Epidemiology, diagnosis and treatment of early COVID-19 patients in Jiaxing City, Zhejiang Province, China, January to March 2020.

BACKGROUND: Imported Coronavirus disease 2019 (COVID-19) patients pose a huge challenge to the prevention and control of the epidemic in prefecture-level cities in China. However, the treatment strategies at that time were mainly empirical and far from perfect. Hence, this study aims to summarize the clinical characteristics, diagnosis and treatment of all COVID-19 patients in Jiaxing City in 2020. METHODS: The clinical data of 42 patients diagnosed with COVID-19 in Jiaxing City, Zhejiang Province from January 23, 2020 to March 4, 2020 were retrospectively analyzed. Epidemiological history and sociodemographic data were collected. Laboratory parameters, imaging and disease progression, treatment methods, efficacy and adverse reactions of COVID-19 cases were recorded. Then, the clinical characteristics as well as diagnosis and treatment were statistically analyzed. RESULTS: The median age of 42 patients was 47 years old, including 24 males (57.1%) and 18 females (42.9%). There were 21 first-generation cases, 29 cases (69%) of clustering onset related to first-generation cases, and 28 cases without any underlying diseases. Radiographic progression was reported in 17 patients (40.5%) (progression duration, 2-11 days; median progression duration, 3.8 days; average progression duration, 4.59-2.48 days). The main clinical symptoms include fever (78.6%) and cough (64.3%). A total of 37 patients (88.10%) received arbidol combined with lopinavir/ritonavir or darunavir/cobicistat. Of these, 22 patients (52.4%) took a combination with moxifloxacin tablets, and 20 patients (47.6%) took combined hormone therapy. Seventeen patients (40.48%) reported diarrhea, nausea, vomiting, rash, and other adverse drug reactions. A total of 38 patients improved (90.5%). The hospital stays of 36 patients ranged from 7 to 33 days, with a median of 19 days (19.00-7.33 days on average). The virus nucleic acid test result return time was 1 to 32 days, with a median of 15.5 days (14.41-8.61 days on average). CONCLUSIONS: Most of the imported COVID-19 patients in Jiaxing City were of the first generation, mainly cluster onset, and the epidemiological characteristics were relatively simple. Arbidol combined with lopinavir/ritonavir or darunavir/cobicistat was the main treatment strategy for the initial treatment of COVID-19.

Excessive Gaming and Online Energy-Drink Marketing Exposure Associated with Energy-Drink Consumption among Adolescents.

In this study, we examined excessive online gaming by adolescents and the resultant effects of their exposure to the online marketing of energy drinks and alcohol, and whether marketing literacy could serve as a mitigating factor. This cross-sectional study was conducted in 2020. Data were obtained from a sample of 2613 seventh-grade students from 30 middle schools in Taiwan. A self-administered questionnaire was conducted. The results showed that nearly 18% of the adolescent respondents had used energy drinks, while 75% reported seeing energy-drink advertisements on the internet in the past year. Multiple regression results indicated that factors such as being male, reporting excessive gaming, being exposed to higher levels of online energy-drink marketing, and reporting alcohol use were positively associated with energy-drink consumption. A higher level of online energy-drink marketing-affective literacy, however, was negatively associated with energy-drink consumption. In conclusion, factors that predicted energy-drink consumption among adolescents included excessive gaming and exposure to online energy-drink marketing, but marketing-affective literacy tended to lessen the impact of such advertising.

Using an untargeted metabolomics approach to analyze serum metabolites in COVID-19 patients with nucleic acid turning negative.

Background: The coronavirus disease of 2019 (COVID-19) is a severe public health issue that has infected millions of people. The effective prevention and control of COVID-19 has resulted in a considerable increase in the number of cured cases. However, little research has been done on a complete metabonomic examination of metabolic alterations in COVID-19 patients following treatment. The current project pursues rigorously to characterize the variation of serum metabolites between healthy controls and COVID-19 patients with nucleic acid turning negative via untargeted metabolomics. Methods: The metabolic difference between 20 COVID-19 patients (CT ≥ 35) and 20 healthy controls were investigated utilizing untargeted metabolomics analysis employing High-resolution UHPLC-MS/MS. COVID-19 patients' fundamental clinical indicators, as well as health controls, were also collected. Results: Out of the 714 metabolites identified, 203 still significantly differed between COVID-19 patients and healthy controls, including multiple amino acids, fatty acids, and glycerophospholipids. The clinical indexes including monocytes, lymphocytes, albumin concentration, total bilirubin and direct bilirubin have also differed between our two groups of participators. Conclusion: Our results clearly showed that in COVID-19 patients with nucleic acid turning negative, their metabolism was still dysregulated in amino acid metabolism and lipid metabolism, which could be the mechanism of long-COVID and calls for specific post-treatment care to help COVID-19 patients recover.

