Proxalutamide for the treatment of COVID-19 rebound following Paxlovid treatment: Report of four cases and review of the literature.

BACKGROUND: The pandemic the coronavirus disease 2019 (COVID-19) has created a global health crisis. Although Paxlovid is recommended for the early-stage treatment of mild-to-moderate COVID-19 in patients at increased risk of progression to severe COVID-19, more and more cases are reported a COVID-19 rebound after Paxlovid treatment. Currently, information on the additional treatment for COVID-19 rebound following Paxlovid treatment is limited. CASE REPORT: Here, we present four cases with COVID-19 who were mild on admission. All cases experienced a COVID-19 rebound and progressed to severe COVID-19, following treatment with Paxlovid (300 mg of nirmatrelvir with 100 mg ritonavir, twice daily for 5 days). After being treated with proxalutamide (300 mg/day), all cases finally turned real-time reverse transcription polymerase chain reaction (RT-PCR) negative. CONCLUSION: Our cases suggested that proxalutamide might be an effective remedial treatment option for patients experiencing a COVID-19 rebound after Paxlovid treatment.

Three-dose vaccination-induced immune responses protect against SARS-CoV-2 Omicron BA.2: a population-based study in Hong Kong.

Background: The ongoing outbreak of SARS-CoV-2 Omicron BA.2 infections in Hong Kong, the model city of universal masking of the world, has resulted in a major public health crisis. Although the third vaccination resulted in strong boosting of neutralization antibody, vaccine efficacy and correlate of immune protection against the major circulating Omicron BA.2 remain to be investigated. Methods: We investigated the vaccine efficacy against the Omicron BA.2 breakthrough infection among 470 public servants who had received different SARS-CoV-2 vaccine regimens including two-dose BNT162b2 (2 × BNT, n = 169), three-dose BNT162b2 (3 × BNT, n = 168), two-dose CoronaVac (2 × CorV, n = 34), three-dose CoronaVac (3 × CorV, n = 67) and third-dose BNT162b2 following 2 × CorV (2 × CorV+1BNT, n = 32). Humoral and cellular immune responses after three-dose vaccination were further characterized and correlated with clinical characteristics of BA.2 infection. Findings: During the BA.2 outbreak, 27.7% vaccinees were infected. The timely third-dose vaccination provided significant protection with lower incidence rates of breakthrough infections (2 × BNT 46.2% vs 3 × BNT 13.1%, p < 0.0001; 2 × CorV 44.1% vs 3 × CorV 19.4%, p = 0.003). Investigation of immune responses on blood samples derived from 90 subjects in three-dose vaccination cohorts collected before the BA.2 outbreak revealed that the third-dose vaccination activated spike (S)-specific memory B cells and Omicron cross-reactive T cell responses, which correlated with reduced frequencies of breakthrough infections and disease severity rather than with types of vaccines. Moreover, the frequency of S-specific activated memory B cells was significantly lower in infected vaccinees than uninfected vaccinees before vaccine-breakthrough infection whereas IFN-γ+ CD4 T cells were negatively associated with age and viral clearance time. Critically, BA.2 breakthrough infection boosted cross-reactive memory B cells with enhanced cross-neutralizing antibodies to Omicron sublineages, including BA.2.12.1 and BA.4/5, in all vaccinees tested. Interpretation: Our results imply that the timely third vaccination and immune responses are likely required for vaccine-mediated protection against Omicron BA.2 pandemic. Although BA.2 conferred the highest neutralization resistance compared with variants of concern tested before the emergence of BA.2.12.1 and BA.4/5, the third dose vaccination-activated S-specific memory B cells and Omicron cross-reactive T cell responses contributed to reduced frequencies of breakthrough infection and disease severity. Neutralizing antibody potency enhanced by BA.2 breakthrough infection in vaccinees with prior 3 doses of CoronaVac or BNT162b2 may reduce the risk of infection against ongoing BA.2.12.1 and BA.4/5. Funding: Hong Kong Research Grants Council Collaborative Research Fund, Health and Medical Research Fund, Wellcome Trust, Shenzhen Science and Technology Program, the Health@InnoHK, Innovation and Technology Commission of Hong Kong, China, National Program on Key Research Project, Emergency Key Program of Guangzhou Laboratory, donations from the Friends of Hope Education Fund and the Hong Kong Theme-Based Research Scheme.

Evaluation of COVID-19 vaccines in primary prevention against infections and reduction in severity of illness following the outbreak of SARS-CoV-2 omicron variant in Shanghai.

