Unsupervised Machine Learning to Detect and Characterize Barriers to Pre-exposure Prophylaxis Therapy: Multiplatform Social Media Study.

Background: Among racial and ethnic minority groups, the risk of HIV infection is an ongoing public health challenge. Pre-exposure prophylaxis (PrEP) is highly effective for preventing HIV when taken as prescribed. However, there is a need to understand the experiences, attitudes, and barriers of PrEP for racial and ethnic minority populations and sexual minority groups. Objective: This infodemiology study aimed to leverage big data and unsupervised machine learning to identify, characterize, and elucidate experiences and attitudes regarding perceived barriers associated with the uptake and adherence to PrEP therapy. This study also specifically examined shared experiences from racial or ethnic populations and sexual minority groups. Methods: The study used data mining approaches to collect posts from popular social media platforms such as Twitter, YouTube, Tumblr, Instagram, and Reddit. Posts were selected by filtering for keywords associated with PrEP, HIV, and approved PrEP therapies. We analyzed data using unsupervised machine learning, followed by manual annotation using a deductive coding approach to characterize PrEP and other HIV prevention-related themes discussed by users. Results: We collected 522,430 posts over a 60-day period, including 408,637 (78.22%) tweets, 13,768 (2.63%) YouTube comments, 8728 (1.67%) Tumblr posts, 88,177 (16.88%) Instagram posts, and 3120 (0.6%) Reddit posts. After applying unsupervised machine learning and content analysis, 785 posts were identified that specifically related to barriers to PrEP, and they were grouped into three major thematic domains: provider level (13/785, 1.7%), patient level (570/785, 72.6%), and community level (166/785, 21.1%). The main barriers identified in these categories included those associated with knowledge (lack of knowledge about PrEP), access issues (lack of insurance coverage, no prescription, and impact of COVID-19 pandemic), and adherence (subjective reasons for why users terminated PrEP or decided not to start PrEP, such as side effects, alternative HIV prevention measures, and social stigma). Among the 785 PrEP posts, we identified 320 (40.8%) posts where users self-identified as racial or ethnic minority or as a sexual minority group with their specific PrEP barriers and concerns. Conclusions: Both objective and subjective reasons were identified as barriers reported by social media users when initiating, accessing, and adhering to PrEP. Though ample evidence supports PrEP as an effective HIV prevention strategy, user-generated posts nevertheless provide insights into what barriers are preventing people from broader adoption of PrEP, including topics that are specific to 2 different groups of sexual minority groups and racial and ethnic minority populations. Results have the potential to inform future health promotion and regulatory science approaches that can reach these HIV and AIDS communities that may benefit from PrEP.

Public Figure Vaccination Rhetoric and Vaccine Hesitancy: Retrospective Twitter Analysis.

