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1.
Clinical eHealth ; 2022.
Article in English | ScienceDirect | ID: covidwho-1936135

ABSTRACT

Background The outbreak of coronavirus disease 2019 (COVID-19) has become a global pandemic acute infectious disease, especially with the features of possible asymptomatic carriers and high contagiousness. Currently, it is difficult to quickly identify asymptomatic cases or COVID-19 patients with pneumonia due to limited access to reverse transcription-polymerase chain reaction (RT-PCR) nucleic acid tests and CT scans. Goal This study aimed to develop a scientific and rigorous clinical diagnostic tool for the rapid prediction of COVID-19 cases based on a COVID-19 clinical case database in China, and to assist doctors to efficiently and precisely diagnose asymptomatic COVID-19 patients and cases who had a false-negative RT-PCR test result. Methods With online consent, and the approval of the ethics committee of Zhongshan Hospital Fudan University (NCT04275947, B2020-032R) to ensure that patient privacy is protected, clinical information has been uploaded in real-time through the New Coronavirus Intelligent Auto-diagnostic Assistant Application of cloud plus terminal (nCapp) by doctors from different cities (Wuhan, Shanghai, Harbin, Dalian, Wuxi, Qingdao, Rizhao, and Bengbu) during the COVID-19 outbreak in China. By quality control and data anonymization on the platform, a total of 3,249 cases from COVID-19 high-risk groups were collected. The effects of different diagnostic factors were ranked based on the results from a single factor analysis, with 0.05 as the significance level for factor inclusion and 0.1 as the significance level for factor exclusion. Independent variables were selected by the step-forward multivariate logistic regression analysis to obtain the probability model. Findings We applied the statistical method of a multivariate regression model to the training dataset (1,624 cases) and developed a prediction model for COVID-19 with 9 clinical indicators that are accessible. The area under the receiver operating characteristic (ROC) curve (AUC) for the model was 0.88 (95% CI: 0.86, 0.89) in the training dataset and 0.84 (95% CI: 0.82, 0.86) in the validation dataset (1,625 cases). Discussion With the assistance of nCapp, a mobile-based diagnostic tool developed from a large database that we collected from COVID-19 high-risk groups in China, frontline doctors can rapidly identify asymptomatic patients and avoid misdiagnoses of cases with false-negative RT-PCR results.

2.
Clinical eHealth ; 2022.
Article in English | ScienceDirect | ID: covidwho-1926264

ABSTRACT

The metaverse has entered people's horizons through virtual reality, digital twinning, the Internet of Things, blockchain technology, etc. In the current healthcare system, the management of chronic diseases, such as chronic obstructive pulmonary disease (COPD) and obstructive sleep apnea-hypopnea syndrome (OSAHS), still faces challenges, such as uneven distribution of medical resources, and difficulty in follow-up, overburdening of specialists, and so on. However, metaverse medical platforms incorporating advanced AI technologies, such as industrial-scale digital twins, may address these issues. In this article, we discuss the application prospect of these technologies in digital medicine and the future of the medical metaverse.

3.
EBioMedicine ; 77: 103904, 2022 Mar.
Article in English | MEDLINE | ID: covidwho-1867071

ABSTRACT

BACKGROUND: Nearly 4 billion doses of the BNT162b2-mRNA and CoronaVac-inactivated vaccines have been administrated globally, yet different vaccine-induced immunity against SARS-CoV-2 variants of concern (VOCs) remain incompletely investigated. METHODS: We compare the immunogenicity and durability of these two vaccines among fully vaccinated Hong Kong people. FINDINGS: Standard BNT162b2 and CoronaVac vaccinations were tolerated and induced neutralizing antibody (NAb) (100% and 85.7%) and spike-specific CD4 T cell responses (96.7% and 82.1%), respectively. The geometric mean NAb IC50 and median frequencies of reactive CD4 subsets were consistently lower among CoronaVac-vaccinees than BNT162b2-vaccinees. CoronaVac did not induce measurable levels of nucleocapsid protein-specific IFN-γ+ CD4+ T or IFN-γ+ CD8+ T cells compared with unvaccinated. Against VOCs, NAb response rates and geometric mean IC50 titers against B.1.617.2 (Delta) and B.1.1.529 (Omicron) were significantly lower for CoronaVac (50%, 23.2 and 7.1%, <20) than BNT162b2 (94.1%, 131 and 58.8%, 35.0), respectively. Three months after vaccinations, NAbs to VOCs dropped near to detection limit, along with waning memory T cell responses, mainly among CoronaVac-vaccinees. INTERPRETATION: Our results indicate that vaccinees especially CoronaVac-vaccinees with significantly reduced NAbs may probably face higher risk to pandemic VOCs breakthrough infection. FUNDING: This study was supported by the Hong Kong Research Grants Council Collaborative Research Fund (C7156-20GF and C1134-20GF); the Wellcome Trust (P86433); the National Program on Key Research Project of China (Grant 2020YFC0860600, 2020YFA0707500 and 2020YFA0707504); Shenzhen Science and Technology Program (JSGG20200225151410198 and JCYJ20210324131610027); HKU Development Fund and LKS Faculty of Medicine Matching Fund to AIDS Institute; Hong Kong Innovation and Technology Fund, Innovation and Technology Commission and generous donation from the Friends of Hope Education Fund. Z.C.'s team was also partly supported by the Theme-Based Research Scheme (T11-706/18-N).