Objectives: To evaluate COVID-19 vaccines in primary prevention against infections and lessen the severity of illness following the most recent outbreak of the SARS-CoV-2 Omicron variant in Shanghai. Data sources: Data from 153,544 COVID-19 patients admitted to the Shanghai "Four-Leaf Clover" Fangcang makeshift shelter hospital were collected using a structured electronic questionnaire, which was then merged with electronic medical records of the hospital. For healthy controls, data on vaccination status and other information were obtained from 228 community-based residents, using the same structured electronic questionnaire. Methods: To investigate whether inactivated vaccines were effective in protecting against SARS-CoV-2 virus, we estimated the odds ratio (OR) of the vaccination by comparing cases and matched community-based healthy controls. To evaluate the potential benefits of vaccination in lowering the risk of symptomatic infection (vs. asymptomatic), we estimated the relative risk (RR) of symptomatic infections among diagnosed patients. We also applied multivariate stepwise logistic regression analyses to measure the risk of disease severity (symptomatic vs. asymptomatic and moderate/severe vs. mild) in the COVID-19 patient cohort with vaccination status as an independent variable while controlling for potential confounding factors. Results: Of the 153,544 COVID-19 patients included in the analysis, the mean age was 41.59 years and 90,830 were males (59.2%). Of the study cohort, 118,124 patients had been vaccinated (76.9%) and 143,225 were asymptomatic patients (93.3%). Of the 10,319 symptomatic patients, 10,031 (97.2%), 281 (2.7%), and 7 (0.1%) experienced mild, moderate, and severe infections, respectively. Hypertension (8.7%) and diabetes (3.0%) accounted for the majority of comorbidities. There is no evidence that the vaccination helped protect from infections (OR = 0.82, p = 0.613). Vaccination, however, offered a small but significant protection against symptomatic infections (RR = 0.92, p < 0.001) and halved the risk of moderate/severe infections (OR = 0.48, 95% CI: 0.37-0.61). Older age (≥60 years) and malignant tumors were significantly associated with moderate/severe infections. Conclusion: Inactivated COVID-19 vaccines helped provide small but significant protection against symptomatic infections and halved the risk of moderate/severe illness among symptomatic patients. The vaccination was not effective in blocking the SARS-CoV-2 Omicron Variant community spread.

MESTrans: Multi-scale embedding spatial transformer for medical image segmentation.

BACKGROUND AND OBJECTIVE: Transformers profiting from global information modeling derived from the self-attention mechanism have recently achieved remarkable performance in computer vision. In this study, a novel transformer-based medical image segmentation network called the multi-scale embedding spatial transformer (MESTrans) was proposed for medical image segmentation. METHODS: First, a dataset called COVID-DS36 was created from 4369 computed tomography (CT) images of 36 patients from a partner hospital, of which 18 had COVID-19 and 18 did not. Subsequently, a novel medical image segmentation network was proposed, which introduced a self-attention mechanism to improve the inherent limitation of convolutional neural networks (CNNs) and was capable of adaptively extracting discriminative information in both global and local content. Specifically, based on U-Net, a multi-scale embedding block (MEB) and multi-layer spatial attention transformer (SATrans) structure were designed, which can dynamically adjust the receptive field in accordance with the input content. The spatial relationship between multi-level and multi-scale image patches was modeled, and the global context information was captured effectively. To make the network concentrate on the salient feature region, a feature fusion module (FFM) was established, which performed global learning and soft selection between shallow and deep features, adaptively combining the encoder and decoder features. Four datasets comprising CT images, magnetic resonance (MR) images, and H&E-stained slide images were used to assess the performance of the proposed network. RESULTS: Experiments were performed using four different types of medical image datasets. For the COVID-DS36 dataset, our method achieved a Dice similarity coefficient (DSC) of 81.23%. For the GlaS dataset, 89.95% DSC and 82.39% intersection over union (IoU) were obtained. On the Synapse dataset, the average DSC was 77.48% and the average Hausdorff distance (HD) was 31.69 mm. For the I2CVB dataset, 92.3% DSC and 85.8% IoU were obtained. CONCLUSIONS: The experimental results demonstrate that the proposed model has an excellent generalization ability and outperforms other state-of-the-art methods. It is expected to be a potent tool to assist clinicians in auxiliary diagnosis and to promote the development of medical intelligence technology.

