Quality and accuracy of gastric cancer related videos in social media videos platforms.

BACKGROUND: Gastric cancer is a major public health problem worldwide. Social media has affected public's daily lives in ways no one ever thought possible. Both TikoTok and its Chinese version Douyin are the most popular short video posting platform. This study aimed to evaluate the quality, accuracy, and completeness of videos for gastric cancer on TikTok and Douyin. METHODS: The terms "gastric cancer" was searched on TikTok in both English and Japanese, and on Douyin in Chinese. The first 100 videos in three languages (website's default setting) were checked. QUality Evaluation Scoring Tool (QUEST) and DISCERN as the instrument for assessing the quality of the information in each video. Content was analysed under six categories (aetiology, anatomy, symptoms, preventions, treatments, and prognosis). The educational value and completeness were evaluated with a checklist developed by the researchers. RESULTS: A total of 78 videos in English, 63 in Japanese, and 99 in Chinese were analyzed. The types of sources were as follows: 6.4% in English, 4.8% in Japanese, and 57.6% in Chinese for health professionals; 93.6% in English, 95.2% in Japanese, and 3.0% in Chinese for private users; none in English and Japanese, but 39.4% in Chinese for other sources. In all, 20.5% in English, 17.5% in Japanese, and 93.9% in Chinese of videos had useful information about gastric cancer. Among the useful videos, the videos published in Chinese had the highest QUEST(p < 0.05) and DISCERN scores(p < 0.05), followed by those published in Japanese. Among the educational videos, prognosis in English (37.5%), symptoms in Japanese (54.5%), and prevention in Chinese (47.3%) were the most frequently covered topic. CONCLUSIONS: TikTok in English and Japanese might not fully meet the gastric cancer information needs of public, but Douyin in Chinese was the opposite.

Can CD147 work as a therapeutic target for tumors through COVID-19 infection?

In this review, we discussed an interesting case infected with "COVID-19" (Corona Virus Disease 2019). The patients with Hodgkin's lymphoma recovered after infection with COVID-19. It may be that COVID-19 activates the patient's immune system, or it may be a coincidence. COVID-19 spike protein can interact with CD147 and use it as an entry to invade host cells. CD147 is a partner of SLC3A2, which is the chaperone subunit of cystine/glutamate reverse transporter (system XC). The catalytic subunit of system XC is SLC7A11. SLC7A11 mediated cysteine uptake plays a key role in ferroptosis. Through literature review and data analysis, we suggest that CD147, as a new potential COVID-19 infection entry, may also lead to ferroptosis of host cells. Our hypothesis is that spike protein of COVID-19 induced ferroptosis in host cells via CD147/SLC3A2/SLC7A11 complex. This is another explanation for the cancer patient recovered after COVID-19 infection.

Antiviral efficacy of selective estrogen receptor modulators against SARS-CoV-2 infection in vitro and in vivo reveals bazedoxifene acetate as an entry inhibitor.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the seventh member of the coronavirus family that can infect humans. Recently, more contagious and pathogenic variants of SARS-CoV-2 have been continuously emerging. Clinical candidates with high efficacy and ready availability are still in urgent need. To identify potent anti-SARS-CoV-2 repurposing drugs, we evaluated the antiviral efficacy of 18 selective estrogen receptor modulators (SERMs) against SARS-CoV-2 infection. Six SERMs exhibited excellent anti-SARS-CoV-2 effects in Vero E6 cells and three human cell lines. Clomifene citrate, tamoxifen, toremifene citrate, and bazedoxifene acetate reduced the weight loss of hamsters challenged with SARS-CoV-2, and reduced hamster pulmonary viral load and interleukin-6 expression when assayed at 4 days postinfection. In particular, bazedoxifene acetate was identified to act on the penetration stage of the postattachment step via altering cholesterol distribution and endosome acidification. And, bazedoxifene acetate inhibited pseudoviruses infection of original SARS-CoV-2, Delta variant, Omicron variant, and SARS-CoV. These results offer critical information supporting bazedoxifene acetate as a promising agent against coronaviruses.

