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2.
Risk management and healthcare policy ; 15:447-456, 2022.
Article in English | EuropePMC | ID: covidwho-1743744

ABSTRACT

Purpose Fever is one of the most typical clinical symptoms of coronavirus disease 2019 (COVID-19), and non-contact infrared thermometers (NCITs) are commonly used to screen for fever. However, there is a lack of authoritative data to define a “fever” when an NCIT is used and previous studies have shown that NCIT readings fluctuate widely depending on ambient temperatures and the body surface site screened. The aim of this study was to establish cut-off points for normal temperatures of different body sites (neck, forehead, temples, and wrist) and investigate the accuracy of NCITs at various ambient temperatures to improve the standardization and accuracy of fever screening. Patients and Methods A prospective investigation was conducted among 904 participants in the outpatient and emergency departments of Chengdu Women’s and Children’s Central Hospital. Body temperature was measured using NCITs and mercury axillary thermometers. A receiver operating characteristic curve was used to determine the accuracy of body temperature detection at the four body surface sites. Data on participant characteristics were also collected. Results Among the four surface sites, the neck temperature detection group had the highest accuracy. When the neck temperature was 37.35°C as the optimum fever diagnostic threshold, the sensitivity was 0.866. The optimum fever diagnostic thresholds for forehead, temporal, and wrist temperature were 36.65°C, 36.65°C, and 36.75°C, respectively. Moreover, triple neck temperature detection had the highest sensitivity, up to 0.998, whereas the sensitivity of triple wrist temperature detections was 0.949. Notably, the accuracy of NCITs significantly reduced when the temperature was lower than 18°C. Conclusion Neck temperature had the highest accuracy among the four NCIT temperature measurement sites, with an optimum fever diagnostic threshold of 37.35°C. Considering the findings reported in our study, we recommend triple neck temperature detection with NCITs as the fever screening standard for COVID-19.

3.
Huan Jing Ke Xue ; 43(3): 1268-1276, 2022 Mar 08.
Article in Chinese | MEDLINE | ID: covidwho-1732501

ABSTRACT

Many restrictive measures were implemented in China from January-February 2020 to control the rapid spread of COVID-19. Many studies reported that the COVID-19 lockdown impacted PM2.5, SO2, volatile organic compounds (VOCs), etc. VOCs play important roles in the production of ozone and PM2.5. Ambient VOCs in Xiong'an were measured from December 25, 2019 to January 24, 2020 (prior to epidemic prevention, P1) and from January 25, 2020 to February 24, 2020 (during epidemic prevention, P2) through a VOCs online instrument. In the study, VOCs characteristics and ozone generation potential (OFP) of ambient VOCs were analyzed, and source apportionment of VOCs were analyzed by using Positive Matrix Factorization (PMF). The results showed that φ(TVOCs) during epidemic prevention and control was 45.1×10-9, which was approximately half of that before epidemic prevention and control (90.5×10-9). The chemical composition of VOCs showed significant changes after epidemic prevention and control, the contribution rate of alkanes increased from 37.6% to 53.8%, and the contribution rate of aromatic hydrocarbons and halogenated hydrocarbons decreased from 13.3% and 12.0% to 7.5% and 7.8%, respectively. Aromatic hydrocarbons, halogenated hydrocarbons, and OVOCs decreased by more than 60%. Seven types of the top ten species were the same before and during the epidemic prevention and control, mainly low-carbon alkanes, olefins, aldehydes, and ketones. Dichloromethane, trichloromethane, and BTEXs decreased significantly. The OPP was 566 µg·m-3 and 231 µg·m-3 in P1 and P2, respectively. The OPP of VOCs decreased by more than 30%. The proportion of OFP contribution of aromatic hydrocarbons decreased significantly after the epidemic prevention and control, and the proportion of OFP contribution of alkanes and alkynes increased significantly. Positive matrix factorization (PMF) was then applied for VOCs sources apportionment. Six sources were identified, including background sources, oil-gas volatile sources, combustion sources, industrial sources, solvent use sources, and vehicle exhaust sources. The results showed that after the epidemic prevention and control, the contribution rate of solvent use sources to TVOCs decreased from 24% to 9%. The contribution rates of background sources, oil-gas volatile sources, and combustion sources increased from 13%, 34%, and 24% to 6%, 14%, and 13%, respectively. The relative contributions of vehicle exhaust sources before and after epidemic prevention and control were 21% and 18%, respectively. The observation points were affected by the emission of VOCs from paroxysmal industrial sources before the epidemic prevention and control. The emission was stopped after the epidemic prevention and control, and its contribution rate was reduced from 22% before the epidemic prevention and control to 1%. The concentrations of industrial sources, solvent sources, motor vehicle tail gas sources, and combustion sources decreased by 97%, 82%, 61%, and 15%, respectively, after the epidemic prevention and control. The concentration of background sources remained stable, and the concentration of oil and gas volatile sources increased by 7%. The control of production and traffic activities cannot reduce the emission of VOCs from oil and gas volatile sources, which is the focus of VOCs control in Xiong'an.


