Health disparity in the spread of COVID-19: Evidence from social distancing, risk of interactions, and access to testing

Objective – To identify and assess whether three major risk factors that due to differential access to flexible resources might help explain disparities in the spread of COVID-19 across communities with different socioeconomic status, including socioeconomic inequalities in social distancing, the potential risk of interpersonal interactions, and access to testing. Methods Analysis uses ZIP code level weekly COVID-19 new cases, weekly population movement flows, weekly close-contact index, and weekly COVID-19 testing sites in Southern California from March 2020 to April 2021, merged with the U.S. census data to measure ZIP code level socioeconomic status and cofounders. This study first develops the measures for social distancing, the potential risk of interactions, and access to testing. Then we employ a spatial lag regression model to quantify the contributions of those factors to weekly COVID-19 case growth. Results Results identify that, during the first COVID-19 wave, new case growth of the low-income group is two times higher than that of the high-income group. The COVID-19 case disparity widens to four times in the second COVID-19 wave. We also observed significant disparities in social distancing, the potential risk of interactions, and access to testing among communities with different socioeconomic status. In addition, all of them contribute to the disparities of COVID-19 incidences. Among them, the potential risk of interactions is the most important contributor, whereas testing accessibility contributes least. We also found that close-contact is a more effective measure of social distancing than population movements in examining the spread of COVID-19. Conclusion – This study answers critically unaddressed questions about health disparities in the spread of COVID-19 by assessing factors that might explain why the spread is different in different groups.

Valence-arousal classification of emotion evoked by Chinese ancient-style music using 1D-CNN-BiLSTM model on EEG signals for college students.

During the COVID-19 pandemic, young people are using multimedia content more frequently to communicate with each other on Internet platforms. Among them, music, as psychological support for a lonely life in this special period, is a powerful tool for emotional self-regulation and getting rid of loneliness. More and more attention has been paid to the music recommender system based on emotion. In recent years, Chinese music has tended to be considered an independent genre. Chinese ancient-style music is one of the new folk music styles in Chinese music and is becoming more and more popular among young people. The complexity of Chinese-style music brings significant challenges to the quantitative calculation of music. To effectively solve the problem of emotion classification in music information search, emotion is often characterized by valence and arousal. This paper focuses on the valence and arousal classification of Chinese ancient-style music-evoked emotion. It proposes a hybrid one-dimensional convolutional neural network and bidirectional and unidirectional long short-term memory model (1D-CNN-BiLSTM). And a self-acquisition EEG dataset for Chinese college students was designed to classify music-induced emotion by valence-arousal based on EEG. In addition to that, the proposed 1D-CNN-BILSTM model verified the performance of public datasets DEAP and DREAMER, as well as the self-acquisition dataset DESC. The experimental results show that, compared with traditional LSTM and 1D-CNN-LSTM models, the proposed method has the highest accuracy in the valence classification task of music-induced emotion, reaching 94.85%, 98.41%, and 99.27%, respectively. The accuracy of the arousal classification task also gained 93.40%, 98.23%, and 99.20%, respectively. In addition, compared with the positive valence classification results of emotion, this method has obvious advantages in negative valence classification. This study provides a computational classification model for a music recommender system with emotion. It also provides some theoretical support for the brain-computer interactive (BCI) application products of Chinese ancient-style music which is popular among young people.

Sequential Glycosylations at the Multibasic Cleavage Site of SARS-CoV-2 Spike Protein Regulate Viral Activation, Assembly, and Infection (preprint)