The Association Between COVID-19 Mortality And The County-Level Partisan Divide In The United States

Partisan differences in attitudes toward the COVID-19 pandemic and toward the appropriateness of local policies requiring masks, social distancing, and vaccines are apparent in the United states. Previous research suggests that areas with a higher Republican vote share may experience more COVID-19 mortality, potentially as a consequence of these differences. In this observational study that captured data from a majority of Us counties, we compared the number of COVID-19 deaths through October 31, 2021, among counties with differing levels of Republican vote share, using 2020 presidential election returns to characterize county political affiliation. Our analyses controlled for demographic characteristics and social determinants likely to influence COVID-19 transmission and outcomes using state fixed effects. We found a positive dose-response relationship between county-level Republican vote share and county-level COVID-19 mortality. Majority Republican counties experienced 72.9 additional deaths per 100,000 people relative to majority Democratic counties during the study period, and COVID-19 vaccine uptake explains approximately 10 percent of the difference. Our findings suggest that county-level voting behavior may act as a proxy for compliance with and support of public health measures that would protect residents from COVID-19.

Discovery of potential anti-SARS-CoV-2 drugs based on large-scale screening in vitro and effect evaluation in vivo.

The coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a global crisis. Clinical candidates with high efficacy, ready availability, and that do not develop resistance are in urgent need. Despite that screening to repurpose clinically approved drugs has provided a variety of hits shown to be effective against SARS-CoV-2 infection in cell culture, there are few confirmed antiviral candidates in vivo. In this study, 94 compounds showing high antiviral activity against SARS-CoV-2 in Vero E6 cells were identified from 2,580 FDA-approved small-molecule drugs. Among them, 24 compounds with low cytotoxicity were selected, and of these, 17 compounds also effectively suppressed SARS-CoV-2 infection in HeLa cells transduced with human ACE2. Six compounds disturb multiple processes of the SARS-CoV-2 life cycle. Their prophylactic efficacies were determined in vivo using Syrian hamsters challenged with SARS-CoV-2 infection. Seven compounds reduced weight loss and promoted weight regain of hamsters infected not only with the original strain but also the D614G variant. Except for cisatracurium, six compounds reduced hamster pulmonary viral load, and IL-6 and TNF-α mRNA when assayed at 4 d postinfection. In particular, sertraline, salinomycin, and gilteritinib showed similar protective effects as remdesivir in vivo and did not induce antiviral drug resistance after 10 serial passages of SARS-CoV-2 in vitro, suggesting promising application for COVID-19 treatment.

Association between clinical characteristics and CT findings in patients with coronavirus disease-2019.

ABSTRACT: This retrospective study was to investigate the association between clinical characteristics and computerized tomography (CT) findings in patients with coronavirus disease-2019 (COVID-19). The clinical data of COVID-19 patients were retrospectively analyzed. Spearman correlation analysis was used to identify the correlation. Totally 209 consecutive COVID-19 patients were eligible for the study, with the mean age of 47.53 ±â€Š13.52 years. At onset of the disease, the most common symptoms were fever (85.65%) and cough (61.24%). The CT features of COVID-19 included pulmonary, bronchial, and pleural changes, with the significant pulmonary presentation of ground-glass opacification (93.30%), consolidation (48.80%), ground-glass opacification plus a reticular pattern (54.07%), telangiectasia (84.21%), and pulmonary fibrotic streaks (49.76%). Spearman analysis showed that the CT findings had significantly inverse associations with the platelets, lymphocyte counts, and sodium levels, but were positively related to the age, erythrocyte sedimentation rate, D-dimer, lactic dehydrogenase, α-hydroxybutyrate dehydrogenase, and C-reactive protein levels (P < .05). In conclusion, the severity of lung abnormalities on CT in COVID-19 patients is inversely associated with the platelets, lymphocyte count, and sodium levels, whereas positively with the age, erythrocyte sedimentation rate, D-dimer, lactic dehydrogenase, hydroxybutyrate dehydrogenase, and C-reactive protein levels.

Automatic detection of multiple types of pneumonia: Open dataset and a multi-scale attention network.

