Reading Chinese anti-COVID-19 pandemic narratives on facemasks as the art of disaster governance: a semiotic and biopolitical survey

In China and around the world, the global spread of COVID-19 has made wearing a facemask more than a pragmatic or aesthetic individual-level issue: it has instilled in people deontic value. In Chinese anti-epidemic narratives, the semiotic ideology of wearing a facemask has been closely related to collectivism, patriotism and, to a certain degree, nationalism. The facemask not only serves as a protective biomedical device but also as a cultural, political and spatial sign of the line of defence against disorders of the natural system, to establish the order of the social system. This paper argues from the perspective of semiotics and life politics that such mask narratives have effectively helped China prevent the large-scale spread of the epidemic across the nation and have served as a means of collective psychotherapy, paradoxically transforming individual separation into collective spiritual cohesion. Previous semiotic studies of disaster have not paid much attention to plagues or disaster governance discourse, between which biomedicine plays an important role. Thus, this paper aims to shed light on how biomedicine works with politics in coding and decoding the relationship between the natural system of the plague and the social system of governance.

A novel photoelectrochemical immunosensor based on TiO2@Bi2WO6 hollow microspheres and Ag2S for sensitive detection of SARS-COV-2 nucleocapsid protein.

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2) is a cluster of ß coronaviruses. The 2019 coronavirus disease (COVID-19) caused by SARS-COV-2 is emerging as a global pandemic. Thus, early diagnosis of SARS-COV-2 is essential to prevent severe outbreaks of the disease. In this experiment, a novel label-free photoelectrochemical (PEC) immunosensor was obtained based on silver sulfide (Ag2S) sensitized titanium dioxide@bismuth tungstate (TiO2@Bi2WO6) nanocomposite for quantitative detection of SARS-COV-2 nucleocapsid protein. The constructed TiO2@Bi2WO6 hollow microspheres had large specific surface area and could produce high photocurrent intensity under visible light illumination. Ag2S was in-situ grown on the surface of thioglycolic acid (TGA) modified TiO2@Bi2WO6. In particular, TiO2@Bi2WO6 and Ag2S formed a good energy level match, which could effectively enhance the photocurrent conversion efficiency and strength the photocurrent response. Ascorbic acid (AA) acted as an effective electron donor to effectively eliminate photogenerated holes. Under optimal experimental conditions, the constructed immunosensor presented a supersensitive response to SARS-COV-2 nucleocapsid protein, with a desirable linear relationship ranged from 0.001 to 50 ng/mL for nucleocapsid protein and a lower detection limit of 0.38 pg/mL. The fabricated sensor exhibited a wide linear range, excellent selectivity, specificity and stability, which provided a valuable referential idea for the detection of SARS-COV-2.

Twist-Net: A multi-modality transfer learning network with the hybrid bilateral encoder for hypopharyngeal cancer segmentation.

Hypopharyngeal cancer (HPC) is a rare disease. Therefore, it is a challenge to automatically segment HPC tumors and metastatic lymph nodes (HPC risk areas) from medical images with the small-scale dataset. Combining low-level details and high-level semantics from feature maps in different scales can improve the accuracy of segmentation. Herein, we propose a Multi-Modality Transfer Learning Network with Hybrid Bilateral Encoder (Twist-Net) for Hypopharyngeal Cancer Segmentation. Specifically, we propose a Bilateral Transition (BT) block and a Bilateral Gather (BG) block to twist (fuse) high-level semantic feature maps and low-level detailed feature maps. We design a block with multi-receptive field extraction capabilities, M Block, to capture multi-scale information. To avoid overfitting caused by the small scale of the dataset, we propose a transfer learning method that can transfer priors experience from large computer vision datasets to multi-modality medical imaging datasets. Compared with other methods, our method outperforms other methods on HPC dataset, achieving the highest Dice of 82.98%. Our method is also superior to other methods on two public medical segmentation datasets, i.e., the CHASE_DB1 dataset and BraTS2018 dataset. On these two datasets, the Dice of our method is 79.83% and 84.87%, respectively. The code is available at: https://github.com/zhongqiu1245/TwistNet.

Long-term passaging of pseudo-typed SARS-CoV-2 reveals the breadth of monoclonal and bispecific antibody cocktails.

The continuous emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants poses challenges to the effectiveness of neutralizing antibodies. Rational design of antibody cocktails is a realizable approach addressing viral immune evasion. However, evaluating the breadth of antibody cocktails is essential for understanding the development potential. Here, based on a replication competent vesicular stomatitis virus model that incorporates the spike of SARS-CoV-2 (VSV-SARS-CoV-2), we evaluated the breadth of a number of antibody cocktails consisting of monoclonal antibodies and bispecific antibodies by long-term passaging the virus in the presence of the cocktails. Results from over two-month passaging of the virus showed that 9E12 + 10D4 + 2G1 and 7B9-9D11 + 2G1 from these cocktails were highly resistant to random mutation, and there was no breakthrough after 30 rounds of passaging. As a control, antibody REGN10933 was broken through in the third passage. Next generation sequencing was performed and several critical mutations related to viral evasion were identified. These mutations caused a decrease in neutralization efficiency, but the reduced replication rate and ACE2 susceptibility of the mutant virus suggested that they might not have the potential to become epidemic strains. The 9E12 + 10D4 + 2G1 and 7B9-9D11 + 2G1 cocktails that picked from the VSV-SARS-CoV-2 system efficiently neutralized all current variants of concern and variants of interest including the most recent variants Delta and Omicron, as well as SARS-CoV-1. Our results highlight the feasibility of using the VSV-SARS-CoV-2 system to develop SARS-CoV-2 antibody cocktails and provide a reference for the clinical selection of therapeutic strategies to address the mutational escape of SARS-CoV-2.