Analysis of blood samples from 42 patients with varying degrees of infection during the epidemic of COVID-19: a retrospective study.

BACKGROUND: Blood samples from 42 patients with coronavirus disease 2019 (COVID-19) with varying degrees of infection were examined to further explore the relationship between clinical features, immune factors and COVID-19, as well as the diagnostic and predictive values of clinical features and immune factors in severe disease progression. METHODS: This study included 42 nucleic acid-positive COVID-19 patients admitted to the First Hospital of Jiaxing from January 26, 2020 to February 21, 2020, who were divided into mild-moderate group and severe group based on respiratory rate, resting oxygen saturation and alveolar oxygen partial pressure/O2 inhalation. On February 21, 2020, clinical data including sex, age, body mass index (BMI), past medical history, clinical symptoms, hematology indexes [white blood cell (WBC); neutrophil (NEUT); lymphocyte (LYM); C-reactive protein (CRP)] were collected. The chi-square test was used to compare the clinical data differences between the two groups, so as to perform comparative analysis in the context of serious disease development. RESULTS: There were 8 cases of severe disease, and 34 cases of mild and moderate symptoms. Comparative analysis showed that patients with advanced age (≥60 years, OR =5.800, P=0.0286), history of hypertension (OR =5.800, P=0.0286) and pulmonary lobe lesions (≥4, OR =6.273, P=0.0270) were more likely to develop serious diseases. In addition, according to clinical symptoms, chest pain was more prominent in patients with severe disease. Laboratory tests showed that levels of WBC (severe 4.96±1.76 vs. mild-moderate 5.45±2.01, P=0.5300), NEUT (severe 3.56±1.44 vs. mild-moderate 3.94±1.87, P=0.5945) and LYM (severe 0.91±0.25 vs. mild-moderate 1.11±0.51, P=0.2903) were normal or decreased, but CRP level (severe 31.03±9.38 vs. mild-moderate 12.53±15.73, P=0.0029) was obviously increased, especially in patients with severe disease, with statistically significant difference between groups. CONCLUSIONS: Patients with hypertension and advanced age are more likely to develop deteriorate with COVID-19, and the number of lung lobes with lesions and chest pain may indicate disease progression. Notably, CRP level is significantly elevated in severe disease and it may be closely related to COVID-19 progression.

XGBoost-Based Feature Learning Method for Mining COVID-19 Novel Diagnostic Markers.

In December 2019, an outbreak of novel coronavirus pneumonia spread over Wuhan, Hubei Province, China, which then developed into a significant global health public event, giving rise to substantial economic losses. We downloaded throat swab expression profiling data of COVID-19 positive and negative patients from the Gene Expression Omnibus (GEO) database to mine novel diagnostic biomarkers. XGBoost was used to construct the model and select feature genes. Subsequently, we constructed COVID-19 classifiers such as MARS, KNN, SVM, MIL, and RF using machine learning methods. We selected the KNN classifier with the optimal MCC value from these classifiers using the IFS method to identify 24 feature genes. Finally, we used principal component analysis to classify the samples and found that the 24 feature genes could effectively be used to classify COVID-19-positive and negative patients. Additionally, we analyzed the possible biological functions and signaling pathways in which the 24 feature genes were involved by GO and KEGG enrichment analyses. The results demonstrated that these feature genes were primarily enriched in biological functions such as viral transcription and viral gene expression and pathways such as Coronavirus disease-COVID-19. In summary, the 24 feature genes we identified were highly effective in classifying COVID-19 positive and negative patients, which could serve as novel markers for COVID-19.

