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1.
J Biomed Sci ; 29(1): 49, 2022 Jul 07.
Article in English | MEDLINE | ID: covidwho-1923546

ABSTRACT

BACKGROUND: With the continuous emergence of new SARS-CoV-2 variants that feature increased transmission and immune escape, there is an urgent demand for a better vaccine design that will provide broader neutralizing efficacy. METHODS: We report an mRNA-based vaccine using an engineered "hybrid" receptor binding domain (RBD) that contains all 16 point-mutations shown in the currently prevailing Omicron and Delta variants. RESULTS: A booster dose of hybrid vaccine in mice previously immunized with wild-type RBD vaccine induced high titers of broadly neutralizing antibodies against all tested SARS-CoV-2 variants of concern (VOCs). In naïve mice, hybrid vaccine generated strong Omicron-specific neutralizing antibodies as well as low but significant titers against other VOCs. Hybrid vaccine also elicited CD8+/IFN-γ+ T cell responses against a conserved T cell epitope present in wild type and all VOCs. CONCLUSIONS: These results demonstrate that inclusion of different antigenic mutations from various SARS-CoV-2 variants is a feasible approach to develop cross-protective vaccines.


Subject(s)
COVID-19 , SARS-CoV-2 , Animals , Antibodies, Neutralizing , Antibodies, Viral , Broadly Neutralizing Antibodies , COVID-19/prevention & control , Humans , Mice , SARS-CoV-2/genetics , Vaccines, Synthetic , mRNA Vaccines
2.
Machine Learning : Science and Technology ; 3(3):035001, 2022.
Article in English | ProQuest Central | ID: covidwho-1922163

ABSTRACT

Neural network training and validation rely on the availability of large high-quality datasets. However, in many cases only incomplete datasets are available, particularly in health care applications, where each patient typically undergoes different clinical procedures or can drop out of a study. Since the data to train the neural networks need to be complete, most studies discard the incomplete datapoints, which reduces the size of the training data, or impute the missing features, which can lead to artifacts. Alas, both approaches are inadequate when a large portion of the data is missing. Here, we introduce GapNet, an alternative deep-learning training approach that can use highly incomplete datasets without overfitting or introducing artefacts. First, the dataset is split into subsets of samples containing all values for a certain cluster of features. Then, these subsets are used to train individual neural networks. Finally, this ensemble of neural networks is combined into a single neural network whose training is fine-tuned using all complete datapoints. Using two highly incomplete real-world medical datasets, we show that GapNet improves the identification of patients with underlying Alzheimer’s disease pathology and of patients at risk of hospitalization due to Covid-19. Compared to commonly used imputation methods, this improvement suggests that GapNet can become a general tool to handle incomplete medical datasets.

3.
Ann Transl Med ; 10(10): 574, 2022 May.
Article in English | MEDLINE | ID: covidwho-1887396

ABSTRACT

Background: Little is known about the change in characteristics of fever-clinic visits during the coronavirus disease 2019 (COVID-19) pandemic. We sought to examine the changes in the volume, characteristics, and outcomes of patients presenting at a fever clinic duringclinic during the first-level response to COVID-19. Methods: We conducted a single tertiary-center retrospective case-control study. We included consecutive patients aged 14 years or older who visited the fever clinic of a tertiary hospital during the period of the first-level response to the COVID-19 pandemic in Fuzhou, China (from 24 January to 26 February 2020). We also analyzed the data of patients in the same period of 2019 as a control. We compared a number of outcome measures, including the fever clinic volumes, consultation length, proportion of patients with pneumonia, hospital admission rate, and in-hospital mortality, using the fever-clinic visit data during the two periods. Results: We included 1,013 participants [median age: 35; interquartile range (IQR): 27-50, 48.7% male] in this retrospective study, including 707 in 2020 and 306 in 2019. The median daily number of participants who presented at the fever clinic in 2020 was significantly higher than that in 2019 [18 (IQR: 15-22) vs. 13 (IQR: 8-17), P=0.001]. Participants in 2020 had a longer consultation length than those in 2019 [127 (IQR: 51-204) vs. 20 (IQR: 1-60) min, P<0.001]. Participants in 2020 were also more likely to be diagnosed with acute pneumonia than those in 2019 [168 (23.8%) vs. 40 (13.1%), P<0.001]. The hospital admission rate in 2020 was higher than in 2019 [73 (10.3%) vs. 13 (4.2%), P=0.001]. No significant difference was found in terms of the in-hospital mortality of participants in 2020 and 2019 [8 (1.1%) vs. 0, P=0.114]. Conclusions: Our findings suggest a higher visits volume, proportion of acute pneumonia, and hospital admission rate among patients presenting at fever clinic during the COVID-19 pandemic. Improved measures need to be implemented.