The multibasic furin cleavage site at the S1/S2 boundary of the spike protein (S protein) is a hallmark of SARS-CoV-2 and is essential for its increased infectivity. O-glycosylation near the furin site catalyzed by host cell glycosyltransferases can theoretically hinder spike protein processing and impede viral infection, but so far such hypothesis has not been tested with authentic viruses. The mechanism for furin activation is not clearly understood either. Here in this study, we discovered that GalNAc-T3 and T7 together initiate clustered O-glycosylations in the multibasic S1/S2 boundary region, which inhibits furin processing of the spike protein and surprisingly suppresses the incorporation of S protein into virus-like-particles (VLPs). Mechanistic analysis revealed that the assembly of spike protein into VLPs relies on protein-protein interaction between the furin-cleaved S protein and a double aspartic motif on the membrane protein of SARS-CoV-2, suggesting a novel mechanism for furin activation of S protein. Interestingly, a point mutation at P681, found in the SARS-CoV-2 variants alpha and delta, resists the glycosylation by GalNAc-T3 and T7 and its inhibitory effect against furin processing. However, an additional mutation at N679 in the most recent omicron variant reverts this resistance, making it both prone to glycosylation in vitro and sensitive to the expression of GalNAc-T3 and T7 in human lung cells. Together, our results suggest a glycosylation-based defense mechanism of host cells against SARS-CoV-2 and reveal the host-pathogen interplay at this critical “battle field” as the virus first escapes and currently surrenders itself to the host cell glycosylation.

Valence-arousal classification of emotion evoked by Chinese ancient-style music using 1D-CNN-BiLSTM model on EEG signals for college students

During the COVID-19 pandemic, young people are using multimedia content more frequently to communicate with each other on Internet platforms. Among them, music, as psychological support for a lonely life in this special period, is a powerful tool for emotional self-regulation and getting rid of loneliness. More and more attention has been paid to the music recommender system based on emotion. In recent years, Chinese music has tended to be considered an independent genre. Chinese ancient-style music is one of the new folk music styles in Chinese music and is becoming more and more popular among young people. The complexity of Chinese-style music brings significant challenges to the quantitative calculation of music. To effectively solve the problem of emotion classification in music information search, emotion is often characterized by valence and arousal. This paper focuses on the valence and arousal classification of Chinese ancient-style music-evoked emotion. It proposes a hybrid one-dimensional convolutional neural network and bidirectional and unidirectional long short-term memory model (1D-CNN-BiLSTM). And a self-acquisition EEG dataset for Chinese college students was designed to classify music-induced emotion by valence-arousal based on EEG. In addition to that, the proposed 1D-CNN-BILSTM model verified the performance of public datasets DEAP and DREAMER, as well as the self-acquisition dataset DESC. The experimental results show that, compared with traditional LSTM and 1D-CNN-LSTM models, the proposed method has the highest accuracy in the valence classification task of music-induced emotion, reaching 94.85%, 98.41%, and 99.27%, respectively. The accuracy of the arousal classification task also gained 93.40%, 98.23%, and 99.20%, respectively. In addition, compared with the positive valence classification results of emotion, this method has obvious advantages in negative valence classification. This study provides a computational classification model for a music recommender system with emotion. It also provides some theoretical support for the brain-computer interactive (BCI) application products of Chinese ancient-style music which is popular among young people.

Collectivism and Altruistic Behavior: A Third-Person Effect Study of COVID-19 News Among Wuhan Residents

This study examines the third-person effects of COVID-19 news among Wuhan residents during the peak of the outbreaks in the city. Using data collected in a telephone survey of 1,071 Wuhan residents, results show that respondents believed others were more influenced by the COVID-19 news. However, the more the respondents systematically processed the news through elaboration and the more they were oriented toward collectivist values, the smaller the self-other perceptual gap. Finally, results suggest the moderating effect of collectivism on the relationship between perceived effects of COVID news and altruistic behavior?collectivism enhances the influence of perceived effects on others on adoption of altruistic behavior. The influence of culture in shaping risk perception and behavioral responses is discussed.

Immune responses to two and three doses of the BNT162b2 mRNA vaccine in adults with solid tumors.