Screening of Gene Expression Markers for Corona Virus Disease 2019 Through Boruta_MCFS Feature Selection.

Since the first report of SARS-CoV-2 virus in Wuhan, China in December 2019, a global outbreak of Corona Virus Disease 2019 (COVID-19) pandemic has been aroused. In the prevention of this disease, accurate diagnosis of COVID-19 is the center of the problem. However, due to the limitation of detection technology, the test results are impossible to be totally free from pseudo-positive or -negative. Improving the precision of the test results asks for the identification of more biomarkers for COVID-19. On the basis of the expression data of COVID-19 positive and negative samples, we first screened the feature genes through ReliefF, minimal-redundancy-maximum-relevancy, and Boruta_MCFS methods. Thereafter, 36 optimal feature genes were selected through incremental feature selection method based on the random forest classifier, and the enriched biological functions and signaling pathways were revealed by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes. Also, protein-protein interaction network analysis was performed on these feature genes, and the enriched biological functions and signaling pathways of main submodules were analyzed. In addition, whether these 36 feature genes could effectively distinguish positive samples from the negative ones was verified by dimensionality reduction analysis. According to the results, we inferred that the 36 feature genes selected via Boruta_MCFS could be deemed as biomarkers in COVID-19.

Efficacy and safety of glucocorticoids in treatment of COVID-19: a retrospective study.

BACKGROUND: Novel coronavirus pneumonia is a novel kind of highly contagious disease without any specific drugs. Considering the successful experience of antiviral therapy combined with glucocorticoids (GCs) in severe acute respiratory syndrome, this study was designed to evaluate the clinical efficacy of GCs in treating patients with coronavirus disease 2019 (COVID-19). METHODS: A cohort of 42 patients with COVID-19 admitted to The First Hospital of Jiaxing from January 4, 2020, to February 16, 2020, were included and grouped into a test group (n=20) and control group (n=22) based on their therapeutic regimens. There were no significant differences in baseline characteristics between patients in the two groups. Conventional treatment (antiviral therapy) was given to patients in both groups, while an additional hormone drug (GCs) was used in patients in the test group. Indices including body temperature, blood routine indices [white blood cell (WBC), lymphocyte, monocyte, and C-reactive protein (CRP)], blood biochemical indices [alanine aminotransferase (ALT) and aspartate aminotransferase (AST)], and complications were recorded during the treatment. Time to achieve negative virus nucleic acid (nCoV-RNA) testing, and hospital stays were also observed and compared between the two groups. RESULTS: All included patients completed the trial. After treatment, superior therapeutic efficacy was achieved in patients in the test group, with body temperature dropping more significantly with a much shorter recovery time compared to the control group (P=0.0412). Simultaneously, the percentage of patients with abnormal blood routine indices (WBC), monocyte, and (CRP) in the test group was reduced more sharply, while no noticeable difference was observed in the number of patients who developed abnormal blood biochemical indices during treatment between the two groups. Additionally, a shorter duration of hospital stays was found in the test group relative to the control group (14.84±8.76 vs. 18.25±7.42 days, P>0.05). Patients who received GCs had a shorter recovery time for body temperature and inflammation. CONCLUSIONS: Hormonotherapy with GCs can accelerate the recovery time for body temperature as well as inflammation in patients with COVID-19. It deserves promotion and application in the clinical treatment of coronavirus disease as a form of adjuvant medicine. The ongoing focus of research is on long-term adverse events in GCs.

[Five patients with novel coronavirus Omicron variant of concern infection].