Background: Social media has emerged as a critical mass communication tool, with both health information and misinformation now spread widely on the web. Prior to the COVID-19 pandemic, some public figures promulgated anti-vaccine attitudes, which spread widely on social media platforms. Although anti-vaccine sentiment has pervaded social media throughout the COVID-19 pandemic, it is unclear to what extent interest in public figures is generating anti-vaccine discourse. Objective: We examined Twitter messages that included anti-vaccination hashtags and mentions of public figures to assess the connection between interest in these individuals and the possible spread of anti-vaccine messages. Methods: We used a data set of COVID-19-related Twitter posts collected from the public streaming application programming interface from March to October 2020 and filtered it for anti-vaccination hashtags "antivaxxing," "antivaxx," "antivaxxers," "antivax," "anti-vaxxer," "discredit," "undermine," "confidence," and "immune." Next, we applied the Biterm Topic model (BTM) to output topic clusters associated with the entire corpus. Topic clusters were manually screened by examining the top 10 posts most highly correlated in each of the 20 clusters, from which we identified 5 clusters most relevant to public figures and vaccination attitudes. We extracted all messages from these clusters and conducted inductive content analysis to characterize the discourse. Results: Our keyword search yielded 118,971 Twitter posts after duplicates were removed, and subsequently, we applied BTM to parse these data into 20 clusters. After removing retweets, we manually screened the top 10 tweets associated with each cluster (200 messages) to identify clusters associated with public figures. Extraction of these clusters yielded 768 posts for inductive analysis. Most messages were either pro-vaccination (n=329, 43%) or neutral about vaccination (n=425, 55%), with only 2% (14/768) including anti-vaccination messages. Three main themes emerged: (1) anti-vaccination accusation, in which the message accused the public figure of holding anti-vaccination beliefs; (2) using "anti-vax" as an epithet; and (3) stating or implying the negative public health impact of anti-vaccination discourse. Conclusions: Most discussions surrounding public figures in common hashtags labelled as "anti-vax" did not reflect anti-vaccination beliefs. We observed that public figures with known anti-vaccination beliefs face scorn and ridicule on Twitter. Accusing public figures of anti-vaccination attitudes is a means of insulting and discrediting the public figure rather than discrediting vaccines. The majority of posts in our sample condemned public figures expressing anti-vax beliefs by undermining their influence, insulting them, or expressing concerns over public health ramifications. This points to a complex information ecosystem, where anti-vax sentiment may not reside in common anti-vax-related keywords or hashtags, necessitating further assessment of the influence that public figures have on this discourse.

Impact of COVID-19 on HIV Prevention Access: A Multi-platform Social Media Infodemiology Study.

This study seeks to identify and characterize key barriers associated with PrEP therapy as self-reported by users on social media platforms. We used data mining and unsupervised machine learning approaches to collect and analyze COVID-19 and PrEP-related posts from three social media platforms including Twitter, Reddit, and Instagram. Predominant themes detected by unsupervised machine learning and manual annotation included users expressing uncertainty about PrEP treatment adherence due to COVID-19, challenges related to accessibility of clinics, concerns about PrEP costs and insurance coverage, perceived lower HIV risk leading to lack of adherence, and misinformation about PrEP use for COVID-19 prevention.

Using Co-Design to Explore New Trends in Future Kitchen Designs: An Exploratory Workshop Study of College Students in China.

The current COVID-19 pandemic is exacerbating the challenges facing human society. The public is increasingly concerned about the health and well-being of individuals, families, and communities. To enhance human health and well-being, user expectations for the future need to be understood. The kitchen, a central area of a home, is closely related to healthy living. In this study, a series of seven exploratory workshops were held at a Chinese university using co-design to understand the expectations and thinking of Chinese college students about the future of kitchen design in terms of health and well-being. A methodological innovation was introduced in co-design workshops, where participants were asked to imagine, discuss, and sketch concepts together to stimulate creative design. A six-dimensional tentative model of future kitchen expectations, including 34 sub-themes, was constructed based on the data analysis to explore the expected characteristics of kitchens. These dimensions include intelligent technologies and interaction experiences, health and well-being, inclusivity and extensibility, ecosystem circulation and sustainability, emotional and meaningful experience, and spatial planning and aesthetic experience. The resulting model provides valuable insights into the expectations of future users, providing direction and systematic strategies for future kitchens along the six-dimensional characteristics. Future kitchens, if the younger generation is to adopt them, need to positively affect users' lives and meet their health and well-being standards.

Efficacy and safety of Qi and Blood Tonic Chinese Medicines in the treatment of COVID-19: A protocol for systematic review and meta-analysis.

