Influence of solitary or commensal dining advertisements on consumers’ expectations of and attitudes toward foods and restaurants

We conducted two studies to investigate how restaurant advertisements depicting different types of eating scenarios (commensal vs. solitary dining) might influence consumers’ expectations of and attitudes toward the foods and restaurants after they were reminded of the pandemic. Participants expected that the foods shown in the advertisements of commensal dining would be more palatable and likable than the same foods depicted in the advertisements of solitary dining. They also showed more positive attitudes toward both the restaurants and foods. The enhanced hedonic expectations induced by the advertisements depicting commensal dining, however, were modulated by the priming for COVID-19 salience. Collectively, these findings suggest that consumers’ preference for commensal dining can be extended to the advertisements depicting such eating scenarios, but this effect could be attenuated by consumers’ awareness of the pandemic. These findings provide insight into restaurant advertisement design and highlight the negative effect of the pandemic on consumers.

Genetic Surveillance of Five SARS-CoV-2 Clinical Samples in Henan Province Using Nanopore Sequencing.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has rapidly spread and poses a major threat to public health worldwide. The whole genome sequencing plays a crucial role in virus surveillance and evolutionary analysis. In this study, five genome sequences of SARS-CoV-2 were obtained from nasopharyngeal swab samples from Zhengzhou, China. Following RNA extraction and cDNA synthesis, multiplex PCR was performed with two primer pools to produce the overlapped amplicons of ~1,200 bp. The viral genomes were obtained with 96% coverage using nanopore sequencing. Forty-five missense nucleotide mutations were identified; out of these, 5 mutations located at Nsp2, Nsp3, Nsp14, and ORF10 genes occurred with a <0.1% frequency in the global dataset. On the basis of mutation profiles, five genomes were clustered into two sublineages (B.1.617.2 and AY.31) or subclades (21A and 21I). The phylogenetic analysis of viral genomes from several regions of China and Myanmar revealed that five patients had different viral transmission chains. Taken together, we established a nanopore sequencing platform for genetic surveillance of SARS-CoV-2 and identified the variants circulating in Zhengzhou during August 2021. Our study provided crucial support for government policymaking and prevention and control of COVID-19.

Determinants of COVID-19 Vaccine Acceptance and Hesitancy: A Health Care Student-Based Online Survey in Northwest China.

Background: With the spread of COVID-19 around the world, herd immunity through vaccination became a key measure to control the pandemic, but high uptake of vaccine is not guaranteed. Moreover, the actual acceptance of COVID-19 vaccination and associated factors remain uncertain among health care students in Northwest China. Methods: A cross-sectional survey of a sample of 631 health care students was performed using a questionnaire developed through Wen Juan Xing survey platform to collect information regarding their attitudes, beliefs, and acceptance of COVID-19 vaccination. Binary logistic regression analyses were performed to identify the association between vaccination willingness and demographics, attitudes, and beliefs to determine the factors that actually effect acceptance and hesitancy of COVID-19 vaccine among health care students. Results: Overall, 491 (77.81%) students actually received the COVID-19 vaccine, and of the 140 unvaccinated, 69 were hesitant and 71 rejected. Binary logistic regression analysis showed that the actually vaccinated individuals were those who mostly believed in the effectiveness of the COVID-19 vaccine (OR = 2.94, 95%CI: 1.37, 6.29), those who mostly felt it is their responsibility to receive the vaccine to protect others from infection (OR = 2.75, 95%CI: 1.45, 5.23), with less previous experience about other vaccines (OR = 1.70, 95%CI: 1.06, 2.72), students who mostly thought COVID-19 to be very severe (OR = 1.77, 95%CI: 1.07, 2.93), and students who mostly thought the COVID-19 vaccine was one of the best protection measures (OR = 1.68, 95%CI: 1.03, 2.76). Concerns about side effects of vaccines (OR = 0.30, 95%CI: 0.18, 0.51) and the use of personal protective behavior as an alternative to the COVID-19 vaccination (OR = 0.16, 95%CI: 0.06, 0.39) hindered the vaccine acceptance. Conclusions: Our study showed higher COVID-19 vaccine acceptance among healthcare students. However, the individuals with vaccine hesitancy and rejection were still worrying. Vaccine safety and effectiveness issues continue to be a major factor affecting students' acceptance. To expand vaccine coverage in response to the COVID-19 pandemic, appropriate vaccination strategies and immunization programs are essential, especially for those with negative attitudes and beliefs.

An Illustrated Guide to the Imaging Evolution of COVID in Non-Epidemic Areas of Southeast China.

Purpose: By analyzing the CT manifestations and evolution of COVID in non-epidemic areas of southeast China, analyzing the developmental abnormalities and accompanying signs in the early and late stages of the disease, providing imaging evidence for clinical diagnosis and identification, and assisting in judging disease progression and monitoring prognosis. Methods: This retrospective and multicenter study included 1,648 chest CT examinations from 693 patients with laboratory-confirmed COVID-19 infection from 16 hospitals of southeast China between January 19 and March 27, 2020. Six trained radiologists analyzed and recorded the distribution and location of the lesions in the CT images of these patients. The accompanying signs include crazy-paving sign, bronchial wall thickening, microvascular thickening, bronchogram sign, fibrous lesions, halo and reverse-halo signs, nodules, atelectasis, and pleural effusion, and at the same time, they analyze the evolution of the abovementioned manifestations over time. Result: There were 1,500 positive findings in 1,648 CT examinations of 693 patients; the average age of the patients was 46 years, including 13 children; the proportion of women was 49%. Early CT manifestations are single or multiple nodular, patchy, or flaky ground-glass-like density shadows. The frequency of occurrence of ground-glass shadows (47.27%), fibrous lesions (42.60%), and microvascular thickening (40.60%) was significantly higher than that of other signs. Ground-glass shadows increase and expand 3-7 days after the onset of symptoms. The distribution and location of lesions were not significantly related to the appearance time. Ground-glass shadow is the most common lesion, with an average absorption time of 6.2 days, followed by consolidation, with an absorption time of about 6.3 days. It takes about 8 days for pure ground-glass lesions to absorb. Consolidation change into ground glass or pure ground glass takes 10-14 days. For ground-glass opacity to evolve into pure ground-glass lesions, it takes an average of 17 days. For ground-glass lesions to evolve into consolidation, it takes 7 days, pure ground-glass lesions need 8 days to evolve into ground-glass lesions. The average time for CT signs to improve is 10-15 days, and the first to improve is the crazy-paving sign and nodules; while the progression of the disease is 6-12 days, the earliest signs of progression are air bronchogram signs, bronchial wall thickening, and bronchiectasis. There is no severe patient in this study. Conclusion: This study depicts the CT manifestation and evolution of COVID in non-epidemic origin areas, and provides valuable first-hand information for clinical diagnosis and judgment of patient's disease evolution and prediction.