Subject(s)
Air Pollutants , COVID-19 , Ozone , Volatile Organic Compounds , Air Pollutants/analysis , COVID-19/epidemiology , COVID-19/prevention & control , China/epidemiology , Communicable Disease Control , Environmental Monitoring/methods , Humans , Ozone/analysis , SARS-CoV-2 , Vehicle Emissions/analysis , Volatile Organic Compounds/analysis
4.
EuropePMC;
Preprint in English | EuropePMC | ID: ppcovidwho-327697

ABSTRACT

SARS-CoV-2 Omicron variant of concern (VOC) contains fifteen mutations on the receptor binding domain (RBD), evading most neutralizing antibodies from vaccinated sera. Emerging evidence suggests that Omicron breakthrough cases are associated with substantially lower antibody titers than other VOC cases. However, the mechanism remains unclear. Here, using a novel geometric deep-learning model, we discovered that the antigenic profile of Omicron RBD is distinct from the prior VOCs, featuring reduced antigenicity in its remodeled receptor binding sites (RBS). To substantiate our deep-learning prediction, we immunized mice with different recombinant RBD variants and found that the Omicron's extensive mutations can lead to a drastically attenuated serologic response with limited neutralizing activity in vivo, while the T cell response remains potent. Analyses of serum cross-reactivity and competitive ELISA with epitope-specific nanobodies revealed that the antibody response to Omicron was reduced across RBD epitopes, including both the variable RBS and epitopes without any known VOC mutations. Moreover, computational modeling confirmed that the RBS is highly versatile with a capacity to further decrease antigenicity while retaining efficient receptor binding. Longitudinal analysis showed that this evolutionary trend of decrease in antigenicity was also found in hCoV229E, a common cold coronavirus that has been circulating in humans for decades. Thus, our study provided unprecedented insights into the reduced antibody titers associated with Omicron infection, revealed a possible trajectory of future viral evolution and may inform the vaccine development against future outbreaks.

5.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-322749

ABSTRACT

Since the outbreak of COVID-19 in China at the end of 2019, the world has experienced a large-scale epidemic caused by the SARS-CoV-2. Epidemiological and clinical course of COVID-19 patients have been reported, but there have been few analyses about the characteristics, predictive risk factors and outcomes of critical patients. In this single-center retrospective case-control study, 90 adult inpatients hospitalized at Tongji Hospital (Wuhan, China) were included. Demographic, clinical, laboratory test and treatment data were obtained and compared between critical and non-critical patients. We found that c ompared with non-critical patients, the critical patients had higher SOFA score and qSOFA scores. Critical patients had lower lymphocyte and platelet count, elevated D-dimer, decreased fibrinogen, and elevated high-sensitivity C-reactive protein (hsCRP) and interleukin-6(IL-6). More critical patients received treatment including antibiotics, anticoagulation, corticosteroid and oxygen therapy than non-critical ones. Multivariable regression showed higher qSOFA score and elevation of IL-6 were related to critical patients. Antibiotic usage and anticoagulation were associated with decreased in-hospital mortality. And critical grouping contributed greatly to in-hospital death. Critical COVID-19 patients have a more severe clinical cours. qSOFA score and elevation of IL-6 are risk factors for critical condition. Non-critical grouping, positive antibiotic application and anticoagulation may be beneficial for patient survival.