4.
Preprint in English | medRxiv | ID: ppmedrxiv-22276209

ABSTRACT

BackgroundA well-known blood biomarker (soluble fms-like tyrosinase-1 [sFLT-1]) for preeclampsia, i.e., a pregnancy disorder, was found to predict severe COVID-19, including in males. True biomarker may be masked by more-abrupt changes related to endothelial instead of placental dysfunction. This study aimed to identify blood biomarkers that represent maternal-fetal interface tissues for predicting preeclampsia but not COVID-19 infection. MethodsThe surrogate transcriptome of the tissues was determined by that in maternal blood, utilizing four datasets (n=1,354) which were collected before the COVID-19 pandemic. Applying machine learning, a preeclampsia prediction model was chosen between those using blood transcriptome (differentially expressed genes [DEGs]) and the blood-derived surrogate for the tissues. We selected the most predictive model by the area under receiver operating characteristic (AUROC) using a dataset for developing the model, and well-replicated in datasets either with or without intervention. To identify eligible blood biomarkers that predicted any-onset preeclampsia from the datasets but did not predict positives in the COVID-19 dataset (n=47), we compared several methods of predictor discovery: (1) the best prediction model; (2) gene sets by standard pipelines; and (3) a validated gene set for predicting any-onset preeclampsia during the pandemic (n=404). We chose the most predictive biomarkers from the best method with the significantly largest number of discoveries by a permutation test. The biological relevance was justified by exploring and reanalyzing low- and high-level, multi-omics information. ResultsA prediction model using the surrogates developed for predicting any-onset preeclampsia (AUROC of 0.85, 95% confidence interval [CI] 0.77 to 0.93) was the only that was well-replicated in an independent dataset with no intervention. No model was well-replicated in datasets with a vitamin D intervention. None of the blood biomarkers with high weights in the best model overlapped with blood DEGs. Blood biomarkers were transcripts of integrin-5 (ITGA5), interferon regulatory factor-6 (IRF6), and P2X purinoreceptor-7 (P2RX7) from the prediction model, which was the only method that significantly discovered the eligible blood biomarkers (n=3/100 combinations, 3.0%; P=.036). Most of the predicted events (73.70%) among any-onset preeclampsia were cluster A as defined by ITGA5 (Z-score [≥]1.1), but were only a minority (6.34%) among positives in the COVID-19 dataset. The remaining were the predicted events (26.30%) among any-onset preeclampsia or those among COVID-19 infection (93.66%) if IRF6 Z-score was [≥]-0.73 (clusters B and C), in which none was the predicted events among either late-onset preeclampsia (LOPE) or COVID-19 infection if P2RX7 Z-score was <0.13 (cluster B). Greater proportion of predicted events among LOPE were cluster A (82.85% vs. 70.53%) compared to early-onset preeclampsia (EOPE). The biological relevance by multi-omics information explained the biomarker mechanism, polymicrobial infection in any-onset preeclampsia by ITGA5, viral co-infection in EOPE by ITGA5-IRF6, a shared prediction with COVID-19 infection by ITGA5-IRF6-P2RX7, and non-replicability in datasets with a vitamin D intervention by ITGA5. ConclusionsIn a model that predicts preeclampsia but not COVID-19 infection, the important predictors were maternal-blood genes that were not extremely expressed, including the proposed blood biomarkers. The predictive performance and biological relevance should be validated in future experiments.

