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1.
Nature ; 604(7907): 723-731, 2022 04.
Article in English | MEDLINE | ID: covidwho-1799583

ABSTRACT

Studying tissue composition and function in non-human primates (NHPs) is crucial to understand the nature of our own species. Here we present a large-scale cell transcriptomic atlas that encompasses over 1 million cells from 45 tissues of the adult NHP Macaca fascicularis. This dataset provides a vast annotated resource to study a species phylogenetically close to humans. To demonstrate the utility of the atlas, we have reconstructed the cell-cell interaction networks that drive Wnt signalling across the body, mapped the distribution of receptors and co-receptors for viruses causing human infectious diseases, and intersected our data with human genetic disease orthologues to establish potential clinical associations. Our M. fascicularis cell atlas constitutes an essential reference for future studies in humans and NHPs.


Subject(s)
Macaca fascicularis , Transcriptome , Animals , Cell Communication , Macaca fascicularis/genetics , Receptors, Virus/genetics , Transcriptome/genetics , Wnt Signaling Pathway
2.
EuropePMC; 2022.
Preprint in English | EuropePMC | ID: ppcovidwho-329211

ABSTRACT

Numerous mutations in the spike protein of SARS-CoV-2 B.1.1.529 Omicron variant pose a crisis for antibody-based immunotherapies. The efficacy of emergency use authorized (EUA) antibodies that developed in early SARS-CoV-2 pandemic seems to be in flounder. In this work, we examined the Omicron variant neutralization using an early B cell antibody repertoire as well as several EUA antibodies in pseudovirus and authentic virus systems. More than half of the antibodies in the repertoire that showed good activity against WA1/2020 previously had completely lost neutralizing activity against Omicron, while antibody 8G3 from our early B cell repertoire displayed non-regressive activity. EUA antibodies Etesevimab, Casirivimab, Imdevimab and Bamlanivimab neutralized authentic WA1/2020 virus with low half maximal inhibitory concentration (IC50) values, but were entirely desensitized by Omicron. Only Sotrovimab targeting the non-ACE2 overlap epitope showed activity but with a dramatic decrease. Interestingly, antibody 8G3 efficiently neutralized Omicron in pseudovirus and authentic virus systems. 8G3 also showed excellent activity against other variants of concern (VOCs). Collectively, our results suggest that neutralizing antibodies with breadth remains broad neutralizing activity in tackling SARS-CoV-2 infection despite the universal evasion from EUA antibodies by Omicron variant.

3.
Stem Cell Reports ; 17(3): 522-537, 2022 03 08.
Article in English | MEDLINE | ID: covidwho-1692862

ABSTRACT

Patients with coronavirus disease 2019 (COVID-19) commonly have manifestations of heart disease. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genome encodes 27 proteins. Currently, SARS-CoV-2 gene-induced abnormalities of human heart muscle cells remain elusive. Here, we comprehensively characterized the detrimental effects of a SARS-CoV-2 gene, Orf9c, on human pluripotent stem cell-derived cardiomyocytes (hPSC-CMs) by preforming multi-omic analyses. Transcriptomic analyses of hPSC-CMs infected by SARS-CoV-2 with Orf9c overexpression (Orf9cOE) identified concordantly up-regulated genes enriched into stress-related apoptosis and inflammation signaling pathways, and down-regulated CM functional genes. Proteomic analysis revealed enhanced expressions of apoptotic factors, whereas reduced protein factors for ATP synthesis by Orf9cOE. Orf9cOE significantly reduced cellular ATP level, induced apoptosis, and caused electrical dysfunctions of hPSC-CMs. Finally, drugs approved by the U.S. Food and Drug Administration, namely, ivermectin and meclizine, restored ATP levels and ameliorated CM death and functional abnormalities of Orf9cOE hPSC-CMs. Overall, we defined the molecular mechanisms underlying the detrimental impacts of Orf9c on hPSC-CMs and explored potentially therapeutic approaches to ameliorate Orf9c-induced cardiac injury and abnormalities.