OBJECTIVE: To analyze clinical characteristics of patients with novel coronavirus Omicron variant of concern infection, and to provide practical data and experience for subsequent clinical treatment. METHODS: A retrospective analysis was performed for the clinical data of 5 cases with novel coronavirus Omicron variant of concern infection treated in the First Hospital of Jiaxing from December 18, 2021 to January 28, 2022. The patients' clinical data were recorded, including gender, age, length of hospital stay, vaccination status, clinical symptoms, laboratory indicators [white blood cell count (WBC), lymphocyte count (LYM), eosinophil count (EOS), hypersensitivity C-reactive protein (hs-CRP), novel coronavirus antibody immunoglobulin (IgG and IgM)], chest CT, treatment course and disease outcome. RESULTS: All 5 patients were male, aged 24-37 years old. Four patients were vaccinated with novel coronavirus vaccine (one patient received 3 doses of the vaccine and 3 patients received only the first 2 doses of the vaccine), and no infection was found in chest CT. Laboratory examination showed that WBC, LYM, EOS and hs-CRP levels were normal, and only showed mild symptoms of upper respiratory tract infection. One patient was not vaccinated with novel coronavirus vaccine, and signs of viral pneumonia could be seen in chest CT, laboratory examination showed that WBC and hs-CRP levels increased, suggesting that bacterial infection, fever, cough, sputum and other respiratory symptoms were obvious, and the treatment time was long. All 5 patients were treated with Chinese medicine Lotus antipyretic and Baihu Yinqiao decoction based on routine antiviral therapy. CONCLUSIONS: Patients with novel coronavirus Omicron variant of concern infection vaccinated with the novel coronavirus vaccine have milder clinical symptoms, with less obvious chest CT findings and faster recovery. Chinese medicine Lotus antipyretic and Baihu Yinqiao decoction has obvious therapeutic effect on such patients.

Mortality in patients with COVID-19 requiring extracorporeal membrane oxygenation: A meta-analysis.

BACKGROUND: Coronavirus disease 2019 (COVID-19) has become a worldwide pandemic and significant public health issue. The effectiveness of extracorporeal membrane oxygenation (ECMO) in treating COVID-19 patients has been called into question. AIM: To conduct a meta-analysis on the mortality of COVID-19 patients who require ECMO. METHODS: This analysis adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyzes 2020 (PRISMA) and has been registered at the International Prospective Register of Systematic Reviews (number CRD42020227414). A quality assessment for all the included articles was performed by the Newcastle-Ottawa Scale (NOS). Studies with tenor more COVID-19 patients undergoing ECMO were included. The random-effects model was used to obtain the pooled incidence of mortality in COVID-19 patients receiving ECMO. The source of heterogeneity was investigated using subgroup and sensitivity analyses. RESULTS: We identified 18 articles with 1494 COVID-19 patients who were receiving ECMO. The score of the quality assessment ranged from 5 to 8 on the NOS. The majority of patients received veno-venous ECMO (93.7%). Overall mortality was estimated to be 0.31 [95% confidence interval (CI): 0.24-0.39; I 2 = 84.8%] based on random-effect pooled estimates. There were significant differences in mortality between location groups (33.0% vs 55.0% vs 37.0% vs 18.0%, P < 0.001), setting groups (28.0% vs 34.0%, P < 0.001), sample size (37.0% vs 31.0%, P < 0.001), and NOS groups (39.0% vs 19.0%, P < 0.001). However, both subgroup analyses based on location, setting, and sample size, and sensitivity analysis failed to identify the source of heterogeneity. The funnel plot indicated no evident asymmetry, and the Egger's (P = 0.95) and Begg's (P = 0.14) tests also revealed no significant publication bias. CONCLUSION: With more resource assessment and risk-benefit analysis, our data reveal that ECMO might be a feasible and effective treatment for COVID-19 patients.

Development and validation of a clinical prediction model to estimate the risk of critical patients with COVID-19.

The outbreak of coronavirus disease 2019 (COVID-19) has globally strained medical resources and caused significant mortality. This study was aimed to develop and validate a prediction model based on clinical features to estimate the risk of patients with COVID-19 at admission progressing to critical patients. Patients admitted to the hospital between January 16, 2020, and March 10, 2020, were retrospectively enrolled, and they were observed for at least 14 days after admission to determine whether they developed into severe pneumonia. According to the clinical symptoms, all patients were divided into four groups: mild, normal, severe, and critical. A total of 390 patients with COVID-19 pneumonia were identified, including 212 severe patients and 178 nonsevere patients. The least absolute shrinkage and selection operator (LASSO) regression reduced the variables in the model to 6, which are age, number of comorbidities, computed tomography severity score, lymphocyte count, aspartate aminotransferase, and albumin. The area under curve of the model in the training set is 0.898, and the specificity and sensitivity were 89.7% and 75.5%. The prediction model, nomogram might be useful to access the onset of severe and critical illness among COVID-19 patients at admission, which is instructive for clinical diagnosis.