5.
Front Immunol ; 13: 868724, 2022.
Article in English | MEDLINE | ID: covidwho-1862608

ABSTRACT

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an emerging virus responsible for the ongoing COVID-19 pandemic. SARS-CoV-2 binds to the human cell receptor angiotensin-converting enzyme 2 (ACE2) through its receptor-binding domain in the S1 subunit of the spike protein (S1-RBD). The serum levels of autoantibodies against ACE2 are significantly higher in patients with COVID-19 than in controls and are associated with disease severity. However, the mechanisms through which these anti-ACE2 antibodies are induced during SARS-CoV-2 infection are unclear. In this study, we confirmed the increase in antibodies against ACE2 in patients with COVID-19 and found a positive correlation between the amounts of antibodies against ACE2 and S1-RBD. Moreover, antibody binding to ACE2 was significantly decreased in the sera of some COVID-19 patients after preadsorption of the sera with S1-RBD, which indicated that antibodies against S1-RBD can cross-react with ACE2. To confirm this possibility, two monoclonal antibodies (mAbs 127 and 150) which could bind to both S1-RBD and ACE2 were isolated from S1-RBD-immunized mice. Measurement of the binding affinities by Biacore showed these two mAbs bind to ACE2 much weaker than binding to S1-RBD. Epitope mapping using synthetic overlapping peptides and hydrogen deuterium exchange mass spectrometry (HDX-MS) revealed that the amino acid residues P463, F464, E465, R466, D467 and E471 of S1-RBD are critical for the recognition by mAbs 127 and 150. In addition, Western blotting analysis showed that these mAbs could recognize ACE2 only in native but not denatured form, indicating the ACE2 epitopes recognized by these mAbs were conformation-dependent. The protein-protein interaction between ACE2 and the higher affinity mAb 127 was analyzed by HDX-MS and visualized by negative-stain transmission electron microscopy imaging combined with antigen-antibody docking. Together, our results suggest that ACE2-cross-reactive anti-S1-RBD antibodies can be induced during SARS-CoV-2 infection due to potential antigenic cross-reactivity between S1-RBD and its receptor ACE2.


Subject(s)
Angiotensin-Converting Enzyme 2 , COVID-19 , Animals , Antibodies, Monoclonal , Antibodies, Viral , Humans , Mice , Pandemics , SARS-CoV-2 , Spike Glycoprotein, Coronavirus
6.
EuropePMC; 2022.
Preprint in English | EuropePMC | ID: ppcovidwho-336878

ABSTRACT

The global emergence of SARS-CoV-2 variants has led to increasing breakthrough infections in vaccinated populations, calling for an urgent need to develop more effective and broad-spectrum vaccines to combat COVID-19. Here we report the preclinical development of RQ3013, an mRNA vaccine candidate intended to bring broad protection against SARS-CoV-2 variants of concern (VOCs). RQ3013, which contains pseudouridine-modified mRNAs formulated in lipid nanoparticles, encodes the spike(S) protein harboring a combination of mutations responsible for immune evasion of VOCs. Here we characterized the expressed S immunogen and evaluated the immunogenicity, efficacy, and safety of RQ3013 in various animal models. RQ3013 elicited robust immune responses in mice, hamsters, and nonhuman primates (NHP). It can induce high titers of antibodies with broad cross-neutralizing ability against the Wild-type, B.1.1.7, B.1.351, B.1.617.2, and the omicron B.1.1.529 variants. In mice and NHP, two doses of RQ3013 protected the upper and lower respiratory tract against infection by SARS-CoV-2 and its variants. We also proved the safety of RQ3013 in NHP models. Our results provided key support for the evaluation of RQ3013 in clinical trials.

