Nanofiber-Based Odor-Free Medical Mask Fabrication Using Polyvinyl Butyral and Eucalyptus Anti Odor Agent.

Following the 2020 COVID-19 worldwide outbreak, many countries adopted sanitary and safety measures to safeguard public health such as wearing medical face mask. While face masks became a necessity for people, disadvantages impede their long period wearing such as uncomfortable breathability and odor. The intermediate layer of the medical face mask is composed of porous non-woven fabric to block external particles while maintaining breathability. To overcome aforementioned limitation, this study uses electrospinning to design and fabricate odorless face masks via the use of aromatic oil. Eucalyptus essential oil is encapsulated through mixing and layer-by-layer by hydrophobic polyvinyl butyral and further used to fabricate the medical mask intermediate layer. We found that adding 0.2 g of eucalyptus into polyvinyl butyral fabric through mixing results in the deodorization rate of 80% after 2 h, with fabric thickness of 440.9 µm, and melt-blown non-woven fabric thickness of 981.7 µm. The Particle Filtration Efficiency of 98.3%, Bacterial Filtration Efficiency above 99.9%, and the differential pressure of 4.7 mm H2O/cm2 meet the CNS 14774 standard on medical face masks. Therefore, this study successfully proved that this type of masks' middle layer not only effectively protects against coronavirus, but also provides better scents and makes it more comfortable for consumers.

In Vitro Characterization of a Tissue Renin-Angiotensin System in Human Nucleus Pulposus Cells (preprint)

Low back pain is a clinically highly relevant musculoskeletal burden and is associated with inflammatory as well as degenerative processes of the intervertebral disc. However, the pathophysiology and cellular pathways contributing to this devastating condition are still poorly understood. Based on previous evidence, we hypothesize that tissue renin-angiotensin system (tRAS) components, including the SARS-CoV-2 entry receptor angiotensin-converting enzyme 2 (ACE2), are present in human nucleus pulposus (NP) cells and associated with inflammatory and degenerative processes. Experiments were performed with NP cells from 4 human donors. The existence of angiotensin II, angiotensin II type 1 receptor (AGTR1), AGTR2, MAS-receptor (MasR), and ACE2 in human NP cells was validated with immunofluorescent staining and gene expression analysis. Hereafter the cell viability was assessed after adding agonists and antagonists of the target receptors as well as angiotensin II in different concentrations for up to 48 hours of exposure. A TNF-α-induced inflammatory in vitro model was employed to assess the impact of angiotensin II addition and the stimulation or inhibition of the tRAS receptors on inflammation, tissue remodeling, expression of tRAS markers, and the release of nitric oxide (NO) into the medium. Further, protein levels of IL-6, IL-8, IL-10, and intracellular as well as secreted angiotensin II were assessed after exposing the cells to the substances, and inducible nitric oxide synthase (iNOS) levels were evaluated utilizing Western Blot. The existence of tRAS receptors and angiotensin II were validated in human NP cells. Cell viability analysis revealed no cytotoxic effects of angiotensin II. The AGTR1 inhibitor Candesartan and the MAS receptors AVE0991 showed cytotoxic effects at high concentrations (100 µM). The addition of angiotensin II only showed a mild impact on gene expression markers. However, there was a significant increase in NO secreted by the cells. The gene expression ratios of pro-inflammatory/anti-inflammatory cytokines IL-6/IL--10, IL-8/IL-10, and TNF-α /IL-10 were positively correlated with the AGTR1/AGTR2 and AGTR1/MAS1 ratios, respectively. The stimulation of the AGTR2 MAS-receptor and the inhibition of the AGTR1 receptor revealed beneficial effects on the gene expression of inflammatory and tissue remodeling markers. This finding was also present at the protein level. We did not find alterations in iNOS protein concentrations after adding the drugs. The current data showed that tRAS components are expressed in human NP cells and are associated with inflammatory and degenerative processes. Further characterization of the associated pathways is warranted. The findings indicate that tRAS modulation might be a novel therapeutic approach to intervertebral disc disease.

Diet related Weibo texts posted during the COVID-19 outbreak

Objective: To investigate the diet-related topics posted on social media during the COVID-19 outbreak.