6.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-308170

ABSTRACT

The pandemic of COVID-19 has caused millions of infections, which has led to a great loss all over the world, socially and economically. Due to the false-negative rate and the time-consuming of the conventional Reverse Transcription Polymerase Chain Reaction (RT-PCR) tests, diagnosing based on X-ray images and Computed Tomography (CT) images has been widely adopted. Therefore, researchers of the computer vision area have developed many automatic diagnosing models based on machine learning or deep learning to assist the radiologists and improve the diagnosing accuracy. In this paper, we present a review of these recently emerging automatic diagnosing models. 70 models proposed from February 14, 2020, to July 21, 2020, are involved. We analyzed the models from the perspective of preprocessing, feature extraction, classification, and evaluation. Based on the limitation of existing models, we pointed out that domain adaption in transfer learning and interpretability promotion would be the possible future directions.

7.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-315570

ABSTRACT

With the COVID-19 epidemic quickly under control in China within the early months of 2020, importing the SARS-CoV-2 virus to the country now poses great challenges in epidemic control and prevention. Asymptomatic carriers play a critical role in the transmission of the virus and transmission on a large-scale poses enormous concern. We obtained data from new cluster outbreak regions with COVID-19 caused by asymptomatic carriers from June 2020 to January 2021, and reported the epidemiological characteristics, clinical data and the possible routes of viral transmission and infection. These results indicate the importance of regularly screening high-risk populations critical for epidemic control and provide the basis for suppressing the spread of the SARS-CoV-2 virus.

8.
Remote Sensing ; 14(3):559, 2022.
Article in English | MDPI | ID: covidwho-1650589

ABSTRACT

Population growth, climate change, and the worldwide COVID-19 pandemic are imposing increasing pressure on global agricultural production. The challenge of increasing crop yield while ensuring sustainable development of environmentally friendly agriculture is a common issue throughout the world. Autonomous systems, sensing technologies, and artificial intelligence offer great opportunities to tackle this issue. In precision agriculture (PA), non-destructive and non-invasive remote and proximal sensing methods have been widely used to observe crops in visible and invisible spectra. Nowadays, the integration of high-performance imagery sensors (e.g., RGB, multispectral, hyperspectral, thermal, and SAR) and unmanned mobile platforms (e.g., satellites, UAVs, and terrestrial agricultural robots) are yielding a huge number of high-resolution farmland images, in which rich crop information is compressed. However, this has been accompanied by challenges, i.e., ways to swiftly and efficiently making full use of these images, and then, to perform fine crop management based on information-supported decision making. In the past few years, deep learning (DL) has shown great potential to reshape many industries because of its powerful capabilities of feature learning from massive datasets, and the agriculture industry is no exception. More and more agricultural scientists are paying attention to applications of deep learning in image-based farmland observations, such as land mapping, crop classification, biotic/abiotic stress monitoring, and yield prediction. To provide an update on these studies, we conducted a comprehensive investigation with a special emphasis on deep learning in multiscale agricultural remote and proximal sensing. Specifically, the applications of convolutional neural network-based supervised learning (CNN-SL), transfer learning (TL), and few-shot learning (FSL) in crop sensing at land, field, canopy, and leaf scales are the focus of this review. We hope that this work can act as a reference for the global agricultural community regarding DL in PA and can inspire deeper and broader research to promote the evolution of modern agriculture.

9.
Geophysical Research Letters ; n/a(n/a):e2021GL096842, 2022.
Article in English | Wiley | ID: covidwho-1616960

ABSTRACT

The significant reduction in human activities during COVID-19 lockdown is anticipated to substantially influence urban climates, especially urban heat islands (UHIs). However, the UHIs variations during lockdown periods remain to be quantified. Based on the MODIS daily land surface temperature and the in-situ surface air temperature observations, we reveal a substantial decline in both surface and canopy UHIs over 300-plus megacities in China during lockdown periods compared with reference periods. The surface UHI intensity (UHII) is reduced by 0.25 (one S.D. = 0.22) K in the daytime and by 0.23 (0.20) K at night during lockdown periods. The reductions in canopy UHII reach 0.42 (one S.D. = 0.26) K in the daytime and 0.39 (0.29) K at night. These reductions are mainly due to the near-unprecedented drop in human activities induced by strict lockdown measures. Our results provide an improved understanding of the urban climate variations during the global pandemic.