7.
EuropePMC; 2022.
Preprint in English | EuropePMC | ID: ppcovidwho-335448

ABSTRACT

Background: COVID-19, the highly contagious respiratory disease, has become a major threat to humanity, and its extrapulmonary effects were also evident. Heart failure (HF) may be the result of myocardial damage associated with COVID-19. Methods: : To understand the relationship between SARS-COV-2 and HF, we used bioinformatics analysis to identify common pathways and molecular biomarkers for HF and COVID-19. In this study, two datasets (GSE152418, GSE57338) from Gene Expression Omnibus (GEO) were used to identify differentially expressed genes (DEGs) of SARS-COV-2 infection in HF patients to find common pathways and drug candidates. Results: : A total of 123 common DEGs were identified in the two datasets. Using a variety of bioinformatics tools, we first constructed protein-protein interactions (PPI) and then identified hub genes that could be served as potential biomarkers or novel therapeutic strategies. In addition, some common associations between HF and the progression of COVID-19 infection were found by using functional under ontological terms and pathway analysis. Through the datasets, we also identified transcription factor-gene interactions, protein-drug interactions, and co-regulatory network of DEGs-miRNAs with common DEGs. We built gene-disease association network to represent diseases associated with mutual DEGs. Conclusions: : Our study has identified the candidate hub genes and drugs that might become a new therapeutic target for novel coronavirus vaccine development and treatment in COVID-19 and HF.

8.
Knowledge-Based Systems ; : 108944, 2022.
Article in English | ScienceDirect | ID: covidwho-1821402

ABSTRACT

Anomaly subgraph detection is an important problem that has been well researched in various applications, ranging from cyberattacks in computer networks to malicious activities in social networks. Most existing approaches detect anomaly subgraphs in attributed networks with sufficient features. However, multitudinous industry data with insufficient anomalous attributes are the main challenge for traditional anomaly subgraph detection algorithms. In particular, none of the literature focuses on connected anomaly subgraph detection with insufficient anomalous features. To address this problem, we propose Anomaly Alignment in Attributed Networks (AAAN), which first detects connected anomaly subgraphs by aligning anomalies in two attributed networks (one with insufficient anomalous features and the other with sufficient anomalous features). Extensive experiments on three real-world datasets (the Weibo, Baidu migration network, and COVID-19 pandemic datasets) show the effectiveness and efficiency of our algorithm. We can identify the crime hotspots in terms of city blocks from urban traffic networks, which are aligned with the criminal events reported on social networks. The results also demonstrate how AAAN outperforms competitive approaches in the COVID-19 outbreak anomaly subgraph detection and urban crime hotspot detection tasks.

9.
Front Immunol ; 13: 832394, 2022.
Article in English | MEDLINE | ID: covidwho-1809391

ABSTRACT

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in countless infections and caused millions of deaths since its emergence in 2019. Coronavirus disease 2019 (COVID-19)-associated mortality is caused by uncontrolled inflammation, aberrant immune response, cytokine storm, and an imbalanced hyperactive immune system. The cytokine storm further results in multiple organ failure and lung immunopathology. Therefore, any potential treatments should focus on the direct elimination of viral particles, prevention strategies, and mitigation of the imbalanced (hyperactive) immune system. This review focuses on cytokine secretions of innate and adaptive immune responses against COVID-19, including interleukins, interferons, tumor necrosis factor-alpha, and other chemokines. In addition to the review focus, we discuss potential immunotherapeutic approaches based on relevant pathophysiological features, the systemic immune response against SARS-CoV-2, and data from recent clinical trials and experiments on the COVID-19-associated cytokine storm. Prompt use of these cytokines as diagnostic markers and aggressive prevention and management of the cytokine storm can help determine COVID-19-associated morbidity and mortality. The prophylaxis and rapid management of the cytokine storm appear to significantly improve disease outcomes. For these reasons, this study aims to provide advanced information to facilitate innovative strategies to survive in the COVID-19 pandemic.