Vaccines against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have shown high efficacy, but immunocompromised participants were excluded from controlled clinical trials. In this study, we compared immune responses to the BNT162b2 mRNA Coronavirus Disease 2019 vaccine in patients with solid tumors (n = 53) who were on active cytotoxic anti-cancer therapy to a control cohort of participants without cancer (n = 50). Neutralizing antibodies were detected in 67% of patients with cancer after the first immunization, followed by a threefold increase in median titers after the second dose. Similar patterns were observed for spike protein-specific serum antibodies and T cells, but the magnitude of each of these responses was diminished relative to the control cohort. In most patients with cancer, we detected spike receptor-binding domain and other S1-specific memory B cell subsets as potential predictors of anamnestic responses to additional immunizations. We therefore initiated a phase 1 trial for 20 cancer cohort participants of a third vaccine dose of BNT162b2 ( NCT04936997 ); primary outcomes were immune responses, with a secondary outcome of safety. At 1 week after a third immunization, 16 participants demonstrated a median threefold increase in neutralizing antibody responses, but no improvement was observed in T cell responses. Adverse events were mild. These results suggest that a third dose of BNT162b2 is safe, improves humoral immunity against SARS-CoV-2 and could be immunologically beneficial for patients with cancer on active chemotherapy.

Duanzi as Networked Practice: How Online Satire Shapes Psychological Well-Being, Social Support, and Issue Knowledge for Chinese with Different Social Capital during COVID-19 Outbreaks.

Practices oriented to digital technologies are being invented to change how people cope with crises. This study examines how Chinese netizens' networked practices (e.g., liking, sharing, or commenting) with COVID-19 related duanzi (short online satires) influenced their psychological well-being, external social support, and issue knowledge during the pandemic. The role of social capital in moderating these relations is explored. Findings from the survey demonstrate that the act of "liking" a COVID-19 duanzi on WeChat has become a routine practice for Chinese netizens to kill time during the quarantine. However, the more bonding social capital one already had, the less they depended on duanzi "liking" to kill their boredom. Those less supported outside the family household, or less knowledgeable about the virus were also more likely to share a COVID-19 duanzi. Bonding social capital promotes one's well-being, therefore, the positive psychological effect of duanzi sharing or commenting grows more pronounced for netizens with more bonding social capital. Bridging social capital brought external social support. Netizens with more bridging social capital obtained more external support and more COVID-19 knowledge from duanzi sharing. The theoretical and practical implications are elaborated in the conclusions.

Characteristic CT findings distinguishing 2019 novel coronavirus disease (COVID-19) from influenza pneumonia.

OBJECTIVES: To investigate the different CT characteristics which may distinguish influenza from 2019 coronavirus disease (COVID-19). METHODS: A total of 13 confirmed patients with COVID-19 were enrolled from January 16, 2020, to February 25, 2020. Furthermore, 92 CT scans of confirmed patients with influenza pneumonia, including 76 with influenza A and 16 with influenza B, scanned between January 1, 2019, to February 25, 2020, were retrospectively reviewed. Pulmonary lesion distributions, number, attenuation, lobe predomination, margin, contour, ground-glass opacity involvement pattern, bronchial wall thickening, air bronchogram, tree-in-bud sign, interlobular septal thickening, intralobular septal thickening, and pleural effusion were evaluated in COVID-19 and influenza pneumonia cohorts. RESULTS: Peripheral and non-specific distributions in COVID-19 showed a markedly higher frequency compared with the influenza group (p < 0.05). Most lesions in COVID-19 showed balanced lobe localization, while in influenza pneumonia they were predominantly located in the inferior lobe (p < 0.05). COVID-19 presented a clear lesion margin and a shrinking contour compared with influenza pneumonia (p < 0.05). COVID-19 had a patchy or combination of GGO and consolidation opacities, while a cluster-like pattern and bronchial wall thickening were more frequently seen in influenza pneumonia (p < 0.05). The lesion number and attenuation, air bronchogram, tree-in-bud sign, interlobular septal thickening, and intralobular septal thickening were not significantly different between the two groups (all p > 0.05). CONCLUSIONS: Though viral pneumonias generally show similar imaging features, there are some characteristic CT findings which may help differentiating COVID-19 from influenza pneumonia. KEY POINTS: • CT can play an early warning role in the diagnosis of COVID-19 in the case of no epidemic exposure. • CT could be used for the differential diagnosis of influenza and COVID-19 with satisfactory accuracy. • COVID-19 had a patchy or combination of GGO and consolidation opacities with peripheral distribution and balanced lobe predomination.