10.
Displays ; : 102144, 2021.
Article in English | ScienceDirect | ID: covidwho-1587952

ABSTRACT

Radiomics based on lesion segmentation has been widely accepted for disease diagnosis;however, it is difficult to precisely determine the boundary for pneumonia due to its diffuse characteristics. In this study, we aimed to propose an automatic radiomics method using whole-lung segmentation in pneumonia discrimination and assist clinical practitioners in fast and accurate diagnosis. In the discovery set, data from 151 participants diagnosed with type A or B influenza virus pneumonia, 63 diagnosed with coronavirus disease 2019 (COVID-19) and 50 healthy participants were collected. The three groups of data were compared in pairs. A total of 117 radiomics features were extracted from whole-lung images segmented by a four-layer U-net. We then utilized a logistic regression model to train the model and used the area under the receiver operating characteristic curve (AUC) to assess its performance. The L1 regularization term was used in feature selection, and 10-fold cross-validation was used to tune the hyperparameters. Fourteen radiomics features were selected to classify influenza pneumonia and health, and the AUC was 0.957 (95% confidential interval (CI): 0.939, 0.976) in the training set and 0.914 (95% CI: 0.866, 0.963) in the testing set. Eighteen features were selected for COVID-19 and health, and the AUC was 0.949 (95% CI: 0.926, 0.973)in the training set and 0.911 (95% CI: 0.859, 0.963) in the testing set. Twenty-eight features were selected for influenza virus pneumonia and COVID-19, and the AUC was 0.895 (95% CI: 0.870, 0.920) in the training set and 0.839 (95% CI: 0.791, 0.887) in the testing set. The results show that the automatic radiomics model based on whole lung segmentation is effective in distinguishing influenza virus pneumonia, COVID-19 and health, and may assist in the diagnosis of influenza virus pneumonia and COVID-19.

11.
2021.
Preprint in English | Other preprints | ID: ppcovidwho-294445

ABSTRACT

The outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) issued a significant and urgent threat to global health. The exact animal origin of SARS-CoV-2 remains obscure and understanding its host range is vital for preventing interspecies transmission. Previously, we have assessed the target cell profiles of SARS-CoV-2 in pets, livestock, poultry and wild animals. Herein, we expand this investigation to a wider range of animal species and viruses to provide a comprehensive source for large-scale screening of potential virus hosts. Single cell atlas for several mammalian species (alpaca, hamster, hedgehog, chinchilla etc.), as well as comparative atlas for lung, brain and peripheral blood mononuclear cells (PBMC) for various lineages of animals were constructed, from which we systemically analyzed the virus entry factors for 113 viruses over 20 species from mammalians, birds, reptiles, amphibians and invertebrates. Conserved cellular connectomes and regulomes were also identified, revealing the fundamental cell-cell and gene-gene cross-talks between these species. Overall, our study could help identify the potential host range and tissue tropism of SARS-CoV-2 and a diverse set of viruses and reveal the host-virus co-evolution footprints.

12.
Sustainability ; 13(22):12789, 2021.
Article in English | ProQuest Central | ID: covidwho-1538505

ABSTRACT

To accurately predict the economic development of each industry in different types of regions, a deep convolutional neural network model was designed for predicting the annual GDP;GDP growth index;and primary, secondary and tertiary industry growth values of each. In the model, raw industrial data are preprocessed by a normalization operation and subsequently transformed by the BoxCox method to approach the normal distribution. Panel data of consecutive years are constructed and used as input to the deep convolutional neural network, and industrial data of year t + 1 are used as the output of the network. Simulation experiments were conducted to analyze 23 years of industrial economic data from 31 provinces, municipalities, and autonomous regions in China. The experimental results show that R-squared value is larger than 0.91 for all 31 provinces and root mean squared log errors (RMSLE) of all regions are less than 0.1, which demonstrate that the proposed method achieves high prediction accuracy with generalization capability and can accurately predict the economic growth trends of different types of regions.