The quick and precise identification of COVID-19 pneumonia, non-COVID-19 viral pneumonia, bacterial pneumonia, mycoplasma pneumonia, and normal lung on chest CT images play a crucial role in timely quarantine and medical treatment. However, manual identification is subject to potential misinterpretations and time-consumption issues owing the visual similarities of pneumonia lesions. In this study, we propose a novel multi-scale attention network (MSANet) based on a bag of advanced deep learning techniques for the automatic classification of COVID-19 and multiple types of pneumonia. The proposed method can automatically pay attention to discriminative information and multi-scale features of pneumonia lesions for better classification. The experimental results show that the proposed MSANet can achieve an overall precision of 97.31%, recall of 96.18%, F1-score of 96.71%, accuracy of 97.46%, and macro-average area under the receiver operating characteristic curve (AUC) of 0.9981 to distinguish between multiple classes of pneumonia. These promising results indicate that the proposed method can significantly assist physicians and radiologists in medical diagnosis. The dataset is publicly available at https://doi.org/10.17632/rf8x3wp6ss.1.

Coronary artery calcification and risk of mortality and adverse outcomes in patients with COVID-19: a Chinese multicenter retrospective cohort study.

Background: Coronary artery calcification (CAC) is an independent risk factor of major adverse cardiovascular events; however, the impact of CAC on in-hospital death and adverse clinical outcomes in patients with coronavirus disease 2019 (COVID-19) remains unclear. Objective: To explore the association between CAC and in-hospital mortality and adverse events in patients with COVID-19. Methods: This multicenter retrospective cohort study enrolled 2067 laboratory-confirmed COVID-19 patients with definitive clinical outcomes (death or discharge) admitted from 22 tertiary hospitals in China between January 3, 2020 and April 2, 2020. Demographic, clinical, laboratory results, chest CT findings, and CAC on admission were collected. The primary outcome was in-hospital death and the secondary outcome was composed of in-hospital death, admission to intensive care unit (ICU), and requiring mechanical ventilation. Multivariable Cox regression analysis and Kaplan-Meier plots were used to explore the association between CAC and in-hospital death and adverse clinical outcomes. Results: The mean age was 50 years (SD,16) and 1097 (53.1%) were male. A total of 177 patients showed high CAC level, and compared with patients with low CAC, these patients were older (mean age: 49 vs. 69 years, P < 0.001) and more likely to be male (52.0% vs. 65.0%, P = 0.001). Comorbidities, including cardiovascular disease (CVD) ([33.3%, 59/177] vs. [4.7%, 89/1890], P < 0.001), presented more often among patients with high CAC, compared with patients with low CAC. As for laboratory results, patients with high CAC had higher rates of increased D-dimer, LDH, as well as CK-MB (all P < 0.05). The mean CT severity score in high CAC group was also higher than low CAC group (12.6 vs. 11.1, P = 0.005). In multivariable Cox regression model, patients with high CAC were at a higher risk of in-hospital death (hazard ratio [HR], 1.731; 95% CI 1.010-2.971, P = 0.046) and adverse clinical outcomes (HR, 1.611; 95% CL 1.087-2.387, P = 0.018). Conclusion: High CAC is a risk factor associated with in-hospital death and adverse clinical outcomes in patients with confirmed COVID-19, which highlights the importance of calcium load testing for hospitalized COVID-19 patients and calls for attention to patients with high CAC. Supplementary Information: The online version contains supplementary material available at 10.1007/s42058-021-00072-4.

The evolution of chest CT findings from admission to follow-up in 30 moderate to severe adult patients with COVID-19 pneumonia.

OBJECTIVE: To analyze the evolution of chest computed tomography (CT) findings from admission to follow-up in moderate to severe patients with coronavirus disease-19 (COVID-19) pneumonia. METHODS: During December 2019-April 2020, the sequential CT images of 30 patients with COVID-19 pneumonia were retrospectively analyzed from admission to follow-up. The qualitative evolution tendency of lung abnormalities and semi-quantitative CT scores were analyzed for temporal change. RESULTS: The mean hospitalized period was 24.5 ± 9.6 days (range 6-49 days). The average time from the first, second, third, fourth and follow-up CT examination to the initial symptom onset were 4.2 ± 3.1 days, 10.7 ± 4.4 days, 17.1 ± 3.9 days, 24.6 ± 7.5 days, and 42.4 ± 15.6 days, respectively. During illness day 0-5, groundglass opacity (GGO) was the main pattern. The following illness day 6-11, the main CT pattern was consolidation and reticular pattern. The consolidation and reticular pattern gradually dissipate during illness day 12-23, and the reticular pattern and light GGO increased. When illness day was ≥ 24 days, the reticular pattern and light GGO gradually decrease until complete dissipation. The highest CT score was at illness day 6-11. Pearson correlation analysis showed that the mean and maximum CT score were not correlated with the length of fever (r = 0.018, p = 0.923 and r = 0.086, p = 0.652) and hospitalization (r = 0.192, p = 0.31 and r = 0.273, p = 0.144). CONCLUSIONS: The dynamic evolution of CT manifestation in moderate to severe COVID-19 pneumonia patients followed a specific pattern over time. During illness day 6-11, the extent of lung abnormalities on chest CT was the most severe. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s42058-021-00058-2.