The application research of AI image recognition and processing technology in the early diagnosis of the COVID-19.

BACKGROUND: This study intends to establish a combined prediction model that integrates the clinical symptoms,the lung lesion volume, and the radiomics features of patients with COVID-19, resulting in a new model to predict the severity of COVID-19. METHODS: The clinical data of 386 patients with COVID-19 at several hospitals, as well as images of certain patients during their hospitalization, were collected retrospectively to create a database of patients with COVID-19 pneumonia. The contour of lungs and lesion locations may be retrieved from CT scans using a CT-image-based quantitative discrimination and trend analysis method for COVID-19 and the Mask R-CNN deep neural network model to create 3D data of lung lesions. The quantitative COVID-19 factors were then determined, on which the diagnosis of the development of the patients' symptoms could be established. Then, using an artificial neural network, a prediction model of the severity of COVID-19 was constructed by combining characteristic imaging features on CT slices with clinical factors. ANN neural network was used for training, and tenfold cross-validation was used to verify the prediction model. The diagnostic performance of this model is verified by the receiver operating characteristic (ROC) curve. RESULTS: CT radiomics features extraction and analysis based on a deep neural network can detect COVID-19 patients with an 86% sensitivity and an 85% specificity. According to the ROC curve, the constructed severity prediction model indicates that the AUC of patients with severe COVID-19 is 0.761, with sensitivity and specificity of 79.1% and 73.1%, respectively. CONCLUSIONS: The combined prediction model for severe COVID-19 pneumonia, which is based on deep learning and integrates clinical aspects, pulmonary lesion volume, and radiomics features of patients, has a remarkable differential ability for predicting the course of disease in COVID-19 patients. This may assist in the early prevention of severe COVID-19 symptoms.

Correlation Analysis between the Viral Load and the Progression of COVID-19.

OBJECTIVES: This study is aimed at exploring the relationship of the viral load of coronavirus disease 2019 (COVID-19) with lymphocyte count, neutrophil count, and C-reactive protein (CRP) and investigating the dynamic change of patients' viral load during the conversion from mild COVID-19 to severe COVID-19, so as to clarify the correlation between the viral load and progression of COVID-19. METHODS: This paper included 38 COVID-19 patients admitted to the First Hospital of Jiaxing from January 28, 2020, to March 6, 2020, and they were clinically classified according to the Guidelines on the Novel Coronavirus-Infected Pneumonia Diagnosis and Treatment. According to the instructions of the Nucleic Acid Detection Kit for the 2019 novel coronavirus (SARS-CoV-2), respiratory tract specimens (throat swabs) were collected from patients for nucleic acid testing. Patients' lymphocyte count and neutrophil count were determined by blood routine examination, and CRP was measured by biochemical test. RESULTS: The results of our study suggested that the cycle threshold (Ct) value of Nucleocapsid protein (N) gene examined by nucleic acid test was markedly positively correlated with lymphocyte count (p = 0.0445, R 2 = 0.1203), but negatively correlated with neutrophil count (p = 0.0446, R 2 = 0.1167) and CRP (p = 0.0393, R 2 = 0.1261), which indicated that patients with a higher viral load tended to have lower lymphocyte count but higher neutrophil count and CRP. Additionally, we detected the dynamic change of Ct value in patients who developed into a severe case, finding that viral load of 3 patients increased before disease progression, whereas this phenomenon was not found in 2 patients with underlying diseases. CONCLUSION: The results of this study demonstrated that viral load of SARS-CoV-2 is significantly negatively correlated with lymphocyte count, but markedly positively correlated with neutrophil count and CRP. The rise of viral load is very likely to be the key factor leading to the overloading of the body's immune response and resulting in the disease progression into severe disease.