Ginger supplement significantly reduced length of hospital stay in individuals with COVID-19.

BACKGROUND: Evidence from previous studies has suggested that ginger extract exhibits the potential as an alternative treatment for Coronavirus disease 2019 (COVID-19). Here, we want to investigate whether ginger supplement improves the clinical manifestation of hospitalized COVID-19 individuals. METHODS: A total of 227 hospitalized individuals with COVID-19 were randomized to either the control (n = 132) or intervention group (n = 95). The intervention group took ginger supplement orally at the dosage of 1.5 g twice daily, until they were discharged from the hospital. Both groups received the same standard of general medical care during hospitalization, and the length of stay was recorded and compared between groups. RESULTS: Among all participants, a significant reduction in hospitalization time (the difference between the treatment and control groups was 2.4 d, 95% CI 1.6-3.2) was detected in response to the ginger supplement. This effect was more pronounced in men, participants aged 60 years or older, and participants with pre-existing medical conditions, relative to their counterparts (P-interactions < 0.05 for all). CONCLUSION: Ginger supplement significantly shortened the length of stay of hospitalized individuals with COVID-19. TRIAL REGISTRATION: The trial was registered on the Chinese Clinical Trial Registry (ChiCTR2200059824).

MA-Net:Mutex attention network for COVID-19 diagnosis on CT images.

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.

Naturally occurring spike mutations influence the infectivity and immunogenicity of SARS-CoV-2.

Mutations in SARS-CoV-2 variants of concern (VOCs) have enhanced transmissibility and immune evasion with respect to current vaccines and neutralizing antibodies (NAbs). How naturally occurring spike mutations affect the infectivity and antigenicity of VOCs remains to be investigated. The entry efficiency of individual spike mutations was determined in vitro using pseudotyped viruses. BALB/c mice were immunized with 2-dose DNA vaccines encoding B.1.1.7, B.1.351, B.1.1.529  and their single mutations. Cellular and humoral immune responses were then compared to determine the impact of individual mutations on immunogenicity. In the B.1.1.7 lineage, Del69-70 and Del 144 in NTD, A570D and P681H in SD1 and S982A and D1118H in S2 significantly increased viral entry, whereas T716I resulted in a decrease. In the B.1.351 lineage, L18F and Del 242-244 in the NTD, K417N in the RBD and A701V in S2 also increased viral entry. S982A weakened the generation of binding antibodies. All sera showed reduced cross-neutralization activity against B.1.351, B.1.617.2 (Delta) and B.1.1.529 (Omicron BA.1). S982A, L18F, and Del 242-244 hindered the induction of cross-NAbs, whereas Del 69-70, Del144, R246I, and K417N showed the opposite effects. B.1.351 elicited adequate broad cross-NAbs against both B.1.351 and B.1.617.2. All immunogens tested, however, showed low neutralization against circulating B.1.1.529. In addition, T-cell responses were unlikely affected by mutations tested in the spike. We conclude that individual spike mutations influence viral infectivity and vaccine immunogenicity. Designing VOC-targeted vaccines is likely necessary to overcome immune evasion from current vaccines and neutralizing antibodies.

A Large-Scale Clinical Validation Study Using nCapp Cloud Plus Terminal by Frontline Doctors for the Rapid Diagnosis of COVID-19 and COVID-19 pneumonia in China

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.

The Metaverse in Current Digital Medicine

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.

Waning immune responses against SARS-CoV-2 variants of concern among vaccinees in Hong Kong.

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).

MA-Net:Mutex attention network for COVID-19 diagnosis on CT images

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.

Nasal prevention of SARS-CoV-2 infection by intranasal influenza-based boost vaccination in mouse models.

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.

Lung Cancer Screening Considerations During Respiratory Infection Outbreaks, Epidemics or Pandemics: An International Association for the Study of Lung Cancer Early Detection and Screening Committee Report.

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.

MSD-Net: Multi-Scale Discriminative Network for COVID-19 Lung Infection Segmentation on CT.

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.

Images denoising for COVID-19 chest X-ray based on multi-resolution parallel residual CNN.

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.

A multi-class COVID-19 segmentation network with pyramid attention and edge loss in CT images.

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.

A label-free electrochemical assay for coronavirus IBV H120 strain quantification based on equivalent substitution effect and AuNPs-assisted signal amplification.

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.

Chinese experts’ consensus on the Internet of Things-aided diagnosis and treatment of coronavirus disease 2019 (COVID-19)

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.