The Impact of Online Teaching Under the COVID-19 Pandemic on the Teaching of Medical Immunology and Medical Microbiology to Second-Year Dentistry Students: A Prospective Comparative Study (preprint)

Background Medical microbiology and medical immunology are required introductory courses for second-year dentistry students. It is essential to find suitable teaching methods after the outbreak of COVID-19 to implement the call for "stop class, but not stop teaching and not stop learning."Methods We mainly used Rain Classroom for online teaching and QQ Group Classroom to support education during this particular period.Result We assessed the effectiveness of this special period instruction by analyzing anonymous questionnaires and final exam results. The anonymous questionnaire showed that most of the students were satisfied with this online instruction and found it very helpful in answering their questions and solving their problems. The final exam results showed that teaching in 2020 was no worse than teaching in 2019 and better in terms of pass rate. Teaching in 2021 was worse than in 2020, but there was a breakthrough in the highest score.Conclusion These data suggest that online teaching during the epidemic had an impact on teaching effectiveness. Most students are satisfied with online teaching, but some students' grades are affected, which may be related to the fact that some students were not comfortable with blended teaching or online teaching. We share our experiences and reflections on online and blended teaching of medical immunology and medical microbiology courses during the epidemic, hoping that it will be helpful for other teachers to teach.

Efficacy and Safety of Anticoagulation Treatment in COVID-19 Patient Subgroups Identified by Clinical-Based Stratification and Unsupervised Machine Learning: A Matched Cohort Study

Objective: To explore the efficacy of anticoagulation in improving outcomes and safety of Coronavirus disease 2019 (COVID-19) patients in subgroups identified by clinical-based stratification and unsupervised machine learning. Methods: This single-center retrospective cohort study unselectively reviewed 2,272 patients with COVID-19 admitted to the Tongji Hospital between Jan 25 and Mar 23, 2020. The association between AC treatment and outcomes was investigated in the propensity score (PS) matched cohort and the full cohort by inverse probability of treatment weighting (IPTW) analysis. Subgroup analysis, identified by clinical-based stratification or unsupervised machine learning, was used to identify sub-phenotypes with meaningful clinical features and the target patients benefiting most from AC. Results: AC treatment was associated with lower in-hospital death risk either in the PS matched cohort or by IPTW analysis in the full cohort. A higher incidence of clinically relevant non-major bleeding (CRNMB) was observed in the AC group, but not major bleeding. Clinical subgroup analysis showed that, at admission, severe cases of COVID-19 clinical classification, mild acute respiratory distress syndrome (ARDS) cases, and patients with a D-dimer level ≥0.5 μg/mL, may benefit from AC. During the hospital stay, critical cases and severe ARDS cases may benefit from AC. Unsupervised machine learning analysis established a four-class clustering model. Clusters 1 and 2 were non-critical cases and might not benefit from AC, while clusters 3 and 4 were critical patients. Patients in cluster 3 might benefit from AC with no increase in bleeding events. While patients in cluster 4, who were characterized by multiple organ dysfunction (neurologic, circulation, coagulation, kidney and liver dysfunction) and elevated inflammation biomarkers, did not benefit from AC. Conclusions: AC treatment was associated with lower in-hospital death risk, especially in critically ill COVID-19 patients. Unsupervised learning analysis revealed that the most critically ill patients with multiple organ dysfunction and excessive inflammation might not benefit from AC. More attention should be paid to bleeding events (especially CRNMB) when using AC.

Advancing COVID-19 Diagnosis with Privacy-Preserving Collaboration in Artificial Intelligence (preprint)

Artificial intelligence (AI) provides a promising substitution for streamlining COVID-19 diagnoses. However, concerns surrounding security and trustworthiness impede the collection of large-scale representative medical data, posing a considerable challenge for training a well-generalised model in clinical practices. To address this, we launch the Unified CT-COVID AI Diagnostic Initiative (UCADI), where the AI model can be distributedly trained and independently executed at each host institution under a federated learning framework (FL) without data sharing. Here we show that our FL model outperformed all the local models by a large yield (test sensitivity /specificity in China: 0.973/0.951, in the UK: 0.730/0.942), achieving comparable performance with a panel of professional radiologists. We further evaluated the model on the hold-out (collected from another two hospitals leaving out the FL) and heterogeneous (acquired with contrast materials) data, provided visual explanations for decisions made by the model, and analysed the trade-offs between the model performance and the communication costs in the federated training process. Our study is based on 9,573 chest computed tomography scans (CTs) from 3,336 patients collected from 23 hospitals located in China and the UK. Collectively, our work advanced the prospects of utilising federated learning for privacy-preserving AI in digital health.