Bioinformatics and system biology approach to identify the influences of SARS-CoV-2 infections to idiopathic pulmonary fibrosis and chronic obstructive pulmonary disease patients.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), better known as COVID-19, has become a current threat to humanity. The second wave of the SARS-CoV-2 virus has hit many countries, and the confirmed COVID-19 cases are quickly spreading. Therefore, the epidemic is still passing the terrible stage. Having idiopathic pulmonary fibrosis (IPF) and chronic obstructive pulmonary disease (COPD) are the risk factors of the COVID-19, but the molecular mechanisms that underlie IPF, COPD, and CVOID-19 are not well understood. Therefore, we implemented transcriptomic analysis to detect common pathways and molecular biomarkers in IPF, COPD, and COVID-19 that help understand the linkage of SARS-CoV-2 to the IPF and COPD patients. Here, three RNA-seq datasets (GSE147507, GSE52463, and GSE57148) from Gene Expression Omnibus (GEO) is employed to detect mutual differentially expressed genes (DEGs) for IPF, and COPD patients with the COVID-19 infection for finding shared pathways and candidate drugs. A total of 65 common DEGs among these three datasets were identified. Various combinatorial statistical methods and bioinformatics tools were used to build the protein-protein interaction (PPI) and then identified Hub genes and essential modules from this PPI network. Moreover, we performed functional analysis under ontologies terms and pathway analysis and found that IPF and COPD have some shared links to the progression of COVID-19 infection. Transcription factors-genes interaction, protein-drug interactions, and DEGs-miRNAs coregulatory network with common DEGs also identified on the datasets. We think that the candidate drugs obtained by this study might be helpful for effective therapeutic in COVID-19.

PreDTIs: prediction of drug-target interactions based on multiple feature information using gradient boosting framework with data balancing and feature selection techniques.

Discovering drug-target (protein) interactions (DTIs) is of great significance for researching and developing novel drugs, having a tremendous advantage to pharmaceutical industries and patients. However, the prediction of DTIs using wet-lab experimental methods is generally expensive and time-consuming. Therefore, different machine learning-based methods have been developed for this purpose, but there are still substantial unknown interactions needed to discover. Furthermore, data imbalance and feature dimensionality problems are a critical challenge in drug-target datasets, which can decrease the classifier performances that have not been significantly addressed yet. This paper proposed a novel drug-target interaction prediction method called PreDTIs. First, the feature vectors of the protein sequence are extracted by the pseudo-position-specific scoring matrix (PsePSSM), dipeptide composition (DC) and pseudo amino acid composition (PseAAC); and the drug is encoded with MACCS substructure fingerings. Besides, we propose a FastUS algorithm to handle the class imbalance problem and also develop a MoIFS algorithm to remove the irrelevant and redundant features for getting the best optimal features. Finally, balanced and optimal features are provided to the LightGBM Classifier to identify DTIs, and the 5-fold CV validation test method was applied to evaluate the prediction ability of the proposed method. Prediction results indicate that the proposed model PreDTIs is significantly superior to other existing methods in predicting DTIs, and our model could be used to discover new drugs for unknown disorders or infections, such as for the coronavirus disease 2019 using existing drugs compounds and severe acute respiratory syndrome coronavirus 2 protein sequences.

A study on clinical effect of Arbidol combined with adjuvant therapy on COVID-19.

This study aims to explore the clinical effect of Arbidol (ARB) combined with adjuvant therapy on patients with coronavirus disease 2019 (COVID-19). The study included 62 patients with COVID-19 admitted to the First Hospital of Jiaxing from January to March 2020, and all patients were divided into the test group and the control group according to whether they received ARB during hospitalization. Various indexes in the two groups before and after treatment were observed and recorded, including fever, cough, hypodynamia, nasal obstruction, nasal discharge, diarrhea, C-reactive protein (CRP), procalcitonin (PCT), blood routine indexes, blood biochemical indexes, time to achieve negative virus nucleic acid, and so on. The fever and cough in the test group were relieved markedly faster than those in the control group (P < .05); there was no obvious difference between the two groups concerning the percentage of patients with abnormal CRP, PCT, blood routine indexes, aspartate aminotransferase, and alanine aminotransferase (P > .05); the time for two consecutive negative nucleic acid tests in the test group were shorter than that in the control group; the hospitalization period of the patients in the test group and control group were (16.5 ± 7.14) days and (18.55 ± 7.52) days, respectively. ARB combined with adjuvant therapy might be able to relieve the fever of COVID-19 sufferers faster and accelerate the cure time to some degree, hence it's recommended for further research clinically.