Subject(s)
COVID-19 , SARS-CoV-2 , Antibodies, Neutralizing , Antibodies, Viral , CD8-Positive T-Lymphocytes , COVID-19/epidemiology , COVID-19/prevention & control , Hong Kong/epidemiology , Humans , Immunity , SARS-CoV-2/genetics , Vaccination
4.
EuropePMC; 2022.
Preprint in English | EuropePMC | ID: ppcovidwho-336861

ABSTRACT

Summary The ongoing outbreak of SARS-CoV-2 Omicron BA.2 infections in Hong Kong, the world model city of universal masking, has resulted in a major public health crisis. In this study, we investigate public servants who had been vaccinated with two dose (82.7%) or three dose (14%) of either CoronaVac (CorV) or BNT162b2 (BNT). During the BA.2 outbreak, 29.3% vaccinees were infected. Three-dose vaccination provided protection with lower incidence rates of breakthrough infections (2×BNT 49.2% vs 3×BNT 16.6%, p <0.0001;2×CorV 48.6% vs 3×CoV 20.6%, p =0.003). The third heterologous vaccination showed the lowest incidence (2×CorV+1×BNT 6.3%). Although BA.2 conferred the highest neutralization resistance compared with variants of concern tested, the third dose vaccination-activated spike-specific memory B and Omicron cross-reactive T cell responses contributed to reduced frequencies of breakthrough infection and disease severity. Our results have implications to timely boost vaccination and immune responses likely required for vaccine-mediated protection against Omicron BA.2 pandemic.

5.
EuropePMC; 2022.
Preprint in English | EuropePMC | ID: ppcovidwho-336250

ABSTRACT

Background: The ongoing outbreak of SARS-CoV-2 Omicron BA.2 infections in Hong Kong, the world model city of universal masking, has resulted in a major public health crisis. Although the third heterologous BNT162b2 vaccination after 2-dose CoronaVac generated higher neutralizing antibody responses than the third homologous CoronaVac booster, vaccine efficacy and corelates of immune protection against the major circulating Omicron BA.2 remains to be investigated. Methods: : We investigated the vaccine efficacy against the Omicron BA.2 breakthrough infection among 481 public servants who had been received with SARS-CoV-2 vaccines including two-dose BNT162b2 (2×BNT, n=169), three-dose BNT162b2 (2×BNT, n=175), two-dose CoronaVac (2×CorV, n=37), three-dose CoronaVac (3×CorV, n=68) and third-dose BNT162b2 following 2×CorV (2×CorV+1BNT, n=32). Humoral and cellular immune responses after three-dose vaccination were characterized and correlated with clinical characteristics of BA.2 infection. Results: : During the BA.2 outbreak, 29.3% vaccinees were infected. Three-dose vaccination provided protection with lower incidence rates of breakthrough infections (2×BNT 49.2% vs 3×BNT 16.6%, p<0.0001;2×CorV 48.6% vs 3×CoV 20.6%, p=0.003). The third heterologous vaccination showed the lowest incidence (2×CorV+1×BNT 6.3%). Although BA.2 conferred the highest neutralization resistance compared with variants of concern tested, the third dose vaccination-activated spike-specific memory B and Omicron cross-reactive T cell responses contributed to reduced frequencies of breakthrough infection and disease severity. Conclusions: : Our results have implications to timely boost vaccination and immune responses likely required for vaccine-mediated protection against Omicron BA.2 pandemic.