Medical-VLBERT: Medical Visual Language BERT for COVID-19 CT Report Generation With Alternate Learning (preprint)

Medical imaging technologies, including computed tomography (CT) or chest X-Ray (CXR), are largely employed to facilitate the diagnosis of the COVID-19. Since manual report writing is usually too time-consuming, a more intelligent auxiliary medical system that could generate medical reports automatically and immediately is urgently needed. In this article, we propose to use the medical visual language BERT (Medical-VLBERT) model to identify the abnormality on the COVID-19 scans and generate the medical report automatically based on the detected lesion regions. To produce more accurate medical reports and minimize the visual-and-linguistic differences, this model adopts an alternate learning strategy with two procedures that are knowledge pretraining and transferring. To be more precise, the knowledge pretraining procedure is to memorize the knowledge from medical texts, while the transferring procedure is to utilize the acquired knowledge for professional medical sentences generations through observations of medical images. In practice, for automatic medical report generation on the COVID-19 cases, we constructed a dataset of 368 medical findings in Chinese and 1104 chest CT scans from The First Affiliated Hospital of Jinan University, Guangzhou, China, and The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China. Besides, to alleviate the insufficiency of the COVID-19 training samples, our model was first trained on the large-scale Chinese CX-CHR dataset and then transferred to the COVID-19 CT dataset for further fine-tuning. The experimental results showed that Medical-VLBERT achieved state-of-the-art performances on terminology prediction and report generation with the Chinese COVID-19 CT dataset and the CX-CHR dataset. The Chinese COVID-19 CT dataset is available at https://covid19ct.github.io/.

Switching to electric vehicles can lead to significant reductions of PM2.5 and NO2 across China

Summary Transportation contributes to around one-fifth of global greenhouse gas emissions, while also causing severe air pollution. The conversion to electric vehicles (EVs) represents a major path to decarbonize the transport sector, with potentially significant co-benefits for human health. However, the scale of such co-benefits largely remains an empirical question and lacks observational evidence. The full lockdown in China during the coronavirus disease 2019 (COVID-19) pandemic provides an unprecedented real-world experiment to evaluate emission reduction potentials of a large-scale transition to EVs. Here, we utilize ground and satellite observations of air quality during the full lockdown to constrain predictions of a comprehensive chemical transport model and find that the substantial traffic reductions are near-linearly linked to reductions of PM2.5 (particles with an aerodynamic diameter ≤2.5 μm) and NO2. A further extrapolation of a full conversion to EVs shows a significant reduction of PM2.5 (30%–70%) and NO2 (30%–80%) in most of China. Our findings provide fact-based evidence of potential environmental benefits generated by fully switching to EVs.

Post-traumatic Growth Level and Its Influencing Factors Among Frontline Nurses During the COVID-19 Pandemic.

Objective: To assess post-traumatic growth (PTG) level and explore its influence factors among frontline nurses during the COVID-19 pandemic. Methods: From April 11th to 12th, 2020, a cross sectional study was conducted on 116 frontline nurses who had participated in fight against the COVID-19 in Wuhan city, China. General information and psychological discomfort were collected. Chinese version post-traumatic growth inventory with 20 items was applied to assess PTG level. Univariable analyses and multiple linear regression were performed to explore potential influencing factors of PTGI score. Results: The average score of PTGI in frontline nurses was 65.65 ± 11.50. In univariable analyses, gender, age, education level, marital status, living with parents, professional title, working years and professional psychological support was not statistically associated with the PTGI score. In both univariable and multivariable analyses, having support from family members and friends, being psychological comfort and having children and increased the PTGI score significantly. The three factors only explained 3.8% variance. Conclusion: Moderate PGT was observed in the frontline nurses who had battled against COVID-19. Social support and professional psychological intervention should be applied to further improve PTG level. Further studies with large sample size are required to explore more potential influencing factors.

A Chinese host genetic study discovered type I interferons and causality of cholesterol levels and WBC counts on COVID-19 severity (preprint)

As of early May 2021, the ongoing pandemic COVID-19 has caused over 160 million of infections and over 3 million deaths worldwide. Many risk factors, such as age, gender, and comorbidities, have been studied to explain the variable symptoms of infected patients. However, these effects may not fully account for the diversity in disease severity. Here, we present a comprehensive analysis of a broad range of patients laboratory and clinical assessments to investigate the genetic contributions to COVID-19 severity. By performing GWAS analysis, we discovered several concrete associations for laboratory features. Based on these findings, we performed Mendelian randomization (MR) analysis to investigate the causality of laboratory traits on disease severity. From the MR study, we identified two causal traits, cholesterol levels and WBC counts. The functional gene related to cholesterol levels is ApoE and people with particular ApoE genotype are more likely to have higher cholesterol levels, facilitating the process that SARS-CoV-2 binds on its receptor ACE2 and aggravating COVID-19 disease. The functional gene related to WBC counts is MHC system that plays a central role in the immune system. The host immune response to the SARS-CoV-2 infection greatly affects the patients severity status and clinical outcome. Additionally, our gene-based and GSEA analysis revealed interferon pathways, including type I interferon receptor binding, regulation of IFNA signaling, and SARS coronavirus and innate immunity. We hope that our work will make a contribution in studying the genetic mechanisms of disease illness and serve as useful reference for the clinical diagnosis and treatment of COVID-19.