Subject(s)
COVID-19/pathology , Coronavirus Nucleocapsid Proteins/genetics , Genome-Wide Association Study/methods , SARS-CoV-2/genetics , Action Potentials/drug effects , Adenosine Triphosphate/metabolism , Apoptosis/drug effects , Apoptosis/genetics , COVID-19/virology , Down-Regulation , Humans , Ivermectin/pharmacology , Meclizine/pharmacology , Myocytes, Cardiac/cytology , Myocytes, Cardiac/metabolism , Phosphoproteins/genetics , Pluripotent Stem Cells/cytology , Pluripotent Stem Cells/metabolism , Protein Interaction Maps/genetics , RNA, Messenger/chemistry , RNA, Messenger/metabolism , SARS-CoV-2/isolation & purification , Signal Transduction/genetics , Transcriptome/drug effects , Up-Regulation
4.
Cell Discov ; 8(1): 16, 2022 Feb 15.
Article in English | MEDLINE | ID: covidwho-1692632

ABSTRACT

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern (VOCs) continue to wreak havoc across the globe. Higher transmissibility and immunologic resistance of VOCs bring unprecedented challenges to epidemic extinguishment. Here we describe a monoclonal antibody, 2G1, that neutralizes all current VOCs and has surprising tolerance to mutations adjacent to or within its interaction epitope. Cryo-electron microscopy structure showed that 2G1 bound to the tip of receptor binding domain (RBD) of spike protein with small contact interface but strong hydrophobic effect, which resulted in nanomolar to sub-nanomolar affinities to spike proteins. The epitope of 2G1 on RBD partially overlaps with angiotensin converting enzyme 2 (ACE2) interface, which enables 2G1 to block interaction between RBD and ACE2. The narrow binding epitope but high affinity bestow outstanding therapeutic efficacy upon 2G1 that neutralized VOCs with sub-nanomolar half maximal inhibitory concentration in vitro. In SARS-CoV-2, Beta or Delta variant-challenged transgenic mice and rhesus macaque models, 2G1 protected animals from clinical illness and eliminated viral burden, without serious impact to animal safety. Mutagenesis experiments suggest that 2G1 is potentially capable of dealing with emerging SARS-CoV-2 variants in the future. This report characterized the therapeutic antibodies specific to the tip of spike against SARS-CoV-2 variants and highlights the potential clinical applications as well as for developing vaccine and cocktail therapy.

5.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-324276

ABSTRACT

Background: A cluster of acute respiratory illness, now known as Corona Virus Disease 2019 (COVID-19) caused by 2019 novel coronavirus (SARS-CoV-2), has become a global pandemic. Aged population with cardiovascular diseases are more likely be to infected with SARS-CoV-2 and result in more severe outcomes and elevated case-fatality rate. Meanwhile, cardiovascular diseases have a high prevalence in the middle-aged and elderly population. However, despite of several researches in COVID-19, cardiovascular implications related to it still remains largely unclear. Therefore, a specific analysis in regard to cardiovascular implications of COVID-19 patients is in great need.Methods In this single-centered, retrospective, observational study, 116 patients with laboratory-confirmed COVID-19 were enrolled, who admitted to the General Hospital of Central Theater Command (Wuhan, China) from January 20 to March 8, 2020. The demographic data, underlying comorbidities, clinical symptoms and signs, laboratory findings, chest computed tomography, treatment measures, and outcome data were collected from electronic medical records. Data were compared between non-severe and severe cases.ResultsOf 116 hospitalized patients with COVID-19, the median age was 58.5 years (IQR, 47.0-69.0), and 36 (31.0%) were female. Hypertension (45 [38.8%]), diabetes (19 [16.4%]), and coronary heart disease (17 [14.7%]) were the most common coexisting conditions. Common symptoms included fever [99 (85.3%)], dry cough (61 [52.6%]), fatigue (60 [51.7%]), dyspnea (52 [44.8%]), anorexia (50 [43.1%]), and chest discomfort (50 [43.1%]). Local and/or bilateral patchy shadowing were the typical radiological findings on chest computed tomography. Lymphopenia (lymphocyte count, 1.0 × 10 9 /L [IQR, 0.7-1.3]) was observed in 66 patients (56.9%), and elevated lactate dehydrogenase (245.5 U/L [IQR, 194.3-319.8]) in 69 patients (59.5%). Hypokalemia occurred in 24 (20.7%) patients. Compared with non-severe cases, severe cases were older (64.0 years [IQR, 53.0-76.0] vs 56.0 years [IQR, 37.0-64.0]), more likely to have comorbidities (35 [63.6%] vs 24 [39.3%]), and more likely to develop acute cardiac injury (19 [34.5%] vs 4 [6.6%]), acute heart failure (18 [32.7%] vs 3 [4.9%]), and ARDS (20 [36.4%] vs 0 [0%]). During hospitalization, the prevalence of new onset hypertension was significantly higher in severe patients (55.2% vs 19.0%) than in non-severe ones.Conclusions In this single-centered, retrospective, observational study, we found that the infection of SARS-CoV-2 was more likely to occur in middle and aged population with cardiovascular comorbidities. Cardiovascular complications, including new onset hypertension and heart injury were common in severe patients with COVID-19. More detailed researches in cardiovascular involvement in COVID-19 are urgently needed to further understand the disease.

