks are still a real possibility, with the valuable medical treatment and public epidemic management experience, as well as the high vigilance and preparedness of the entire country, potential future outbreak should be "preventable and manageable".According to experts from home and abroad, the novel coronavirus will likely to exist within human communities for a long time with seasonal or sporadic recurrence.Therefore, a critical and essential defense measure to prevent next epidemic is to establish broad immunity within large population against the new coronavirus.At present, countries around the world are aggressively developing vaccines against the new coronavirus, in order to build up so-called "Herd Immunity"through vaccination.In this review, the author discussed several vaccine development technology platforms and compared the advantages and disadvantages of these platforms and their products, in a hope to provide useful thoughts and suggestions for scientists who are fiercely developing novel vaccines against time.

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