Network Public Opinion Detection During the Coronavirus Pandemic: A Short-Text Relational Topic Model

Online social media provides rich and varied information reflecting the significant concerns of the public during the coronavirus pandemic. Analyzing what the public is concerned with from social media information can support policy-makers to maintain the stability of the social economy and life of the society. In this article, we focus on the detection of the network public opinions during the coronavirus pandemic. We propose a novel Relational Topic Model for Short texts (RTMS) to draw opinion topics from social media data. RTMS exploits the feature of texts in online social media and the opinion propagation patterns among individuals. Moreover, a dynamic version of RTMS (DRTMS) is proposed to capture the evolution of public opinions. Our experiment is conducted on a real-world dataset which includes 67,592 comments from 14,992 users. The results demonstrate that, compared with the benchmark methods, the proposed RTMS and DRTMS models can detect meaningful public opinions by leveraging the feature of social media data. It can also effectively capture the evolution of public concerns during different phases of the coronavirus pandemic. © 2021 Association for Computing Machinery.

Identification and development of a five-gene signature to improve the prediction of mechanical ventilator-free days for patients with COVID-19.

OBJECTIVE: Coronavirus disease 2019 (COVID-19) is a highly contagious infectious disease caused by the newly discovered severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Severe COVID-19 infection causes complications in the respiratory tract, which results in pulmonary failure, thus requiring prolonged mechanical ventilation (MV). An increase in the number of patients with COVID-19 poses numerous challenges to the healthcare system, including the shortage of MV facilities. Despite continued efforts to improve COVID-19 diagnosis and treatment, no study has established a reliable predictive model for the risk assessment of deteriorating COVID-19 cases. MATERIALS AND METHODS: We extracted the expression profiles and clinical data of the GSE157103, GSE116560 and GSE21802 cohorts from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified as the intersection of the resulting differential genes as analysed via limma, edgeR and DESeq2 R packages. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed using the R package 'clusterProfiler'. Variables closely related to MV were examined using univariate Cox regression analysis, and significant variables were subjected to least absolute shrinkage and selection operator regression (LASSO) analysis for the construction of a risk model. Kaplan-Meier analysis and receiver operating characteristic (ROC) curves were generated to verify the predictive values of the risk model. RESULTS: We identified 198 unigenes that were differentially expressed in COVID-19 samples. Moreover, a five-gene signature (BTN3A1, GPR35, HAAO, SLC2A6 and TEX2) was constructed to predict the ventilator-free days of patients with COVID-19. In our study, we used the five-gene signature to calculate the risk score (MV score) for each patient. The results revealed a statistical correlation between the MV score and the scores of the Acute Physiology and Chronic Health Evaluation and Sequential Organ Failure Assessment of patients with COVID-19. Kaplan-Meier analysis revealed that the number of ventilator-free days was significantly reduced in the low-MVscore group compared to the high-MVscore group. The ROC curves revealed that our model had a good performance, and the areas under the ROC curve were 0.93 (3-week ROC) and 0.97 (4-week ROC). The 'Limma' package analysis revealed 71 upregulated genes and 59 downregulated genes in the high-MV score group compared to the low-MV score group. These DEGs were mainly enriched in cytokine signalling in immune system and cellular response to cytokine stimulus. CONCLUSIONS: This study identified a five-gene signature that can predict the length of ventilator-free days for patients with COVID-19.

[Analysis of non-bacterial respiratory pathogens in children in Ningbo City from 2019 to 2021].