13.
Front Med (Lausanne) ; 8: 736060, 2021.
Article in English | MEDLINE | ID: covidwho-1518495

ABSTRACT

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has wreaked havoc on millions of people around the world. Although China quickly brought the Coronavirus disease (COVID-19) under control, there have been several sporadic outbreaks in different regions of China since June 2020. This article described the chronological nosocomial COVID-19 infection events related to several sporadic outbreaks of SARS-CoV-2 in different regions of China. We have reported epidemiological characteristics and management measures of sporadic nosocomial COVID-19 infections from June 2020 to June 2021 and specially focused on the domestic COVID-19 breakthrough infection in China, such as domestic COVID-19 breakthrough infection-a vaccinated healthcare professional working in the isolation ward of a designated COVID-19 hospital.

14.
Preprint in English | medRxiv | ID: ppmedrxiv-21259671

ABSTRACT

Current tests for SARS-CoV-2 antibodies (IgG, IgM, IgA) cannot differentiate recent and past infections. We describe a point of care, lateral flow assay for SARS-CoV-2 dIgA based on the highly selective binding of dIgA to a chimeric form of secretory component (CSC), that distinguishes dIgA from monomeric IgA. Detection of specific dIgA uses a complex of biotinylated SARS-CoV-2 receptor binding domain and streptavidin-colloidal gold. SARS-CoV-2-specific dIgA was measured both in 112 cross-sectional samples and a longitudinal panel of 362 plasma samples from 45 patients with PCR-confirmed SARS-CoV-2 infection, and 193 discrete pre-COVID-19 or PCR-negative patient samples. The assay demonstrated 100% sensitivity from 11 days post-symptom onset, and a specificity of 98.2%. With an estimated half-life of 6.3 days, dIgA provides a unique biomarker for the detection of recent SARS-CoV-2 infections with potential to enhance diagnosis and management of COVID-19 at point-of-care.

15.
Remote Sensing ; 12(10):1613, 2020.
Article in English | ProQuest Central | ID: covidwho-1268340

ABSTRACT

The outbreak of the COVID-19 virus in Wuhan, China, in January 2020 just before the Spring Festival and subsequent country-wide measures to contain the virus, effectively resulted in the lock-down of the country. Most industries and businesses were closed, traffic was largely reduced, and people were restrained to their homes. This resulted in the reduction of emissions of trace gases and aerosols, the concentrations of which were strongly reduced in many cities around the country. Satellite imagery from the TROPOspheric Monitoring Instrument (TROPOMI) showed an enormous reduction of tropospheric NO2 concentrations, but aerosol optical depth (AOD), as a measure of the amount of aerosols, was less affected, likely due to the different formation mechanisms and the influence of meteorological factors. In this study, satellite data and ground-based observations were used together to estimate the separate effects of the Spring Festival and the COVID-19 containment measures on atmospheric composition in the winter of 2020. To achieve this, data were analyzed for a period from 30 days before to 60 days after the Spring Festivals in 2017–2020. This extended period of time, including similar periods in previous years, were selected to account for both the decreasing concentrations in response to air pollution control measures, and meteorological effects on concentrations of trace gases and aerosols. Satellite data from TROPOMI provided the spatial distributions over mainland China of the tropospheric vertical column density (VCD) of NO2, and VCD of SO2 and CO. The MODerate resolution Imaging Spectroradiometer (MODIS) provided the aerosol optical depth (AOD). The comparison of the satellite data for different periods showed a large reduction of, e.g., NO2 tropospheric VCDs due to the Spring Festival of up to 80% in some regions, and an additional reduction due to the COVID-19 containment measures of up to 70% in highly populated areas with intensive anthropogenic activities. In other areas, both effects are very small. Ground-based in situ observations from 26 provincial capitals provided concentrations of NO2, SO2, CO, O3, PM2.5, and PM10. The analysis of these data was focused on the situation in Wuhan, based on daily averaged concentrations. The NO2 concentrations started to decrease a few days before the Spring Festival and increased after about two weeks, except in 2020 when they continued to be low. SO2 concentrations behaved in a similar way, whereas CO, PM2.5, and PM10 also decreased during the Spring Festival but did not trace NO2 concentrations as SO2 did. As could be expected from atmospheric chemistry considerations, O3 concentrations increased. The analysis of the effects of the Spring Festival and the COVID-19 containment measures was complicated due to meteorological influences. Uncertainties contributing to the estimates of the different effects on the trace gas concentrations are discussed. The situation in Wuhan is compared with that in 26 provincial capitals based on 30-day averages for four years, showing different effects across China.