Assisting scalable diagnosis automatically via CT images in the combat against COVID-19.

The pandemic of Coronavirus Disease 2019 (COVID-19) is causing enormous loss of life globally. Prompt case identification is critical. The reference method is the real-time reverse transcription PCR (RT-PCR) assay, whose limitations may curb its prompt large-scale application. COVID-19 manifests with chest computed tomography (CT) abnormalities, some even before the onset of symptoms. We tested the hypothesis that the application of deep learning (DL) to 3D CT images could help identify COVID-19 infections. Using data from 920 COVID-19 and 1,073 non-COVID-19 pneumonia patients, we developed a modified DenseNet-264 model, COVIDNet, to classify CT images to either class. When tested on an independent set of 233 COVID-19 and 289 non-COVID-19 pneumonia patients, COVIDNet achieved an accuracy rate of 94.3% and an area under the curve of 0.98. As of March 23, 2020, the COVIDNet system had been used 11,966 times with a sensitivity of 91.12% and a specificity of 88.50% in six hospitals with PCR confirmation. Application of DL to CT images may improve both efficiency and capacity of case detection and long-term surveillance.

Temporal changes of COVID-19 pneumonia by mass evaluation using CT: a retrospective multi-center study.

BACKGROUND: Coronavirus disease 2019 (COVID-19) has widely spread worldwide and caused a pandemic. Chest CT has been found to play an important role in the diagnosis and management of COVID-19. However, quantitatively assessing temporal changes of COVID-19 pneumonia over time using CT has still not been fully elucidated. The purpose of this study was to perform a longitudinal study to quantitatively assess temporal changes of COVID-19 pneumonia. METHODS: This retrospective and multi-center study included patients with laboratory-confirmed COVID-19 infection from 16 hospitals between January 19 and March 27, 2020. Mass was used as an approach to quantitatively measure dynamic changes of pulmonary involvement in patients with COVID-19. Artificial intelligence (AI) was employed as image segmentation and analysis tool for calculating the mass of pulmonary involvement. RESULTS: A total of 581 confirmed patients with 1,309 chest CT examinations were included in this study. The median age was 46 years (IQR, 35-55; range, 4-87 years), and 311 (53.5%) patients were male. The mass of pulmonary involvement peaked on day 10 after the onset of initial symptoms. Furthermore, the mass of pulmonary involvement of older patients (>45 years) was significantly severer (P<0.001) and peaked later (day 11 vs. day 8) than that of younger patients (≤45 years). In addition, there were no significant differences in the peak time (day 10 vs. day 10) and median mass (P=0.679) of pulmonary involvement between male and female. CONCLUSIONS: Pulmonary involvement peaked on day 10 after the onset of initial symptoms in patients with COVID-19. Further, pulmonary involvement of older patients was severer and peaked later than that of younger patients. These findings suggest that AI-based quantitative mass evaluation of COVID-19 pneumonia hold great potential for monitoring the disease progression.

Study on the dynamic change law and correlation between CT imaging manifestations and cellular immunity of 2019 novel coronavirus disease.

PURPOSE: To explore the dynamic changes and correlation between CT imaging manifestations and cellular immunity of COVID-19. MATERIALS AND METHODS: This retrospective review analyzed 23 patients with COVID-19, including 13 males and 10 females aged 27-70 years, with an average age of 48 years. Patients were divided into two groups: group A with 11 critical-severe patients, and group B with 12 common-mild patients. Clinical, laboratory, and radiological data were collected and analyzed. RESULTS: LYM, LYM (%), CD3+, CD4+, and CD8+ decreased, while NEU (%), CRP, and CT scores increased in all patients, WBC in group A increased. In group A, on day 10-12 after disease onset, CT scores and CRP reached the highest point, and day 13-15 LYM, LYM (%) reached the lowest but NEU (%) and WBC reached the highest, CD3+, CD4+ and CD8+ were at the lowest on day 10-15. In group B, on day 7-9, CT scores, NEU (%) and CRP reached the peak, but LYM, LYM (%), CD3+, CD4+ and CD8+ reached the lowest. In all patients, CT scores had a significantly negative correlation with CD3+, CD4+, CD8+, LYM (%), and LYM (p = 0.001, r = - 0.797; p = 0.008, r = - 0.698; p = 0.002, r = - 0.775; p < 0.001, r = - 0.785; p = 0.021, r = - 0.571, respectively), and a significantly positive correlation with WBC and NEU (%) (p < 0.001, r = 0.785; p = 0.003, r = 0.691, respectively). CONCLUSION: Dynamic changes of CT manifestations and cellular immunity of patients with COVID-19 were regular and correlation was high between these two parameters.