6.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-306299

ABSTRACT

The COVID-19 is sweeping the world with deadly consequences. Its contagious nature and clinical similarity to other pneumonias make separating subjects contracted with COVID-19 and non-COVID-19 viral pneumonia a priority and a challenge. However, COVID-19 testing has been greatly limited by the availability and cost of existing methods, even in developed countries like the US. Intrigued by the wide availability of routine blood tests, we propose to leverage them for COVID-19 testing using the power of machine learning. Two proven-robust machine learning model families, random forests (RFs) and support vector machines (SVMs), are employed to tackle the challenge. Trained on blood data from 208 moderate COVID-19 subjects and 86 subjects with non-COVID-19 moderate viral pneumonia, the best result is obtained in an SVM-based classifier with an accuracy of 84%, a sensitivity of 88%, a specificity of 80%, and a precision of 92%. The results are found explainable from both machine learning and medical perspectives. A privacy-protected web portal is set up to help medical personnel in their practice and the trained models are released for developers to further build other applications. We hope our results can help the world fight this pandemic and welcome clinical verification of our approach on larger populations.

7.
Front Genet ; 12: 819493, 2021.
Article in English | MEDLINE | ID: covidwho-1674328

ABSTRACT

The masked palm civet (Paguma larvata) is a small carnivore with distinct biological characteristics, that likes an omnivorous diet and also serves as a vector of pathogens. Although this species is not an endangered animal, its population is reportedly declining. Since the severe acute respiratory syndrome (SARS) epidemic in 2003, the public has been particularly concerned about this species. Here, we present the first genome of the P. larvata, comprising 22 chromosomes assembled using single-tube long fragment read (stLFR) and Hi-C technologies. The genome length is 2.41 Gb with a scaffold N50 of 105.6 Mb. We identified the 107.13 Mb X chromosome and one 1.34 Mb Y-linked scaffold and validated them by resequencing 45 P. larvata individuals. We predicted 18,340 protein-coding genes, among which 18,333 genes were functionally annotated. Interestingly, several biological pathways related to immune defenses were found to be significantly expanded. Also, more than 40% of the enriched pathways on the positively selected genes (PSGs) were identified to be closely related to immunity and survival. These enriched gene families were inferred to be essential for the P. larvata for defense against the pathogens. However, we did not find a direct genomic basis for its adaptation to omnivorous diet despite multiple attempts of comparative genomic analysis. In addition, we evaluated the susceptibility of the P. larvata to the SARS-CoV-2 by screening the RNA expression of the ACE2 and TMPRSS2/TMPRSS4 genes in 16 organs. Finally, we explored the genome-wide heterozygosity and compared it with other animals to evaluate the population status of this species. Taken together, this chromosome-scale genome of the P. larvata provides a necessary resource and insights for understanding the genetic basis of its biological characteristics, evolution, and disease transmission control.

8.
Nat Commun ; 12(1): 7083, 2021 12 06.
Article in English | MEDLINE | ID: covidwho-1555251

ABSTRACT

The availability of viral entry factors is a prerequisite for the cross-species transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Large-scale single-cell screening of animal cells could reveal the expression patterns of viral entry genes in different hosts. However, such exploration for SARS-CoV-2 remains limited. Here, we perform single-nucleus RNA sequencing for 11 non-model species, including pets (cat, dog, hamster, and lizard), livestock (goat and rabbit), poultry (duck and pigeon), and wildlife (pangolin, tiger, and deer), and investigated the co-expression of ACE2 and TMPRSS2. Furthermore, cross-species analysis of the lung cell atlas of the studied mammals, reptiles, and birds reveals core developmental programs, critical connectomes, and conserved regulatory circuits among these evolutionarily distant species. Overall, our work provides a compendium of gene expression profiles for non-model animals, which could be employed to identify potential SARS-CoV-2 target cells and putative zoonotic reservoirs.