Immune responses to SARS-CoV-2 infection in Humans and ACE2 humanized mice

The coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) represents a major public health threat worldwide. Insight into protective and pathogenic aspects of SARS-CoV-2 immune responses is critical to work out effective therapeutics and develop vaccines for controlling the disease. Here, we review the present literature describing the innate and adaptive immune responses including innate immune cells, cytokine responses, antibody responses and T cell responses against SARS-CoV-2 in human infection, as well as in AEC2-humanized mouse infection. We also summarize the now known and unknown about the role of the SARS-CoV-2 immune responses. By better understanding the mechanisms that drive the immune responses, we can tailor treatment strategies at specific disease stages and improve our response to this worldwide public health threat.

Surveillance of SARS-CoV-2 Antibody of Patients in Local Affected Area During the Lockdown (preprint)

Background: Serosurvalence is crucial in estimating the range of SARS-CoV-2 infections, predicting the possibility of another wave, and decide on a vaccination strategy. To understand the herd immunity after the COVID-19 pandemic, the seroprevalence was measured in 3062 individuals with or without COVID-19. Methods: The levels of SARS-CoV-2 antibody IgM and IgG were measured by the immuno-colloidal gold method.Results: The mean seroprevalence for IgM and IgG in all participants was 2.81% and 7.51%, respectively. The positive rate of IgG was significantly higher in women than in men (P<0.05). The highest positive rate of IgM was observed in 41-60 years of age (3.49%), while the highest seroprevalence for IgG was observed in persons >60 years of age (8.61%). The positive rates of IgM and IgG in the convalescent patients were 31.82% and 77.27%, respectively, which was significantly higher than individuals with suspected syndromes or individuals without any clinical signs (P<0.01). Seroprevalence for IgG in medical staff was markedly higher than those in residents. The seroprevalence in patients with various comorbidity was no significant difference (P>0.05).Conclusions: The low positive rate of the SARS-CoV-2 IgM and NA test indicated that the SARS-CoV-2 outbreak is subsiding after three months, and the possibility of reintroduction of the virus from an unidentified natural reservoir is low. Seroprevalence provides the information for humoral immunity and vaccine in the future.

MedDG: A Large-scale Medical Consultation Dataset for Building Medical Dialogue System (preprint)

Developing conversational agents to interact with patients and provide primary clinical advice has attracted increasing attention due to its huge application potential, especially in the time of COVID-19 Pandemic. However, the training of end-to-end neural-based medical dialogue system is restricted by an insufficient quantity of medical dialogue corpus. In this work, we make the first attempt to build and release a large-scale high-quality Medical Dialogue dataset related to 12 types of common Gastrointestinal diseases named MedDG, with more than 17K conversations collected from the online health consultation community. Five different categories of entities, including diseases, symptoms, attributes, tests, and medicines, are annotated in each conversation of MedDG as additional labels. To push forward the future research on building expert-sensitive medical dialogue system, we proposes two kinds of medical dialogue tasks based on MedDG dataset. One is the next entity prediction and the other is the doctor response generation. To acquire a clear comprehension on these two medical dialogue tasks, we implement several state-of-the-art benchmarks, as well as design two dialogue models with a further consideration on the predicted entities. Experimental results show that the pre-train language models and other baselines struggle on both tasks with poor performance in our dataset, and the response quality can be enhanced with the help of auxiliary entity information. From human evaluation, the simple retrieval model outperforms several state-of-the-art generative models, indicating that there still remains a large room for improvement on generating medically meaningful responses.

COVID-19 Full Lockdowns Expose Significant Air Quality Benefits Towards Electric Vehicles in China (preprint)