Subject(s)
COVID-19 , Chemokines , Cytokine Release Syndrome , Cytokines , Humans , Pandemics , SARS-CoV-2
10.
BMC Infect Dis ; 22(1): 402, 2022 Apr 25.
Article in English | MEDLINE | ID: covidwho-1808343

ABSTRACT

The scientific response to the COVID-19 pandemic has produced an abundance of publications, including peer-reviewed articles and preprints, across a wide array of disciplines, from microbiology to medicine and social sciences. Genomics and precision health (GPH) technologies have had a particularly prominent role in medical and public health investigations and response; however, these domains are not simply defined and it is difficult to search for relevant information using traditional strategies. To quantify and track the ongoing contributions of GPH to the COVID-19 response, the Office of Genomics and Precision Public Health at the Centers for Disease Control and Prevention created the COVID-19 Genomics and Precision Health database (COVID-19 GPH), an open access knowledge management system and publications database that is continuously updated through machine learning and manual curation. As of February 11, 2022, COVID-GPH contained 31,597 articles, mostly on pathogen and human genomics (72%). The database also includes articles describing applications of machine learning and artificial intelligence to the investigation and control of COVID-19 (28%). COVID-GPH represents about 10% (22983/221241) of the literature on COVID-19 on PubMed. This unique knowledge management database makes it easier to explore, describe, and track how the pandemic response is accelerating the applications of genomics and precision health technologies. COVID-19 GPH can be freely accessed via https://phgkb.cdc.gov/PHGKB/coVInfoStartPage.action .


Subject(s)
COVID-19 , Artificial Intelligence , COVID-19/epidemiology , Genomics , Humans , Pandemics , Precision Medicine , SARS-CoV-2/genetics
11.
Biosens Bioelectron ; 209: 114226, 2022 Aug 01.
Article in English | MEDLINE | ID: covidwho-1767929

ABSTRACT

Protein sensors based on allosteric enzymes responding to target binding with rapid changes in enzymatic activity are potential tools for homogeneous assays. However, a high signal-to-noise ratio (S/N) is difficult to achieve in their construction. A high S/N is critical to discriminate signals from the background, a phenomenon that might largely vary among serum samples from different individuals. Herein, based on the modularized luciferase NanoLuc, we designed a novel biosensor called NanoSwitch. This sensor allows direct detection of antibodies in 1 µl serum in 45 min without washing steps. In the detection of Flag and HA antibodies, NanoSwitches respond to antibodies with S/N ratios of 33-fold and 42-fold, respectively. Further, we constructed a NanoSwitch for detecting SARS-CoV-2-specific antibodies, which showed over 200-fold S/N in serum samples. High S/N was achieved by a new working model, combining the turn-off of the sensor with human serum albumin and turn-on with a specific antibody. Also, we constructed NanoSwitches for detecting antibodies against the core protein of hepatitis C virus (HCV) and gp41 of the human immunodeficiency virus (HIV). Interestingly, these sensors demonstrated a high S/N and good performance in the assays of clinical samples; this was partly attributed to the combination of off-and-on models. In summary, we provide a novel type of protein sensor and a working model that potentially guides new sensor design with better performance.


Subject(s)
Biosensing Techniques , COVID-19 , Antibodies, Viral , COVID-19/diagnosis , Humans , Luciferases , SARS-CoV-2
12.
Current psychology (New Brunswick, N.J.) ; : 1-10, 2022.
Article in English | EuropePMC | ID: covidwho-1728565

ABSTRACT

This study explores the relationship between adolescents’ perceptions of epidemic risk and their emotions through three follow-up surveys during the early stages of the COVID-19 pandemic on February 11th (T1), 18th (T2), and 25th (T3), 2020. Three hundred and four adolescents in different academic stages (junior high middle school, senior high middle school, and university) participated in the online survey, and cross-lag analysis was used to examine the causal relationship between epidemic risk perceptions and positive and negative emotions. The results found that the individual’s positive emotions were significantly higher than the negative emotions in T1, T2 and T3. Cross-lag analysis found that for positive emotions, T2 positive emotions could negatively predict T3 epidemic risk perceptions, and T2 epidemic risk perceptions could negatively predict the individual’s T3 positive emotions. For negative emotions, risk perceptions at T1 could positively predict negative emotions at T2, and at the same time, negative emotions at T1 could also positively predict epidemic risk perceptions at T2. This indicates that during the early stages of the COVID-19 pandemic, there was a causal relationship between the perceptions of epidemic risk and the emotions of adolescents, and this relationship had high stability among groups of different genders and academic stages.