Subject(s)
Atlases as Topic , Single-Cell Analysis/veterinary , Angiotensin-Converting Enzyme 2/genetics , Angiotensin-Converting Enzyme 2/metabolism , Animals , Birds , Cell Communication , Evolution, Molecular , Gene Regulatory Networks , Host-Pathogen Interactions , Lung/cytology , Lung/metabolism , Lung/virology , Mammals , Receptors, Virus/genetics , Receptors, Virus/metabolism , Reptiles , SARS-CoV-2/metabolism , Serine Endopeptidases/genetics , Serine Endopeptidases/metabolism , Transcriptome , Viral Tropism , Virus Internalization
9.
Front Public Health ; 9: 712190, 2021.
Article in English | MEDLINE | ID: covidwho-1405442

ABSTRACT

Fever is one of the typical symptoms of coronavirus disease (COVID-19). We aimed to investigate the association between early fever (EF) and clinical outcomes in COVID-19 patients. A total of 1,014 COVID-19 patients at the Leishenshan Hospital were enrolled and classified into the EF and non-EF groups based on whether they had fever within 5 days of symptom onset. Risk factors for clinical outcomes in patients with different levels of disease severity were analyzed using multivariable analyses. Time from symptom onset to symptom alleviation, CT image improvement, and discharge were longer for patients with moderate and severe disease in the EF group than in the non-EF group. Multivariable analysis showed that sex, EF, eosinophil number, C-reactive protein, and IL-6 levels were positively correlated with the time from symptom onset to hospital discharge in moderate cases. The EF patients showed no significant differences in the development of acute respiratory distress syndrome, compared with the non-EF patients. The Kaplan-Meier curve showed no obvious differences in survival between the EF and non-EF patients. However, EF patients with increased temperature showed markedly lower survival than the non-EF patients with increased temperature. EF had no significant impact on the survival of critically ill patients, while an increase in temperature was identified as an independent risk factor. EF appears to be a predictor of longer recovery time in moderate/severe COVID-19 infections. However, its value in predicting mortality needs to be considered for critically ill patients with EF showing increasing temperature.


Subject(s)
COVID-19 , Critical Illness , Fever/epidemiology , Humans , Retrospective Studies , SARS-CoV-2
10.
JMIR Public Health Surveill ; 7(9): e31052, 2021 09 16.
Article in English | MEDLINE | ID: covidwho-1394691

ABSTRACT

BACKGROUND: The outbreak of the COVID-19 pandemic has caused great panic among the public, with many people suffering from adverse stress reactions. To control the spread of the pandemic, governments in many countries have imposed lockdown policies. In this unique pandemic context, people can obtain information about pandemic dynamics on the internet. However, searching for health-related information on the internet frequently increases the possibility of individuals being troubled by the information that they find, and consequently, experiencing symptoms of cyberchondria. OBJECTIVE: We aimed to examine the relationships between people's perceived severity of the COVID-19 pandemic and their depression, anxiety, and stress to explore the role of cyberchondria, which, in these relationship mechanisms, is closely related to using the internet. In addition, we also examined the moderating role of lockdown experiences. METHODS: In February 2020, a total of 486 participants were recruited through a web-based platform from areas in China with a large number of infections. We used questionnaires to measure participants' perceived severity of the COVID-19 pandemic, to measure the severity of their cyberchondria, depression, anxiety, and stress symptoms, and to assess their lockdown experiences. Confirmatory factor analysis, exploratory factor analysis, common method bias, descriptive statistical analysis, and correlation analysis were performed, and moderated mediation models were examined. RESULTS: There was a positive association between perceived severity of the COVID-19 pandemic and depression (ß=0.36, t=8.51, P<.001), anxiety (ß=0.41, t=9.84, P<.001), and stress (ß=0.46, t=11.45, P<.001), which were mediated by cyberchondria (ß=0.36, t=8.59, P<.001). The direct effects of perceived severity of the COVID-19 pandemic on anxiety (ß=0.07, t=2.01, P=.045) and stress (ß=0.09, t=2.75, P=.006) and the indirect effects of cyberchondria on depression (ß=0.10, t=2.59, P=.009) and anxiety (ß=0.10, t=2.50, P=.01) were moderated by lockdown experience. CONCLUSIONS: The higher the perceived severity of the COVID-19 pandemic, the more serious individuals' symptoms of depression, anxiety, and stress. In addition, the associations were partially mediated by cyberchondria. Individuals with higher perceived severity of the COVID-19 pandemic were more likely to develop cyberchondria, which aggravated individuals' depression, anxiety, and stress symptoms. Negative lockdown experiences exacerbated the COVID-19 pandemic's impact on mental health.