COVID-19: the epidemiology and treatment.

After initially emerging in late 2019, coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has spread rapidly to cause a global pandemic. SARS-CoV-2 is a betacoronavirus that is closely related to severe acute respiratory syndrome coronavirus and Middle East respiratory syndrome coronavirus, all of which can cause severe lung injury, respiratory distress and cytokine storm. While mortality rates associated with SARS-CoV-2 are lower than those associated with severe acute respiratory syndrome coronavirus or Middle East respiratory syndrome coronavirus, it is more contagious and spreads more rapidly than these other viruses. This article summarises the epidemiology and potential options for treating COVID-19 to give a foundation for future studies of the diagnosis, treatment and prevention of this deadly disease.

Isolation and identification of two strains of severe acute respiratory syndrome coronavirus 2 from coronavirus disease 2019 patients in Shanghai

Objective To isolate the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) from nasal/throat swabs of coronavirus disease 19 (COVID-19) patients. Methods Three nasal/throat swab samples from COVID-19 patients in Shanghai were treated with TPCK trypsin and were used to treat Vero E6 cells inoculated in 96-well plates. When most of the cells showed obvious cytopathy, the cell culture supernatants were collected. We then detected the viral nucleic acid by fluorescent quantitative real-time polymerase chain reaction, and amplified the gene fragment of the virus receptor binding domain (RBD) by reverse transcription polymerase chain reaction. After amplification and culture, the virus was used to infect the Vero E6 cells inoculated in 96-well plates. The cytopathy was observed and the virus protein was detected by immunofluorescence. Results The Vero E6 cells that cultured with two of three nasal/pharyngeal swab samples showed obvious cytopathic effect and newly synthesized viral nucleic acid was detected in the supernatants of the cell culture. The amplified RBD sequence was completely consistent with the corresponding fragment of SARS-CoV-2 isolated earlier. Virus-infected Vero E6 cells showed cytopathies rapidly and could react with the monoclonal antibody against nucleocapsid protein (N protein) and spike protein (S protein) of SARS-CoV-2, and convalescence sera of COVID-19 patients. Conclusion Two SARS-CoV-2 strains were successfully isolated from two nasal/throat swab samples of COVID-19 patients in Shanghai, which provides evidence for the mechanism research on the infection and pathogenesis of SARS-CoV-2 as well as the development of drugs and vaccines against SARS-CoV-2.

Automatic distinction between COVID-19 and common pneumonia using multi-scale convolutional neural network on chest CT scans.

The COVID-19 pneumonia is a global threat since it emerged in early December 2019. Driven by the desire to develop a computer-aided system for the rapid diagnosis of COVID-19 to assist radiologists and clinicians to combat with this pandemic, we retrospectively collected 206 patients with positive reverse-transcription polymerase chain reaction (RT-PCR) for COVID-19 and their 416 chest computed tomography (CT) scans with abnormal findings from two hospitals, 412 non-COVID-19 pneumonia and their 412 chest CT scans with clear sign of pneumonia are also retrospectively selected from participating hospitals. Based on these CT scans, we design an artificial intelligence (AI) system that uses a multi-scale convolutional neural network (MSCNN) and evaluate its performance at both slice level and scan level. Experimental results show that the proposed AI has promising diagnostic performance in the detection of COVID-19 and differentiating it from other common pneumonia under limited number of training data, which has great potential to assist radiologists and physicians in performing a quick diagnosis and mitigate the heavy workload of them especially when the health system is overloaded. The data is publicly available for further research at https://data.mendeley.com/datasets/3y55vgckg6/1https://data.mendeley.com/datasets/3y55vgckg6/1.