16.
Atmospheric Chemistry and Physics ; 21(10):7723-7748, 2021.
Article in English | ProQuest Central | ID: covidwho-1239092

ABSTRACT

The variation of NO2 concentrations in mainland China is analyzed on different timescales, from decadal to weekly, using both satellite data and data from ground-based monitoring networks. TROPOMI (TROPOspheric Monitoring Instrument) data were used to study the spatial variations of tropospheric NO2 vertical column densities (TVCDs) over the study area during 16–20 weeks after the Chinese Spring Festival (25 January 2020). These data were used to select 11 regions for more detailed analysis of the variation of NO2 TVCDs on a decadal timescale. In this analysis, monthly and annual averaged NO2 TVCDs derived from OMI (Ozone Monitoring Instrument) observations were used for the years 2011 to 2019. The results show the NO2 TVCD trends for different regions, all decreasing in response to emission reduction policies but with a different onset and a possible halt of the decrease in recent years;trends and period in the south of the study area are different from those in the north. Variations of NO2 TVCDs on shorter timescales, monthly and weekly, were analyzed using TROPOMI data. In addition, the variations of weekly-averaged ground-based NO2 concentrations in 11 major cities were analyzed together with those for O3 and PM2.5. In particular these data were used to determine their effect on the air quality as expressed by the air quality index (AQI). For quantitative estimates, the use of weekly concentrations is more accurate than the use of monthly values, and the effects of long-term trends and their reversal needs to be taken into account for the separation of effects of the lockdown and the Spring Festival. Neglecting the possible reversal of the trends leads to overestimation of the lockdown effect in the south and underestimation in the north. The ground-based data confirm earlier reports, based on satellite observations, that the expected improvement of air quality due to the reduction of NO2 concentrations was offset by the increase of the concentrations of O3 and the different effects of the lockdown measures on PM2.5, as well as effects of meteorological influences and heterogeneous chemistry. The AQI seems to be mostly influenced by PM2.5 rather than NO2. A qualitative comparison between time series of satellite and ground-based NO2 observations shows both similarities and differences. The study further shows the different behaviors in city clusters in the north and south of China, as well as inland in the Sichuan and Guanzhong basins. Effects of other holidays and events are small, except in Beijing where the air quality in 2020 was notably better than in previous years. This study was undertaken for China, but the methodology and results have consequences for air quality studies in other areas, and part of the conclusions are generally applicable.

17.
Preprint in English | medRxiv | ID: ppmedrxiv-21255368

ABSTRACT

As vaccines against SARS-CoV-2 are now being rolled out, a better understanding of immunity to the virus; whether through infection, or passive or active immunisation, and the durability of this protection is required. This will benefit from the ability to measure SARS-CoV-2 immunity, ideally with rapid turnaround and without the need for laboratory-based testing. Current rapid point-of-care (POC) tests measure antibodies (Ab) against the SARS-CoV-2 virus, however, these tests provide no information on whether the antibodies can neutralise virus infectivity and are potentially protective, especially against newly emerging variants of the virus. Neutralising Antibodies (NAb) are emerging as a strong correlate of protection, but most current NAb assays require many hours or days, samples of venous blood, and access to laboratory facilities, which is especially problematic in resource-limited settings. We have developed a lateral flow POC test that can measure levels of RBD-ACE2 neutralising antibodies from whole blood, with a result that can be determined by eye (semi-quantitative) or on a small instrument (quantitative), and results show high correlation with microneutralisation assays. This assay also provides a measure of total anti-RBD antibody, thereby providing evidence of exposure to SARS-CoV-2, regardless of whether NAb are present in the sample. By testing samples from immunised macaques, we demonstrate that this test is equally applicable for use with animal samples, and we show that this assay is readily adaptable to test for immunity to newly emerging SARS-CoV-2 variants. Accordingly, the COVID-19 NAb-test test described here can provide a rapid readout of immunity to SARS-CoV-2 at the point of care.