Subject(s)
COVID-19/psychology , Perception , Quarantine/psychology , Stress, Psychological/complications , Adolescent , Adult , Anxiety/etiology , Anxiety/psychology , COVID-19/epidemiology , COVID-19/prevention & control , China/epidemiology , Cross-Sectional Studies , Depression/etiology , Depression/psychology , Female , Humans , Male , Middle Aged , Quarantine/standards , Social Media/standards , Social Media/statistics & numerical data , Stress, Psychological/psychology
11.
J Inflamm Res ; 14: 1677-1687, 2021.
Article in English | MEDLINE | ID: covidwho-1231284

ABSTRACT

BACKGROUND: Whether COVID-19 comorbidities and risk factors such as old age, male gender, smoking, obesity, eosinophils and blood types have direct contact with expression of ACE2 and pro-inflammation cytokines in human lung tissues were still unclear. PATIENTS AND METHODS: Sixty-four patients with normal FEV1 and FEV1/FVC underwent thoracotomy for pulmonary nodules were included. Blinded histological assessments were performed by two pathologists. Clinical features and results of the immunohistochemical staining of ACE2 were collected and analyzed. RESULTS: ACE2 expressed in alveolar macrophages (most obvious), alveolar epithelia and vascular endothelia, but not in small-airway epithelia. ACE2 expressions are positively related to age (r =0.26, P =0.040), weight (r =0.43, P<0.001), as well as BMI (r = 0.38, P =0.002), and male patients show higher expressions of ACE2 in lungs (P <0.05). ACE2 expressions are negatively related to peripheral eosinophils (r = -0.30, P =0.017). There was no correlation between ABO blood types and ACE2 expression in normal lung tissues (P > 0.05). IL-13 and IL-6R expression in lung tissue increased with age (r =0.26, P <0.05, for both). CONCLUSION: Our pathological evidences showed that the alveolar epithelia, vascular endothelia, and alveolar macrophages are susceptible in human lungs for SARS-CoV-2 infection. The risk factors such as high body weight/BMI, old age, male gender, and eosinopenia may be related to ACE2 expression in human lungs, and associated with more chance to develop the severe cases. IL-6R expression in lung tissue also increased with age. Therefore, weight control and smoking cessation are essential to reduce the susceptibility of SARS-CoV-2 infection, especially in obesity, old or male patients. Peripheral eosinophils monitor is also quite necessary to detect severe tendency in COVID-19 patients.

12.
J Asthma Allergy ; 14: 415-426, 2021.
Article in English | MEDLINE | ID: covidwho-1231282

ABSTRACT

PURPOSE: Patients with variable symptoms suggestive of asthma but with normal forced expiratory volume in 1 second (FEV1) often fail to be diagnosed without a bronchial provocation test, but the test is expensive, time-consuming, risky, and not readily available in all clinical settings. PATIENTS AND METHODS: A cross-sectional study was performed in 692 patients with FEV1≥80% predicted; normal neutrophils and chest high-resolution computed tomography; and recurrent dyspnea, cough, wheeze, and chest tightness. RESULTS: Compared with subjects negative for AHR (n=522), subjects positive for AHR (n=170) showed increased FENO values, peripheral eosinophils (EOS), and R5-R20; decreased FEV1, FEV1/Forced vital capacity (FVC), and forced expiratory flow (FEFs) (P≤.001 for all). Small-airway dysfunction was identified in 104 AHR+ patients (61.17%), and 132 AHR- patients (25.29%) (P<0.001). The areas under the curve (AUCs) of variables used singly for an AHR diagnosis were lower than 0.77. Using joint models of FEF50%, FEF75%, or FEF25%-75% with FENO increased the AUCs to 0.845, 0.824, and 0.844, respectively, significantly higher than univariate AUCs (P <0.001 for all). Patients who reported chest tightness (n=75) had lower FEFs than patients who did not (P<0.001 for all). In subjects with chest tightness, the combination of FEF50% or FEF25%-75% with EOS also increased the AUCs substantially, to 0.815 and 0.816, respectively (P <0.001 for all versus the univariate AUCs). CONCLUSION: FENO combined with FEF50% and FEF25%-75% predict AHR in patients with normal FEV1. FEF25%-75%, FEF50%, or FEF25%-75% together with EOS also can potentially suggest asthma in patients with chest tightness.