13.
Cell Res ; 32(4): 375-382, 2022 Apr.
Article in English | MEDLINE | ID: covidwho-1707327

ABSTRACT

Monoclonal antibodies represent important weapons in our arsenal to against the COVID-19 pandemic. However, this potential is severely limited by the time-consuming process of developing effective antibodies and the relative high cost of manufacturing. Herein, we present a rapid and cost-effective lipid nanoparticle (LNP) encapsulated-mRNA platform for in vivo delivery of SARS-CoV-2 neutralization antibodies. Two mRNAs encoding the light and heavy chains of a potent SARS-CoV-2 neutralizing antibody HB27, which is currently being evaluated in clinical trials, were encapsulated into clinical grade LNP formulations (named as mRNA-HB27-LNP). In vivo characterization demonstrated that intravenous administration of mRNA-HB27-LNP in mice resulted in a longer circulating half-life compared with the original HB27 antibody in protein format. More importantly, a single prophylactic administration of mRNA-HB27-LNP provided protection against SARS-CoV-2 challenge in mice at 1, 7 and even 63 days post administration. In a close contact transmission model, prophylactic administration of mRNA-HB27-LNP prevented SARS-CoV-2 infection between hamsters in a dose-dependent manner. Overall, our results demonstrate a superior long-term protection against SARS-CoV-2 conferred by a single administration of this unique mRNA antibody, highlighting the potential of this universal platform for antibody-based disease prevention and therapy against COVID-19 as well as a variety of other infectious diseases.


Subject(s)
COVID-19 , SARS-CoV-2 , Animals , Antibodies, Neutralizing/therapeutic use , Antibodies, Viral/therapeutic use , COVID-19/prevention & control , Cricetinae , Humans , Liposomes , Mice , Nanoparticles , Pandemics/prevention & control , RNA, Messenger/genetics , Spike Glycoprotein, Coronavirus
14.
EuropePMC;
Preprint in English | EuropePMC | ID: ppcovidwho-327285

ABSTRACT

The emerging SARS-CoV-2 variants of concern (VOC) harbor mutations associated with increasing transmission and immune escape, hence undermine the effectiveness of current COVID-19 vaccines. In late November of 2021, the Omicron (B.1.1.529) variant was identified in South Africa and rapidly spread across the globe. It was shown to exhibit significant resistance to neutralization by serum not only from convalescent patients, but also from individuals recieving currently used COVID-19 vaccines with multiple booster shots. Therefore, there is an urgent need to develop next generation vaccines against VOCs like Omicron. In this study, we develop a panel of mRNA-LNP-based vaccines using the receptor binding domain (RBD) of Omicron and Delta variants, which are dominant in the current wave of COVID-19. In addition to the Omicron- and Delta-specific vaccines, the panel also includes a Hybrid vaccine that uses the RBD containing all 16 point-mutations shown in Omicron and Delta RBD, as well as a bivalent vaccine composed of both Omicron and Delta RBD-LNP in half dose. Interestingly, both Omicron-specific and Hybrid RBD-LNP elicited extremely high titer of neutralizing antibody against Omicron itself, but few to none neutralizing antibody against other SARS-CoV-2 variants. The bivalent RBD-LNP, on the other hand, generated antibody with broadly neutralizing activity against the wild-type virus and all variants. Surprisingly, similar cross-protection was also shown by the Delta-specifc RBD-LNP. Taken together, our data demonstrated that Omicron-specific mRNA vaccine can induce potent neutralizing antibody response against Omicron, but the inclusion of epitopes from other variants may be required for eliciting cross-protection. This study would lay a foundation for rational development of the next generation vaccines against SARS-CoV-2 VOCs.