6.
Applied Intelligence ; : 1-16, 2022.
Article in English | EuropePMC | ID: covidwho-1782300

ABSTRACT

COVID-19 is an infectious pneumonia caused by 2019-nCoV. The number of newly confirmed cases and confirmed deaths continues to remain at a high level. RT–PCR is the gold standard for the COVID-19 diagnosis, but the computed tomography (CT) imaging technique is an important auxiliary diagnostic tool. In this paper, a deep learning network mutex attention network (MA-Net) is proposed for COVID-19 auxiliary diagnosis on CT images. Using positive and negative samples as mutex inputs, the proposed network combines mutex attention block (MAB) and fusion attention block (FAB) for the diagnosis of COVID-19. MAB uses the distance between mutex inputs as a weight to make features more distinguishable for preferable diagnostic results. FAB acts to fuse features to obtain more representative features. Particularly, an adaptive weight multiloss function is proposed for better effect. The accuracy, specificity and sensitivity were reported to be as high as 98.17%, 97.25% and 98.79% on the COVID-19 dataset-A provided by the Affiliated Medical College of Qingdao University, respectively. State-of-the-art results have also been achieved on three other public COVID-19 datasets. The results show that compared with other methods, the proposed network can provide effective auxiliary information for the diagnosis of COVID-19 on CT images.

8.
EBioMedicine ; 75: 103762, 2022 Jan.
Article in English | MEDLINE | ID: covidwho-1587929

ABSTRACT

BACKGROUND: Vaccines in emergency use are efficacious against COVID-19, yet vaccine-induced prevention against nasal SARS-CoV-2 infection remains suboptimal. METHODS: Since mucosal immunity is critical for nasal prevention, we investigated the efficacy of an intramuscular PD1-based receptor-binding domain (RBD) DNA vaccine (PD1-RBD-DNA) and intranasal live attenuated influenza-based vaccines (LAIV-CA4-RBD and LAIV-HK68-RBD) against SARS-CoV-2. FINDINGS: Substantially higher systemic and mucosal immune responses, including bronchoalveolar lavage IgA/IgG and lung polyfunctional memory CD8 T cells, were induced by the heterologous PD1-RBD-DNA/LAIV-HK68-RBD as compared with other regimens. When vaccinated animals were challenged at the memory phase, prevention of robust SARS-CoV-2 infection in nasal turbinate was achieved primarily by the heterologous regimen besides consistent protection in lungs. The regimen-induced antibodies cross-neutralized variants of concerns. Furthermore, LAIV-CA4-RBD could boost the BioNTech vaccine for improved mucosal immunity. INTERPRETATION: Our results demonstrated that intranasal influenza-based boost vaccination induces mucosal and systemic immunity for effective SARS-CoV-2 prevention in both upper and lower respiratory systems. FUNDING: This study was supported by the Research Grants Council Collaborative Research Fund, General Research Fund and Health and Medical Research Fund in Hong Kong; Outbreak Response to Novel Coronavirus (COVID-19) by the Coalition for Epidemic Preparedness Innovations; Shenzhen Science and Technology Program and matching fund from Shenzhen Immuno Cure BioTech Limited; the Health@InnoHK, Innovation and Technology Commission of Hong Kong; National Program on Key Research Project of China; donations from the Friends of Hope Education Fund; the Theme-Based Research Scheme.


Subject(s)
COVID-19 Vaccines , COVID-19/prevention & control , Immunization, Secondary , Influenza Vaccines , SARS-CoV-2 , Vaccines, DNA , Administration, Intranasal , Animals , COVID-19/genetics , COVID-19/immunology , COVID-19 Vaccines/genetics , COVID-19 Vaccines/immunology , Chlorocebus aethiops , Disease Models, Animal , Dogs , Female , HEK293 Cells , Humans , Immunity, Mucosal , Influenza Vaccines/genetics , Influenza Vaccines/immunology , Madin Darby Canine Kidney Cells , Male , Mice , Mice, Inbred BALB C , Mice, Transgenic , SARS-CoV-2/genetics , SARS-CoV-2/immunology , Vaccines, Attenuated/genetics , Vaccines, Attenuated/immunology , Vaccines, DNA/genetics , Vaccines, DNA/immunology , Vero Cells
9.
J Thorac Oncol ; 17(2): 228-238, 2022 02.
Article in English | MEDLINE | ID: covidwho-1587119