Objective: To investigate the distribution characteristics of respiratory non-bacterial pathogens in children in Ningbo from 2019 to 2021. Methods: A retrospective analysis was performed on 23 733 children with respiratory tract infection who visited the department of pediatrics of Ningbo Women and Children's Hospital from July 2019 to December 2021. There were 13 509 males (56.92%) and 10 224 females (43.08%), with an age range of 1 day to 18 years old. There were 981 cases in the neonatal group (younger than 1 month old), 5 880 cases in the infant group (1 month to younger than 1 year old), 6 552 cases in the toddler group (1 to younger than 3 years old), 7 638 cases in the preschool group (3 to younger than 7 years old), and 2 682 cases in the school-age group (7 to 18 years old). Thirteen respiratory pathogens were detected by multiple polymerase chain reaction (PCR) based on capillary electrophoresis, and SPSS 23.0 software was used for statistical analysis of the results, the count data were expressed as percentages, and the χ2 test was used for comparison between groups. Results: Of the 23 733 specimens, 13 330 were positive for respiratory pathogens, with a total positive rate of 56.17%. The positive rates of human rhinovirus (HRV) 24.05% (5 707/23 733), human respiratory syncytial virus (HRSV) 10.45% (2 480/2 3733) and mycoplasma pneumoniae (Mp) 7.03% (1 668/23 733) were in the first three. The positive rates of pathogens in the male and female children were 57.47% (7 763/13 509) and 54.45% (5 567/10 224), respectively, and the difference was statistically significant (χ2=21.488, P<0.001). The positive rates in the neonatal group, infant group, toddler group, preschool group, and school-age group were 31.80% (312/981), 54.71% (3 217/5 880), 63.23% (4 143/6 552), 59.83% (4 570/7 638), 40.57% (1 088/2 682), respectively, and the difference among the groups was statistically significant (χ2=681.225, P<0.001). The single infection rate was 47.43% (11 256/23 733), the mixed infection rate of two or more pathogens was 8.74% (2 074/23 733), most of which were mixed infections of two pathogens. HRV, HADV, HCOV, Ch disseminated in the whole year. HRSV, HMPV, Boca, HPIV occurred mostly in fall and winter. The positive rates of FluA, FluB, Mp were at a low level after the corona virus disease 2019 (COVID-19) epidemic (2020 and 2021). The positive rates of FluA, H1N1, H3N2, FluB, HADV, Mp in 2020 were significantly lower than in 2019 (P<0.05). The positive rates of HPIV, HRV, HCOV, Ch in 2020 were significantly higher than in 2019 (P<0.05). The positive rates of FluA, H1N1, H3N2, HPIV, HCOV, Mp, Ch in 2021 were significantly lower than in 2020 (P<0.05). The positive rates of Boca, HMPV, HRSV in 2021 were significantly higher than in 2020 (P<0.05). Conclusion: From 2019 to 2021, the main non-bacterial respiratory pathogens of children in Ningbo City were Mp and HRV, and the detection rates of respiratory pathogens varied among different ages, seasons and genders.

[Psychological and behavioral functioning of children and adolescents during long-term home-schooling].

Objective: To study the characteristics and risk factors of psychological and behavioral problems of children and adolescents of different ages and genders in long-term home-schooling during the coronavirus disease-2019 pandemic. Further, to provide scientific basis for more targeted psychological intervention and coping strategies in the future. Methods: A cross-sectional survey using an online questionnaire was conducted on students aged 6-16 years old in five representative cities of North (Beijing), East (Shanghai), West (Chongqing), South (Guangzhou) and Middle (Wuhan) in China. In this study, the social behavior and psychological abnormalities which was defined as the positive of any dimension were investigated in multiple dimensions during long-term home-schooling. The influencing factors of psycho-behavioral problems were analyzed by Logistic regression, and the confounding factors were corrected with graded multivariable adjustment. Results: A total of 6 906 valid questionnaires were collected including 3 592 boys and 3 314 girls, of whom 3 626 were children (6-11 years old) and 3 280 were adolescents (12-16 years old). The positive detection rate of psychosocial-behavioral problems were 13.0% (900/6 906) totally, 9.6% (344/3 592) in boys and 16.8% (556/3 314) in girls respectively, and 7.3%(142/1 946) in boys aged 6-11, 14.0%(235/1 680) in girls aged 6-11, 12.3%(202/1 646) in boys aged 12-16, 19.6%(321/1 634) in girls aged 12-16 respectively. There were significant differences between the psychological problems group and the non-psychological problems group in gender, parent-offspring conflict, number of close friends, family income change, sedentary time, homework time, screen exposure time, physical activity, dietary problems (χ²=78.851, 285.264, 52.839, 26.284, 22.778, 11.024, 10.688, 36.814, 70.982, all P<0.01). The most common symptoms in boys aged 6-11 years were compulsive activity, schizoid and depression, in girls aged 6-11 years were schizoid/compulsive activity, hyperactivity and social withdrawal, in boys aged 12-16 years were hyperactivity, compulsive activity and aggressive behavior, and in girls aged 12-16 years were schizoid, anxiety/compulsive activity and depression/withdrawal, respectively. After graded multivariable adjustment, besides the common risk factors, homework time and online study time were the risk factors of 6-11 years old groups [boys OR(95%CI): 1.750 (1.32-2.32), 1.214(1.00-1.47), girls: 1.579(1.25-1.99), 1.222(1.05-1.42), all P<0.05], videogames time were the risk factors of 12-16 years old groups [ boys: 2.237 (1.60-3.13), girls: 1.272 (1.00-1.61), all P<0.05]. Conclusions: Some children and adolescents may have psychological and behavioral problems during long-term home-schooling. The psychological and behavioral manifestations differed in age and gender subgroups, which deserve special attention in each subgroups. Schools, families and specialists should actively provide precise psychological support and comprehensive intervention strategies according to special features and risk factors.