15.
SSRN;
Preprint in English | SSRN | ID: ppcovidwho-326503

ABSTRACT

Background: SARS-CoV-2 is released directly into the air through breathing by COVID-19 patients. Understanding the particle size distribution of SARS-CoV-2 in the exhaled breath of COVID-19 patients is essential for SARS-CoV-2 infection prevention policies. Methods: Here, serial nasopharyngeal swabs, exhaled breath specimens and environmental surface samples were collected at different days of COVID-19 patient hospitalization. Exhaled breath specimen collection was performed through a viral sampling tube and an air sampler. SARS-CoV-2 RNA detection was determined by real-time quantitative reverse transcription polymerase chain reaction (qRT–PCR). Results: We found that the COVID-19 patients exhaled ten million SARS-CoV-2 RNA copies per hour. The SARS-CoV-2 particles in the exhaled breath were mainly distributed in respiratory droplets (>4.7 μm), accounting for 87.14% of the total virus. There was a positive correlation between viral loads of nasopharyngeal swabs and exhaled breath specimens. The viral load of exhaled breath also showed a positive correlation with the viral load of environmental surface. Conclusions: SARS-CoV-2 in the exhaled breath of COVID-19 patients might be mainly transmitted via respiratory droplets. The viral load in the exhaled breath is determined by the viral load in the nasopharyngeal swab, and the viral load in the exhaled breath determines the presence and extent of environmental surface contamination. Therefore, the exhaled breath of COVID-19 patients plays a major role in the transmission of SARS-CoV-2.

16.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-324789

ABSTRACT

Background: Frontline epidemic prevention workers play a pivotal role against COVID-19. Their baseline of dietary and behavior habits and willingness to change these habits after experiencing the outbreak of COVID-19 remains unclear.Methods: A self-developed online questionnaire survey was carried out via the WeChat platform, and 22,459 participated, including 9402 frontline epidemic prevention workers.Findings: Before COVID-19, 23.9% of frontline epidemic prevention workers reported a high-salt diet, 46.9% reported a high frequency of fried food intake, 21.6% reported a low frequency of fresh vegetable intake, and 50.9% smoked cigarettes. After experiencing the outbreak of COVID-19, 34.6% had the willingness to reduce salt intake, and 43.7% want to reduce the frequency of pickled vegetable intake. 37.9% had the willingness to decrease or quit smoking, and 44.5% want to increase sleep duration. Significant differences in the baseline of dietary and behavioral habits and willingness to change their habits were observed between the frontline epidemic prevention worker and others(P<0.05). However, for the frontline epidemic prevention workers with poor dietary and behavioral habits, the frontline epidemic prevention experience might be a promoting factor to adopt worse dietary and behavioral habits, including the high-salt intake subgroup (OR 2.824, 95% CI 2.341-3.405) and shortest physical exercise time subgroup (OR 1.379, 95% CI 1.041-1.828).Interpretation: The dietary and behavior habits of the frontline epidemic prevention workers were worse than others before COVID-19. They had more willingness to adopt healthy dietary and behavior habits after experiencing the outbreak of COVID-19. Because the frontline epidemic prevention workers, who had poor dietary and behavior habits before COVID-19, still choose worse habits, dietary and behavior intervention policies should be drafted to protect their health, especially for those poor habits subgroups.Funding: This work was supported by The Science and Technology Project of Bao'an (NO.2020JD101).Declaration of Interests: The authors declare that they have no competing interests.Ethics Approval Statement: This study was approved by the ethics committee of Guangdong Medical University.