ABSTRACT

After the results of two large, randomized trials, the global implementation of lung cancer screening is of utmost importance. However, coronavirus disease 2019 infections occurring at heightened levels during the current global pandemic and also other respiratory infections can influence scan interpretation and screening safety and uptake. Several respiratory infections can lead to lesions that mimic malignant nodules and other imaging changes suggesting malignancy, leading to an increased level of follow-up procedures or even invasive diagnostic procedures. In periods of increased rates of respiratory infections from severe acute respiratory syndrome coronavirus 2 and others, there is also a risk of transmission of these infections to the health care providers, the screenees, and patients. This became evident with the severe acute respiratory syndrome coronavirus 2 pandemic that led to a temporary global stoppage of lung cancer and other cancer screening programs. Data on the optimal management of these situations are not available. The pandemic is still ongoing and further periods of increased respiratory infections will come, in which practical guidance would be helpful. The aims of this report were: (1) to summarize the data available for possible false-positive results owing to respiratory infections; (2) to evaluate the safety concerns for screening during times of increased respiratory infections, especially during a regional outbreak or an epidemic or pandemic event; (3) to provide guidance on these situations; and (4) to stimulate research and discussions about these scenarios.


Subject(s)
COVID-19 , Lung Neoplasms , Respiratory Tract Infections , Disease Outbreaks , Early Detection of Cancer , Humans , Lung , Lung Neoplasms/diagnosis , Lung Neoplasms/epidemiology , Pandemics , Respiratory Tract Infections/diagnosis , Respiratory Tract Infections/epidemiology , SARS-CoV-2
10.
IEEE Access ; 8: 185786-185795, 2020.
Article in English | MEDLINE | ID: covidwho-1528291

ABSTRACT

Since the first patient reported in December 2019, 2019 novel coronavirus disease (COVID-19) has become global pandemic with more than 10 million total confirmed cases and 500 thousand related deaths. Using deep learning methods to quickly identify COVID-19 and accurately segment the infected area can help control the outbreak and assist in treatment. Computed tomography (CT) as a fast and easy clinical method, it is suitable for assisting in diagnosis and treatment of COVID-19. According to clinical manifestations, COVID-19 lung infection areas can be divided into three categories: ground-glass opacities, interstitial infiltrates and consolidation. We proposed a multi-scale discriminative network (MSD-Net) for multi-class segmentation of COVID-19 lung infection on CT. In the MSD-Net, we proposed pyramid convolution block (PCB), channel attention block (CAB) and residual refinement block (RRB). The PCB can increase the receptive field by using different numbers and different sizes of kernels, which strengthened the ability to segment the infected areas of different sizes. The CAB was used to fusion the input of the two stages and focus features on the area to be segmented. The role of RRB was to refine the feature maps. Experimental results showed that the dice similarity coefficient (DSC) of the three infection categories were 0.7422,0.7384,0.8769 respectively. For sensitivity and specificity, the results of three infection categories were (0.8593, 0.9742), (0.8268,0.9869) and (0.8645,0.9889) respectively. The experimental results demonstrated that the network proposed in this paper can effectively segment the COVID-19 infection on CT images. It can be adopted for assisting in diagnosis and treatment of COVID-19.

11.
Mach Vis Appl ; 32(4): 100, 2021.
Article in English | MEDLINE | ID: covidwho-1286132

ABSTRACT

Chest X-ray (CXR) is a medical imaging technology that is common and economical to use in clinical. Recently, coronavirus (COVID-19) has spread worldwide, and the second wave is rebounding strongly now with the coming winter that has a detrimental effect on the global economy and health. To make pre-diagnosis of COVID-19 as soon as possible, and reduce the work pressure of medical staff, making use of deep learning networks to detect positive CXR images of infected patients is a critical step. However, there are complex edge structures and rich texture details in the CXR images susceptible to noise that can interfere with the diagnosis of the machines and the doctors. Therefore, in this paper, we proposed a novel multi-resolution parallel residual CNN (named MPR-CNN) for CXR images denoising and special application for COVID-19 which can improve the image quality. The core of MPR-CNN consists of several essential modules. (a) Multi-resolution parallel convolution streams are utilized for extracting more reliable spatial and semantic information in multi-scale features. (b) Efficient channel and spatial attention can let the network focus more on texture details in CXR images with fewer parameters. (c) The adaptive multi-resolution feature fusion method based on attention is utilized to improve the expression of the network. On the whole, MPR-CNN can simultaneously retain spatial information in the shallow layers with high resolution and semantic information in the deep layers with low resolution. Comprehensive experiments demonstrate that our MPR-CNN can better retain the texture structure details in CXR images. Additionally, extensive experiments show that our MPR-CNN has a positive impact on CXR images classification and detection of COVID-19 cases from denoised CXR images.