18.
Preprint in English | medRxiv | ID: ppmedrxiv-21253850

ABSTRACT

BackgroundThe significant morbidity and mortality resulted from the infection of a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) call for urgent development of effective and safe vaccines. We report the immunogenicity and safety of a SARS-CoV-2 inactivated vaccine, KCONVAC, in healthy adults. MethodsTwo phase 1 and phase 2 randomized, double-blind, and placebo-controlled trials of KCONVAC were conducted in Chinese healthy adults aged 18 through 59 years. The phase 1 trial was conducted in a manner of dosage escalation. The first 30 participants were randomized in a ratio of 4:1 to receive two doses of either KCONVAC at 5 g per dose or placebo on Day 0 and Day 14, and the second 30 participants were randomized to receive either KCONVAC at 10 g per dose or placebo following the same procedures. The participants in the phase 2 trial were randomized in a ratio of 2:2:1 to receive either KCONVAC at 5 g or 10 g per dose, or placebo on Day 0 and Day 14, or Day 0 and Day 28. In the phase 1 trial, the primary safety endpoint was the proportion of participants experiencing adverse reactions/events within 28 days following each vaccination. Antibody response and cellular response were assayed in the phase 1 trial. In the phase 2 trial, the primary immunogenicity endpoint was the seroconversion and titre of neutralization antibody, and the seroconversion of receptor binding domain (RBD)-IgG 28 days after the second dose. FindingsIn the phase 1 trial, 60 participants were enrolled and received at least one dose of 5-g vaccine (N=24), 10-g vaccine (N=24), or placebo (N=12). In the phase 2 trial, 500 participants were enrolled and received at least one dose of 5-g vaccine (N=100 for 0/14 or 0/28 regimens), 10-g vaccine (N=100 for each regimen), or placebo (N=50 for each regimen). In the phase 1 trial, 13 (54%), 11(46%), and 7 (58%) participants reported at least one adverse event (AE), of whom 10 (42%), 6 (25%), and 6 (50%) participants reported at least one vaccination-related AE after receiving 5-g vaccine, 10-g vaccine, or placebo, respectively. In the phase 2 trial, 16 (16%), 19 (19%), and 9 (18%) participants reported at least one AE, of whom 13 (13%), 17 (17%), and 6 (12%) participants reported at least one vaccination-related AE after receiving 5-g vaccine, 10-g vaccine, or placebo at the regimen of Day 0/14, respectively. Similar results were observed in the three treatment groups of Day 0/28 regimen. All the AEs were grade 1 or 2 in intensity. No AE of grade 3 or more was reported. One SAE (foot fracture) was reported in the phase 1 trial. KCONVAC induced significant antibody response. 87{middle dot}5% (21/24) to 100% (24/24) of participants in the phase 1 trial and 83{middle dot}0% (83/100) to 100% (99/99) of participants in the phase 2 trial seroconverted for neutralising antibody to live virus, neutralising antibody to pseudovirus, and RBD-IgG after receiving two doses. Across the treatment groups in the two trials, the geometric mean titres (GMTs) of neutralising antibody to live virus ranged from 29{middle dot}3 to 49{middle dot}1 at Day 0/14 regimen and from 100{middle dot}2 to 131{middle dot}7 at Day 0/28 regimen, neutralising antibody to pseudovirus ranged from 69{middle dot}4 to 118{middle dot}7 at Day 0/14 regimen and from 153{middle dot}6 to 276{middle dot}6 at Day 0/28 regimen, and RBD-IgG ranged from 605{middle dot}3 to 1169{middle dot}8 at Day 0/14 regimen and from 1496{middle dot}8 to 2485{middle dot}5 at Day 0/28 regimen. RBD-IgG subtyping assay showed that a significant part of RBD-IgG was IgG1. The vaccine induced obvious T-cell response with 56{middle dot}5% (13/23) and 62{middle dot}5% (15/24) of participants in 5-g and 10-g vaccine groups showed positive interferon-{gamma} enzyme-linked immunospot responses 14 days after the second dose in the phase 1 trial, respectively. InterpretationKCONVAC is well tolerated and able to induce robust antibody response and cellular response in adults aged 18 to 59 years, which warrants further evaluation with this vaccine in the upcoming phase 3 efficacy trial. FundingGuandong Emergency Program for Prevention and Control of COVID-19 (2020A1111340002) and Shenzhen Key Research Project for Prevention and Control of COVID-19.