13.
Ann Transl Med ; 9(8): 665, 2021 Apr.
Article in English | MEDLINE | ID: covidwho-1224387

ABSTRACT

BACKGROUND: Since the outbreak of coronavirus disease 2019 (COVID-19), the pattern of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA shedding has not been well characterized. METHODS: In our study, 652 patients in Wuhan Designated Hospital were recruited, and their clinical and laboratory findings were extracted and analyzed. RESULTS: The median duration of SARS-CoV-2 RNA detection was 23 days [interquartile range (IQR), 18 days] from symptom onset. Compared to patients with early viral RNA clearance (<23 days after illness onset), we found that patients with late viral RNA clearance (≥23 days) had a higher proportion of clinical features, as follows: symptoms, including fever, dry cough, and sputum production; comorbidities, including hypertension, chronic kidney disease, uremia, chronic liver disease, anemia, hyperlipidemia, and bilateral lung involvement; complications, such as liver injury; delayed admission to hospital; laboratory parameters at baseline, including higher eosinophils, uric acid, cholesterol, triglycerides, and lower hemoglobin; and less treatment with arbidol, chloroquine, or any antivirals. After generalized linear regression, prolonged SARS-CoV-2 RNA shedding was independently associated with younger age; delayed admission to hospital; symptoms including fever, shivering, and sputum production; comorbidities including hypertension, diabetes, cardiovascular disease, anemia, hyperlipidemia, uremia, and lung involvement; and higher alanine aminotransferase (ALT), uric acid, and cholesterol levels at baseline. CONCLUSIONS: In conclusion, the factors mentioned above are associated with the negative conversion of SARS-CoV-2 RNA. A deeper insight into virological dynamics will be helpful for establishing patient discharge and quarantine release criteria.

14.
Journal of Medical Internet Research ; 23(4), 2021.
Article in English | ProQuest Central | ID: covidwho-1209585

ABSTRACT

Background: Effectively and efficiently diagnosing patients who have COVID-19 with the accurate clinical type of the disease is essential to achieve optimal outcomes for the patients as well as to reduce the risk of overloading the health care system. Currently, severe and nonsevere COVID-19 types are differentiated by only a few features, which do not comprehensively characterize the complicated pathological, physiological, and immunological responses to SARS-CoV-2 infection in the different disease types. In addition, these type-defining features may not be readily testable at the time of diagnosis. Objective: In this study, we aimed to use a machine learning approach to understand COVID-19 more comprehensively, accurately differentiate severe and nonsevere COVID-19 clinical types based on multiple medical features, and provide reliable predictions of the clinical type of the disease. Methods: For this study, we recruited 214 confirmed patients with nonsevere COVID-19 and 148 patients with severe COVID-19. The clinical characteristics (26 features) and laboratory test results (26 features) upon admission were acquired as two input modalities. Exploratory analyses demonstrated that these features differed substantially between two clinical types. Machine learning random forest models based on all the features in each modality as well as on the top 5 features in each modality combined were developed and validated to differentiate COVID-19 clinical types. Results: Using clinical and laboratory results independently as input, the random forest models achieved >90% and >95% predictive accuracy, respectively. The importance scores of the input features were further evaluated, and the top 5 features from each modality were identified (age, hypertension, cardiovascular disease, gender, and diabetes for the clinical features modality, and dimerized plasmin fragment D, high sensitivity troponin I, absolute neutrophil count, interleukin 6, and lactate dehydrogenase for the laboratory testing modality, in descending order). Using these top 10 multimodal features as the only input instead of all 52 features combined, the random forest model was able to achieve 97% predictive accuracy. Conclusions: Our findings shed light on how the human body reacts to SARS-CoV-2 infection as a unit and provide insights on effectively evaluating the disease severity of patients with COVID-19 based on more common medical features when gold standard features are not available. We suggest that clinical information can be used as an initial screening tool for self-evaluation and triage, while laboratory test results should be applied when accuracy is the priority.

15.
China Rural Economy ; 1(13), 2021.
Article in Chinese | CAB Abstracts | ID: covidwho-1206663

ABSTRACT

Since China's entry into WTO, the scale of grain import has been increasing continuously, and the structure of grain import has been continuously optimized, which has greatly alleviated the pressure of resources and environment on domestic grain production, and effectively met the consumption demand of domestic grain market. Meanwhile, the diversification process of grain import channels has been accelerated, and the center of gravity has been constantly shifting to the regions along the Belt and Road, so as to enhance the ability of avoiding the market risks of single import channels and strengthening the initiative of grain import. The fluctuation of grain import price has been weakened, which provides favorable external conditions for the stability of domestic agricultural product prices. Meanwhile, the inversion of grain prices at home and abroad has been intensified for a time, which highlights the competitive disadvantage of the domestic grain market. The outbreak of COVID-19 in 2020 tested and strengthened China's ability to utilize foreign food resources and markets in major international public security emergencies and exposed the potential risk of price fluctuations due to China's long-standing dependence on soybean imports. Therefore, China should focus on the transformation and upgrading of domestic grain production, continue to shift the focus of grain import from the traditional layout in Europe and the United States to the countries along the Belt and Road, strengthen price risk management with agricultural financial instruments, and enrich the grain security strategy with the concept of "food security".