12.
IET Image Process ; 15(11): 2604-2613, 2021 Sep.
Article in English | MEDLINE | ID: covidwho-1223116

ABSTRACT

At the end of 2019, a novel coronavirus COVID-19 broke out. Due to its high contagiousness, more than 74 million people have been infected worldwide. Automatic segmentation of the COVID-19 lesion area in CT images is an effective auxiliary medical technology which can quantitatively diagnose and judge the severity of the disease. In this paper, a multi-class COVID-19 CT image segmentation network is proposed, which includes a pyramid attention module to extract multi-scale contextual attention information, and a residual convolution module to improve the discriminative ability of the network. A wavelet edge loss function is also proposed to extract edge features of the lesion area to improve the segmentation accuracy. For the experiment, a dataset of 4369 CT slices is constructed, including three symptoms: ground glass opacities, interstitial infiltrates, and lung consolidation. The dice similarity coefficients of three symptoms of the model achieve 0.7704, 0.7900, 0.8241 respectively. The performance of the proposed network on public dataset COVID-SemiSeg is also evaluated. The results demonstrate that this model outperforms other state-of-the-art methods and can be a powerful tool to assist in the diagnosis of positive infection cases, and promote the development of intelligent technology in the medical field.

13.
Mikrochim Acta ; 187(11): 624, 2020 10 23.
Article in English | MEDLINE | ID: covidwho-888208

ABSTRACT

A label-free electrochemical strategy is proposed combining equivalent substitution effect with AuNPs-assisted signal amplification. According to the differences of S1 protein in various infectious bronchitis virus (IBV) strains, a target DNA sequence that can specifically recognize H120 RNA forming a DNA-RNA hybridized double-strand structure has been designed. Then, the residual single-stranded target DNA is hydrolyzed by S1 nuclease. Therefore, the content of target DNA becomes equal to the content of virus RNA. After equivalent coronavirus, the target DNA is separated from DNA-RNA hybridized double strand by heating, which can partly hybridize with probe 2 modified on the electrode surface and probe 1 on AuNPs' surface. Thus, AuNPs are pulled to the surface of the electrode and the abundant DNA on AuNPs' surface could adsorb a large amount of hexaammineruthenium (III) chloride (RuHex) molecules, which produce a remarkably amplified electrochemical response. The voltammetric signal of RuHex with a peak near - 0.28 V vs. Ag/AgCl is used as the signal output. The proposed method shows a detection range of 1.56e-9 to 1.56e-6 µM with the detection limit of 2.96e-10 µM for IBV H120 strain selective quantification detection, exhibiting good accuracy, stability, and simplicity, which shows a great potential for IBV detection in vaccine research and avian infectious bronchitis diagnosis. Graphical abstract.


Subject(s)
Biosensing Techniques/methods , Coronavirus Infections/virology , Coronavirus/isolation & purification , Electrochemical Techniques/methods , Infectious bronchitis virus/isolation & purification , Spike Glycoprotein, Coronavirus/chemistry , Animals , Biosensing Techniques/standards , Capsid Proteins/genetics , Chickens , Coronavirus/genetics , DNA Probes , Gold , In Situ Hybridization , Infectious bronchitis virus/genetics , Limit of Detection , Metal Nanoparticles/chemistry , RNA, Viral/genetics , RNA, Viral/isolation & purification , Species Specificity
14.
Clinical eHealth ; 3:7-15, 2020.
Article in English | PMC | ID: covidwho-822402

ABSTRACT

The aim is to diagnose COVID-19 earlier and to improve its treatment by applying medical technology, the “COVID-19 Intelligent Diagnosis and Treatment Assistant Program (nCapp)” based on the Internet of Things. Terminal eight functions can be implemented in real-time online communication with the “cloud” through the page selection key. According to existing data, questionnaires, and check results, the diagnosis is automatically generated as confirmed, suspected, or suspicious of 2019 novel coronavirus (2019-nCoV) infection. It classifies patients into mild, moderate, severe or critical pneumonia. nCapp can also establish an online COVID-19 real-time update database, and it updates the model of diagnosis in real time based on the latest real-world case data to improve diagnostic accuracy. Additionally, nCapp can guide treatment. Front-line physicians, experts, and managers are linked to perform consultation and prevention. nCapp also contributes to the long-term follow-up of patients with COVID-19. The ultimate goal is to enable different levels of COVID-19 diagnosis and treatment among different doctors from different hospitals to upgrade to the national and international through the intelligent assistance of the nCapp system. In this way, we can block disease transmission, avoid physician infection, and epidemic prevention and control as soon as possible.

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