19.
Chin J Acad Radiol ; 4(4): 241-247, 2021.
Article in English | MEDLINE | ID: covidwho-1107922

ABSTRACT

PURPOSE: To analyze the initial CT features of different clinical categories of COVID-19. MATERIAL AND METHODS: A total of 86 patients with COVID-19 were analyzed, including the clinical, laboratory and imaging features. The following imaging features were analyzed, the lesion amount, location, density, lung nodule, halo sign, reversed-halo sign, distribution pattern, inner structures and changes of adjacent structures. Chi-square test, Fisher's exact test, or Mann-Whitney U test was used for the enumeration data. Binary logistic regression analysis was performed to draw a regression equation to estimate the likelihood of severe and critical category. The forward conditional method was employed for variable selection. RESULTS: Significant statistical differences were found in age (p = 0.001) and sex (p = 0.028) between mild and moderate and severe and critical category. No significant difference was found in clinical symptoms and WBC count between the two groups. The majority of cases (91.8%) showed multifocal lesions. The presence of GGO was higher in severe and critical category than in the mild and moderate category. (57.8% vs.31.7%, p = 0.015). Lymphocyte count was important indicator for the severe and critical category. CONCLUSION: The initial CT features of the different clinical category overlapped. Combining with laboratory test, especially the lymphocyte count, could help to predict the severity of COVID-19. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s42058-021-00056-4.

20.
Acta Radiol ; 63(3): 291-310, 2022 Mar.
Article in English | MEDLINE | ID: covidwho-1105634

ABSTRACT

Quick screening patients with COVID-19 is the most important way of controlling transmission by isolation and medical treatment. Chest computed tomography (CT) has been widely used during the initial screening process, including pneumonia diagnosis, severity assessment, and differential diagnosis of COVID-19. The course of COVID-19 changes rapidly. Serial CT imaging could observe the distribution, density, and range of lesions dynamically, monitor the changes, and then guide towards appropriate treatment. The aim of the review was to explore the chest CT findings and dynamic CT changes of COVID-19 using systematic evaluation methods, instructing the clinical imaging diagnosis. A systematic literature search was performed. The quality of included literature was evaluated with a quality assessment tool, followed by data extraction and meta-analysis. Homogeneity and publishing bias were analyzed. A total of 109 articles were included, involving 2908 adults with COVID-19. The lesions often occurred in bilateral lungs (74%) and were multifocal (77%) with subpleural distribution (81%). Lesions often showed ground-glass opacity (GGO) (68%), followed by GGO with consolidation (48%). The thickening of small vessels (70%) and thickening of intralobular septum (53%) were also common. The dynamic changes of chest CT manifestations showed that lesions were absorbed and improved gradually after reaching the peak (80%), had progressive deterioration (55%), were absorbed and improved gradually (46%), fluctuated (22%), or remained stable (26%). The review showed the common and key CT features and the dynamic imaging change patterns of COVID-19, helping with timely management during COVID-19 pandemic.


Subject(s)
COVID-19/diagnostic imaging , Lung/diagnostic imaging , Tomography, X-Ray Computed , Adolescent , Adult , Aged , Aged, 80 and over , COVID-19/therapy , Confidence Intervals , Diagnosis, Differential , Disease Progression , Female , Humans , Male , Middle Aged , Publication Bias , Young Adult
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