16.
Allergy ; 76(2): 471-482, 2021 02.
Article in English | MEDLINE | ID: covidwho-1140082

ABSTRACT

BACKGROUND: Coronavirus disease 2019 (COVID-19) emerged in Wuhan city and rapidly spread globally outside China. We aimed to investigate the role of peripheral blood eosinophil (EOS) as a marker in the course of the virus infection to improve the efficiency of diagnosis and evaluation of COVID-19 patients. METHODS: 227 pneumonia patients who visited the fever clinics in Shanghai General Hospital and 97 hospitalized COVID-19 patients admitted to Shanghai Public Health Clinical Center were involved in a retrospective research study. Clinical, laboratory, and radiologic data were collected. The trend of EOS level in COVID-19 patients and comparison among patients with different severity were summarized. RESULTS: The majority of COVID-19 patients (71.7%) had a decrease in circulating EOS counts, which was significantly more frequent than other types of pneumonia patients. EOS counts had good value for COVID-19 prediction, even higher when combined with neutrophil-to-lymphocyte ratio. Patients with low EOS counts at admission were more likely to have fever, fatigue, and shortness of breath, with more lesions in chest CT and radiographic aggravation, and longer length of hospital stay and course of disease than those with normal EOS counts. Circulating EOS level gradually increased over the time, and was synchronous with the improvement in chest CT (12 days vs 13 days, P = .07), later than that of body temperature (12 days vs 10 days, P = .014), but earlier than that of the negative conversion of nucleic acid assays (12 days vs 17 days, P = .001). CONCLUSION: Peripheral blood EOS counts may be an effective and efficient indicator in diagnosis, Evaluation, and prognosis monitoring of COVID-19 patients.


Subject(s)
Biomarkers/blood , COVID-19/blood , COVID-19/diagnosis , Eosinophils , Adult , Aged , Female , Humans , Leukocyte Count/methods , Male , Middle Aged , Prognosis , Retrospective Studies , SARS-CoV-2
17.
J Med Internet Res ; 23(4): e23948, 2021 04 07.
Article in English | MEDLINE | ID: covidwho-1133811

ABSTRACT

BACKGROUND: Effectively and efficiently diagnosing patients who have COVID-19 with the accurate clinical type of the disease is essential to achieve optimal outcomes for the patients as well as to reduce the risk of overloading the health care system. Currently, severe and nonsevere COVID-19 types are differentiated by only a few features, which do not comprehensively characterize the complicated pathological, physiological, and immunological responses to SARS-CoV-2 infection in the different disease types. In addition, these type-defining features may not be readily testable at the time of diagnosis. OBJECTIVE: In this study, we aimed to use a machine learning approach to understand COVID-19 more comprehensively, accurately differentiate severe and nonsevere COVID-19 clinical types based on multiple medical features, and provide reliable predictions of the clinical type of the disease. METHODS: For this study, we recruited 214 confirmed patients with nonsevere COVID-19 and 148 patients with severe COVID-19. The clinical characteristics (26 features) and laboratory test results (26 features) upon admission were acquired as two input modalities. Exploratory analyses demonstrated that these features differed substantially between two clinical types. Machine learning random forest models based on all the features in each modality as well as on the top 5 features in each modality combined were developed and validated to differentiate COVID-19 clinical types. RESULTS: Using clinical and laboratory results independently as input, the random forest models achieved >90% and >95% predictive accuracy, respectively. The importance scores of the input features were further evaluated, and the top 5 features from each modality were identified (age, hypertension, cardiovascular disease, gender, and diabetes for the clinical features modality, and dimerized plasmin fragment D, high sensitivity troponin I, absolute neutrophil count, interleukin 6, and lactate dehydrogenase for the laboratory testing modality, in descending order). Using these top 10 multimodal features as the only input instead of all 52 features combined, the random forest model was able to achieve 97% predictive accuracy. CONCLUSIONS: Our findings shed light on how the human body reacts to SARS-CoV-2 infection as a unit and provide insights on effectively evaluating the disease severity of patients with COVID-19 based on more common medical features when gold standard features are not available. We suggest that clinical information can be used as an initial screening tool for self-evaluation and triage, while laboratory test results should be applied when accuracy is the priority.