BACKGROUND: Coronavirus disease in 2019 (COVID-19) is a sudden public event affecting all human beings, with the rapid transmission, extensive groups affected, many complications, and high mortality. Traditional Chinese Medicine has a long history of preventing and treating infectious diseases, and numerous studies have shown that Traditional Chinese Medicine, especially herbal medicine, has a positive effect on the prevention, treatment, and post-healing recovery of this COVID-19, and herbal medicines to supplement qi and blood often occupy a certain proportion of it. However, there is no relevant meta-analysis to date. Therefore, this study aims to evaluate the efficacy and safety of qi and blood tonic herbal medicines in the treatment of COVID-19 through Systematic Review and meta-analysis to provide a reference basis for widespread clinical application. METHODS: We will search from the following databases for the period from the time of database construction to March 1st, 2023. The English databases include: PubMed, MEDLINE, EMBASE, Cochrane library, WOS, Google Scholar, and CENTRAL; The Chinese databases include: China National Knowledge Infrastructure, China Biomedical Literature Database, Technology Journal Database, and Wanfang. Randomized controlled trials in English or Chinese that include Chinese herbal medicines for tonifying Qi and Blood in the treatment of patients with COVID-19 will be included. Data were independently screened and collected by 2 investigators. The risk of bias for each trial was assessed using the Cochrane Risk of Bias Tool 2.0. RevMan 5.3 software was used for the meta-analysis of the data. Primary outcome indicators included cure, mortality, and exacerbation rates (change in disease severity category, patient admission to ICU, etc.). Secondary outcome indicators included recovery rate or duration of major symptoms (e.g., fever, cough, fatigue, and weakness, etc.), rate or duration of nucleic acid conversion for severe acute respiratory syndrome coronavirus-2, improvement or recovery of chest CT performance, length of hospital stay, and other adverse events. RESULTS: This protocol adheres to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses-P guidelines to ensure clarity and completeness of reporting in all phases of the systematic review. CONCLUSION: This study will provide evidence regarding the efficacy and safety of Qi and Blood Tonic Chinese Medicines for the treatment of COVID-19. PROSPERO REGISTRATION NUMBER: CRD42022361822 (https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022361822).

Radiative effects and feedbacks of anthropogenic aerosols on boundary layer meteorology and fine particulate matter during the COVID-19 lockdown over China.

The COVID-19 epidemic has exerted significant impacts on human health, social and economic activities, air quality and atmospheric chemistry, and potentially on climate change. In this study, an online coupled regional climate-chemistry-aerosol model (RIEMS-Chem) was applied to explore the direct, indirect, and feedback effects of anthropogenic aerosols on radiation, boundary layer meteorology, and fine particulate matter during the COVID-19 lockdown period from 23 January to 8 April 2020 over China. Model performance was validated against a variety of observations for meteorological variables, PM2.5 and its chemical components, aerosol optical properties, as well as shortwave radiation flux, which demonstrated that RIEMS-Chem was able to reproduce the spatial distribution and temporal variation of the above variables reasonably well. During the study period, direct radiative effect (DRE) of anthropogenic aerosols was stronger than indirect radiative effect (IRE) in most regions north of the Yangtze River, whereas IRE dominated over DRE in the Yangtze River regions and South China. In North China, DRE induced larger changes in meteorology and PM2.5 than those induced by IRE, whereas in South China, the changes by IRE were remarkably larger than those by DRE. Emission reduction alone during the COVID-19 lockdown reduced PM2.5 concentration by approximately 32 % on average over East China. As a result, DRE at the surface was weakened by 15 %, whereas IRE changed little over East China, leading to a decrease in total radiative effect (TRE) by approximately 7 % in terms of domain average. The DRE-induced changes in meteorology and PM2.5 were weakened due to emission reduction, whereas the IRE-induced changes were almost the same between the cases with and without emission reductions. By aerosol radiative and feedback effects, the COVID-19 emission reductions resulted in 0.06 °C and 0.04 °C surface warming, 1.6 and 4.0 µg m-3 PM2.5 decrease, 0.4 and 1.3 mm precipitation increase during the lockdown period in 2020 in terms of domain average over North China and South China, respectively, whereas the lockdown caused negligible changes on average over East Asia.

COVID-19 vaccination influences subtypes of γδ-T cells during pregnancy.