Electric vehicles (EVs) represent a major path for global decarbonization, improvement of air quality and promotion of renewable energy. To tackle the COVID-19 pandemic, China imposed full lockdowns and thorough travel restrictions. This event represents an unprecedented inadvertent geoengineering experiment in vehicle emissions, emulating transition to EVs. Here we novelly exploited observations of air quality during the full lockdown to constrain predictions of a comprehensive chemical transport model. Large traffic flux reductions were near-linearly linked to reductions of NO 2 and PM 2.5 (correlation = 0.491 ~ 0.626). Extrapolating to a full conversion to EV results in a large reduction of PM 2.5 (30 ~ 70%) in most of central and south eastern China. A smaller reduction of PM 2.5 (10 ~ 20%) in Beijing and Tianjin was achievable due to the presence of major industrial emission sources which cause haze at a regional scale. The potential reductions in NO 2 were 40 ~ 90% in most of the megacities. At this present crossroads of policy, our findings reveal timely evidence supporting the transition towards renewable energy.Funding: This study is supported by the Department of Science and Technology of China (No. 2016YFC0202702, 2018YFC0213506 and 2018YFC0213503), National Research Program for Key Issues in Air Pollution Control in China (No. DQGG0107) and National Natural Science Foundation of China (No. 21577126, 41561144004, 21625701). Pengfei Li is supported by Initiation Fund for Introducing Talents of Hebei Agricultural University (412201904) and Youth Top Fund of Universities in Hebei Province (BJ2020032). Conflict of Interest: Authors declare no competing interests.

Individualism and the Fight against COVID-19 (preprint)

We demonstrate that COVID-19 displays a spatial discontinuity along the former East-West German border and German virus-fighting policies are more effective in former pro-state and pro-collectivism East German districts. In an international setting using the number of Nobel-Prize winners as an instrument, we show that individualism aggravates the severity of COVID-19 through reducing the effectiveness of social distancing and mobility restriction policies. We conclude that individualism potentially impedes the containment of COVID-19. A greater reluctance among people in more individualistic cultures to heed virus-fighting policies can potentially impose a negative public health externality during a pandemic.

Comparison of three TaqMan Real-Time Reverse Transcription-PCR assays in detecting SARS-CoV-2 (preprint)

Quick and accurate detection of SARS-CoV-2 is critical for COVID-19 control. Dozens of real-time reverse transcription PCR (qRT-PCR) assays have been developed to meet the urgent need of COVID-19 control. However, methodological comparisons among the developed qRT-PCR assays are limited. In the present study, we evaluated the sensitivity, specificity, amplification efficiency, and linear detection ranges of three qRT-PCR assays, including the assays developed by our group (IPBCAMS), and the assays recommended by WHO and China CDC (CCDC). The three qRT-PCR assays exhibited similar sensitivities, with the limit of detection (LOD) at about 10 copies per reaction (except the ORF 1b gene assay in CCDC assays with a LOD at about 100 copies per reaction). No cross reaction with other respiratory viruses were observed in all of the three qRT-PCR assays. Wide linear detection ranges from 106 to 101 copies per reaction and acceptable reproducibility were obtained. By using 25 clinical specimens, the N gene assay of IPBCAMS assays and CCDC assays performed better (with detection rates of 92% and 100%, respectively) than that of the WHO assays (with a detection rate of 60%), and the ORF 1b gene assay in IPBCAMS assays performed better (with a detection rate of 64%) than those of the WHO assays and the CCDC assays (with detection rates of 48% and 20%, respectively). In conclusion, the N gene assays of CCDC assays and IPBCAMS assays and the ORF 1b gene assay of IPBCAMS assays were recommended for qRT-PCR screening of SARS-CoV-2.Competing Interest StatementThe authors have declared no competing interest.View Full Text

SARS-CoV-2 Antibody responses do not predict COVID-19 disease severity (preprint)

Background: Initial reports indicate adequate performance of some serological-based SARS-CoV-2 assays. However, additional studies are required to facilitate interpretation of results, including how antibody levels impact immunity and disease course. Methods: In this study, a total of 968 subjects were tested for IgG antibodies reactive to SARS-CoV-2. We confirmed analytic specificity using 656 plasma samples from healthy donors, 49 sera from patients with rheumatic disease, and 90 specimens from individuals positive for PCR-based respiratory viral panel. One-hundred seventy-three cases of confirmed or suspected SARS-CoV-2 were tested for IgG. A subgroup of 37 SARS-CoV-2 PCR-positive cases was tested for nucleocapsid-specific IgM antibody using an in-house developed microarray method. Antibody levels were compared between disease severity groups. Results: All specificity specimens were negative for SARS-CoV-2 IgG antibodies (0/656, 0%). Cross reactivity was not detected in specimens with antinuclear antibodies and rheumatoid factor, or cases with previous diagnosis of viral infection including human coronavirus. Positive agreement of IgG with PCR was 83% of samples confirmed to be more than 14 days from symptom onset, with less than 100% sensitivity attributable to a case with severe immunosuppression. Virus-specific IgM was positive in a higher proportion of cases less than 3 days from symptom onset. No association was observed between mild and severe disease course with respect to IgG and IgM levels. Conclusions: The studied SARS-CoV-2 IgG assay had 100% specificity and no adverse cross-reactivity. Index values of IgG and IgM antibodies did not predict disease severity in our patient population.