Subject(s)
COVID-19 , Machine Learning , SARS-CoV-2 , Severity of Illness Index , Triage , China , Female , Humans , Male , Middle Aged , Models, Theoretical , Reproducibility of Results
18.
J Med Internet Res ; 23(1): e25535, 2021 01 06.
Article in English | MEDLINE | ID: covidwho-1011363

ABSTRACT

BACKGROUND: Effectively identifying patients with COVID-19 using nonpolymerase chain reaction biomedical data is critical for achieving optimal clinical outcomes. Currently, there is a lack of comprehensive understanding in various biomedical features and appropriate analytical approaches for enabling the early detection and effective diagnosis of patients with COVID-19. OBJECTIVE: We aimed to combine low-dimensional clinical and lab testing data, as well as high-dimensional computed tomography (CT) imaging data, to accurately differentiate between healthy individuals, patients with COVID-19, and patients with non-COVID viral pneumonia, especially at the early stage of infection. METHODS: In this study, we recruited 214 patients with nonsevere COVID-19, 148 patients with severe COVID-19, 198 noninfected healthy participants, and 129 patients with non-COVID viral pneumonia. The participants' clinical information (ie, 23 features), lab testing results (ie, 10 features), and CT scans upon admission were acquired and used as 3 input feature modalities. To enable the late fusion of multimodal features, we constructed a deep learning model to extract a 10-feature high-level representation of CT scans. We then developed 3 machine learning models (ie, k-nearest neighbor, random forest, and support vector machine models) based on the combined 43 features from all 3 modalities to differentiate between the following 4 classes: nonsevere, severe, healthy, and viral pneumonia. RESULTS: Multimodal features provided substantial performance gain from the use of any single feature modality. All 3 machine learning models had high overall prediction accuracy (95.4%-97.7%) and high class-specific prediction accuracy (90.6%-99.9%). CONCLUSIONS: Compared to the existing binary classification benchmarks that are often focused on single-feature modality, this study's hybrid deep learning-machine learning framework provided a novel and effective breakthrough for clinical applications. Our findings, which come from a relatively large sample size, and analytical workflow will supplement and assist with clinical decision support for current COVID-19 diagnostic methods and other clinical applications with high-dimensional multimodal biomedical features.


Subject(s)
COVID-19/diagnosis , Decision Support Systems, Clinical , Health , Machine Learning , Pneumonia, Viral/diagnosis , COVID-19/diagnostic imaging , Diagnosis, Differential , Humans , Middle Aged , Pneumonia, Viral/diagnostic imaging , SARS-CoV-2 , Support Vector Machine , Tomography, X-Ray Computed
19.
Journal of Integrative Agriculture ; 19(12):2903-2915, 2020.
Article in English | CAB Abstracts | ID: covidwho-974785

ABSTRACT

The purposes of this study are to assess the COVID-19 pandemic's impacts on the dairy industries in China and the United States and to derive policy recommendations for enhancing the diary industries' resilience to pandemics and other market shocks. Specifically, data from the two nations are used to analyze and compare the mechanisms through which the pandemic has affected their dairy industries and to discuss potential lessons from their experiences. The findings suggest that this pandemic has heavily affected the dairy industries in both China and the United States through similar mechanisms, such as decreased farmgate milk prices, disruption and difficulties of moving milk within the supply chains, worker shortages, increased production costs, and lack of operating capital. There were also significant differences in the affecting mechanisms between the two nations, including transportation difficulties from widespread road closures and significant reduction in holiday sales of dairy products in China, and the shutdown of many dairy processors in the United States due to the closing of schools, restaurants, and hotels. While government financial reliefs are highly needed to help many dairy farms and processors survive this pandemic in the short term, the dairy industries and governments need to work together to develop long-term strategies and policies to balance the industries' efficiency and flexibility, product specialization and diversification, supply chain integration and local food systems, and market mechanisms and policy regulations and interventions.

20.
Journal of Integrative Agriculture ; 19(12):2903-2915, 2020.
Article in English | ScienceDirect | ID: covidwho-933562

ABSTRACT

The purposes of this study are to assess the COVID-19 pandemic's impacts on the dairy industries in China and the United States and to derive policy recommendations for enhancing the diary industries' resilience to pandemics and other market shocks. Specifically, data from the two nations are used to analyze and compare the mechanisms through which the pandemic has affected their dairy industries and to discuss potential lessons from their experiences. The findings suggest that this pandemic has heavily affected the dairy industries in both China and the United States through similar mechanisms, such as decreased farmgate milk prices, disruption and difficulties of moving milk within the supply chains, worker shortages, increased production costs, and lack of operating capital. There were also significant differences in the affecting mechanisms between the two nations, including transportation difficulties from widespread road closures and significant reduction in holiday sales of dairy products in China, and the shutdown of many dairy processors in the United States due to the closing of schools, restaurants, and hotels. While government financial reliefs are highly needed to help many dairy farms and processors survive this pandemic in the short term, the dairy industries and governments need to work together to develop long-term strategies and policies to balance the industries' efficiency and flexibility, product specialization and diversification, supply chain integration and local food systems, and market mechanisms and policy regulations and interventions.

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