17.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-324331

ABSTRACT

Background: To investigate impact of the 2019 novel coronavirus disease (COVID-19) pandemic on willingness to adopt healthy dietary habits in China. Methods: : A survey was carried out, and subjective perception of impact due to COVID-19 and willingness to change dietary habits were obtained. Results: : A total of 22,459 subjects were derived from China, with an average age of 27.9±7.8 years old. Of them, the mean score of willingness to adopt healthy dietary habits was 2.2 (ranges from -9 to 9). Multivariate regression analysis showed that the impact of the COVID-19 pandemic (epidemic concern, impact of psychology, impact of work or study) are associated with a higher score of willingness to adopt healthy dietary habits among female, the older, on-medical worker, and individuals married or with higher education level, normal BMI. Conclusions: : There was a positive improvement to a proper diet, so the changing features of diets should be considered in nutritional interventions for maintaining health, and prevention and control COVID-19 during the pandemic period.

18.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-310678

ABSTRACT

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is the causative agent of COVID-19 disease. RT-qPCR has been the primary method of diagnosis;however, the required infrastructure is lacking in many developing countries and the virus has remained a global challenge. More inexpensive and immediate test methods are required to facilitate local, regional, and national management strategies to re-open world economies. Here we have developed a SARS-CoV-2 antigen test in an inexpensive lateral flow format to generate a chromatographic result identifying the presence of the SARS-CoV-2 antigen, and thus an active infection, within a patient anterior nares swab sample. Our 15-minute test requires no equipment or laboratory infrastructure to administer with a limit of detection of 2.0 x 10 2 TCID 50 /mL and 87.5% sensitivity, 100% specificity when tested against 40 known positive and 40 known negative patient samples established by a validated RT-qPCR test.

19.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-308225

ABSTRACT

Background: Coronavirus disease 2019 (COVID-19) is a systemic disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. The purpose of the present study was to investigate the association between lung injury and cytokine profile in COVID-19 pneumonia. Methods: This retrospective study was conducted in COVID-19 patients. Demographic characteristics, symptoms, signs, underlying diseases, and laboratory data were collected. The patients were divided into COVID-19 with pneumonia and without pneumonia. CT severity score and PaO 2 /FiO 2 ratio and were used to assess lung injury. Results: 106 patients with 12 COVID-19 without pneumonia and 94 COVID-19 with pneumonia were included. Compared with COVID-19 without pneumonia, COVID-19 with pneumonia had significant higher serum interleukin (IL)-2R, IL-6, and tumor necrosis factor (TNF)-α. Correlation analysis showed that CT severity score and PaO 2 /FiO 2 were significantly correlated with age, presence of any coexisting disorder, lymphocyte count, procalcitonin, IL-2R, and IL-6. In multivariate analysis, log IL6 was only independent explanatory variables for CT severity score (β=0.397, p<0.001) and PaO 2 /FiO 2 (β=-0.434, p=0.003). Conclusions: Elevation of circulating cytokines was significantly associated with presence of pneumonia in COVID-19 and the severity of lung injury in COVID-19 pneumonia. Circulating IL-6 independently predicted the severity of lung injury in COVID-19 pneumonia.

20.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-308055

ABSTRACT

Neural network training and validation rely on the availability of large high-quality datasets. However, in many cases only incomplete datasets are available, particularly in health care applications, where each patient typically undergoes different clinical procedures or can drop out of a study. Since the data to train the neural networks need to be complete, most studies discard the incomplete datapoints, which reduces the size of the training data, or impute the missing features, which can lead to artefacts. Alas, both approaches are inadequate when a large portion of the data is missing. Here, we introduce GapNet, an alternative deep-learning training approach that can use highly incomplete datasets. First, the dataset is split into subsets of samples containing all values for a certain cluster of features. Then, these subsets are used to train individual neural networks. Finally, this ensemble of neural networks is combined into a single neural network whose training is fine-tuned using all complete datapoints. Using two highly incomplete real-world medical datasets, we show that GapNet improves the identification of patients with underlying Alzheimer's disease pathology and of patients at risk of hospitalization due to Covid-19. By distilling the information available in incomplete datasets without having to reduce their size or to impute missing values, GapNet will permit to extract valuable information from a wide range of datasets, benefiting diverse fields from medicine to engineering.

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