Associations Between the Perceived Severity of the COVID-19 Pandemic, Cyberchondria, Depression, Anxiety, Stress, and Lockdown Experience: Cross-sectional Survey Study.

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.

[Changes in the disease spectrum in the pediatric intensive care units within 2 years before and after the outbreak of coronavirus disease 2019]. / æ–°åž‹å† çŠ¶ç— æ¯’è‚ºç‚Žç–«æƒ å‰åŽ2å¹´å„¿ç«¥é‡ç—‡ç›‘æŠ¤ç— æˆ¿ç–¾ç— è°±çš„å˜åŒ–.

OBJECTIVES: To investigate the changes in the disease spectrum among hospitalized children in the pediatric intensive care units (PICU) within 2 years before and after the outbreak of coronavirus disease 2019 (COVID-19). METHODS: The related data on disease diagnosis were collected from all children who were hospitalized in the PICU of Affiliated Hospital of Jining Medical College from January 2018 to December 2019 (pre-COVID-19 group) and from January 2020 to December 2021 (post-COVID-19 group). A statistical analysis was performed for the disease spectrum of the two groups. RESULTS: There were 2 368 children in the pre-COVID-19 group and 1 653 children in the post-COVID-19 group. The number of children in the post-COVID-19 group was reduced by 30.19% compared with that in the pre-COVID-19 group. There was a significant difference in age composition between the two groups (P<0.05). The top 10 diseases in the pre-COVID-19 group by number of cases were respiratory diseases, neurological diseases, sepsis, critical illness, circulatory system diseases, severe neurosurgical diseases, digestive system diseases, unintentional injuries, endocrine system diseases, and tumors. The top 10 diseases in the post-COVID-19 group by number of cases were respiratory diseases, neurological diseases, sepsis, circulatory system diseases, unintentional injuries, endocrine system diseases, severe neurosurgical diseases, acute abdomen, trauma surgical diseases, and digestive system diseases. The proportions of respiratory diseases, critical illness and severe neurosurgical diseases in the post-COVID-19 group were lower than those in the pre-COVID-19 group (P<0.05), while the proportions of unintentional injuries, acute abdomen, endocrine system diseases, trauma surgical diseases and sepsis were higher than those in the pre-COVID-19 group (P<0.05). CONCLUSIONS: COVID-19 epidemic has led to a significant reduction in the number of children admitted to the PICU, and there are significant changes in the disease spectrum within 2 years before and after the outbreak of COVID-19. Relevant prevention and control measures taken during the COVID-19 epidemic can reduce the incidence of respiratory diseases, neurological diseases, and other critical illness in children, but it is necessary to strengthen the prevention of unintentional injuries and chronic disease management during the epidemic.

Cell transcriptomic atlas of the non-human primate Macaca fascicularis.

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.

Genome-wide analyses reveal the detrimental impacts of SARS-CoV-2 viral gene Orf9c on human pluripotent stem cell-derived cardiomyocytes.

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.

Chromosome-Scale Genome of Masked Palm Civet (Paguma larvata) Shows Genomic Signatures of Its Biological Characteristics and Evolution.

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.

Single cell atlas for 11 non-model mammals, reptiles and birds.

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.

Early Fever Is Associated With Clinical Outcomes in Patients With Coronavirus Disease.

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.

Factors Associated with the Expression of ACE2 in Human Lung Tissue: Pathological Evidence from Patients with Normal FEV1 and FEV1/FVC.

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.

Small-Airway Function Variables in Spirometry, Fractional Exhaled Nitric Oxide, and Circulating Eosinophils Predicted Airway Hyperresponsiveness in Patients with Mild Asthma.

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.

Clinical features in coronavirus disease 2019 (COVID-19) patients with early clearance and prolonged shedding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA.

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.

The impacts of import-side changes in grain supply on China's food security

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".

The role of peripheral blood eosinophil counts in COVID-19 patients.

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.

A Multimodality Machine Learning Approach to Differentiate Severe and Nonsevere COVID-19: Model Development and Validation.

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.

Accurately Differentiating Between Patients With COVID-19, Patients With Other Viral Infections, and Healthy Individuals: Multimodal Late Fusion Learning Approach.

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.

Six-month follow-up of functional status in discharged patients with coronavirus disease 2019 (preprint)

Background The long-term functional outcome of discharged patients with coronavirus disease 2019 (COVID-19) remains unresolved. We aimed to describe a six-month follow-up of functional status of COVID-19 survivors.Methods We reviewed the data of COVID-19 patients who had been consecutively admitted to the Tumor Center of Union Hospital (Wuhan, China) between 15 February and 14 March 2020. We quantified a six-month functional outcome reflecting symptoms and disability in COVID-19 survivors using a post-COVID-19 functional status scale ranging from 0 to 5 (PCFS). We examined the risk factors for the incomplete functional status defined as a PCFS > 0 at a six-month follow-up after discharge.Results We included a total of 95 COVID-19 survivors with a median age of 62 (IQR 53-69) who had a complete functional status (PCFS grade 0) at baseline in this retrospective observational study. At six-month follow-up, 67 (70.5%) patients had a complete functional outcome (grade 0), 9 (9.5%) had a negligible limited function (grade 1), 12 (12.6%) had a mild limited function (grade 2), 7 (7.4%) had moderate limited function (grade 3). Univariable logistic regression analysis showed a significant association between the onset symptoms of muscle or joint pain and an increased risk of incomplete function (unadjusted OR 4.06, 95%CI 1.33 - 12.37). This association remained after adjustment for age and admission delay (adjusted OR 3.39, 95%CI 1.06 - 10.81, p = 0.039).Conclusions A small proportion of discharged COVID-19 patients may have an incomplete functional outcome at a six-month follow-up; intervention strategies are required.