Up to now, there has been insufficient clinical data to support the safety and effects of vaccination on pregnancy post COVID-19 vaccination. The γδ-T cells are considered an important component in the immune system to fight against viral infection and exhibit critical roles throughout the pregnancy period. However, the immunological roles of γδ-T cells in pregnant women with the COVID-19 vaccination remain unclear. Therefore, the objective of this study is to investigate the alteration of frequency and expression pattern of activation receptors and inhibitory receptors in γδ-T cell and its subsets in peripheral blood samples collected from non-pregnant vaccinated women, vaccinated pregnant women, and unvaccinated pregnant women. Our findings indicated that the frequency of CD3+γδ-T+ cells is lower in vaccinated pregnant women than in unvaccinated pregnant women. But no significant difference was found in the frequency of CD3+γδ-T+ cells between non-pregnant vaccinated women and vaccinated pregnant women. In addition, there were no significant differences in the frequencies of CD3+γδ-T+Vδ1+T cells, CD3+γδ-T+Vδ2+T cells, CD3+γδ-T+Vδ1-Vδ2-T cells, and Vδ1+T cell/Vδ2+T cell ratio between the pregnant women with or without COVID-19 vaccination. Similar results were found after comparing non-pregnant and pregnant women who received the COVID-19 vaccine. However, there was a significant difference in the fraction of Vδ1-Vδ2-T cells in CD3+γδ-T+ cells between non-pregnant vaccinated women and vaccinated pregnant women. The frequency of NKG2D+ cells in Vδ2+T cells was not significantly different in the vaccinated pregnant women when compared to that in unvaccinated pregnant women or non-pregnant vaccinated women. But the percentage of NKG2D+ cells in Vδ1+T cells was the lowest in pregnant women after COVID-19 vaccination. Furthermore, down-regulation of NKP46 and NKP30 were found in Vδ2+T and Vδ1+T cells in the vaccinated pregnant women, respectively. After the vaccination, up-regulation of PD-1 expression in Vδ1+T cells and Vδ2+T cells indicated γδ-T cells could respond to COVID-19 vaccination and display an exhausted phenotype following activation. In conclusion, COVID-19 vaccination influences subtypes of γδ-T cells during pregnancy, but the side effects might be limited. The phenotypical changes of Vδ1+T cells and Vδ2+T cells will be a promising predictor for evaluating the clinical outcome of the COVID-19 vaccine.

Comparative Study on International Research Hotspots and National-Level Policy Keywords of Dynamic Disaster Monitoring and Early Warning in China (2000-2021).

For more than 20 years, disaster dynamic monitoring and early warning have achieved orderly and sustainable development in China, forming a systematic academic research system and top-down policy design, which are inseparable from the research of China's scientific community and the promotion of government departments. In the past, most of the research on dynamic disaster monitoring and early warning focused on specific research in a certain field, scene, and discipline, while a few studies focused on research review or policy analysis, and few studies combined macro and meso research reviews in academia with national policy analysis for comparative analysis. It is necessary and urgent to explore the interaction between scholars' research and policy deployment, which can bring theoretical contributions and policy references to the top-down design, implementation promotion, and academic research of China's dynamic disaster monitoring and early warning. Based on 608 international research articles on dynamic disaster monitoring and early warning published by Chinese scholars from 2000-2021 and 187 national policy documents published during this period, this paper conducts a comparative analysis between the knowledge maps of international research hotspots and the co-occurrence maps of policy keywords on dynamic disaster monitoring and early warning. The research shows that in the stage of initial development (2000-2007), international research articles are few and focused, and research hotspots are somewhat alienated from policy keywords. In the stage of rising development (2008-2015), after the Wenchuan earthquake, research hotspots are closely related to policy keywords, mainly in the fields of geology, engineering disasters, meteorological disasters, natural disasters, etc. Meanwhile, research hotspots also focus on cutting-edge technologies and theories, while national-level policy keywords focus more on overall governance and macro promotion, but the two are gradually closely integrated. In the stage of rapid development (2016-2021), with the continuous attention and policy promotion of the national government, the establishment of the Ministry of Emergency Management, and the gradual establishment and improvement of the disaster early warning and monitoring system, research hotspots and policy keywords are integrated and overlapped with each other, realizing the organic linkage and mutual promotion between academic research and political deployment. The motivation, innovation, integration, and transformation of dynamic disaster monitoring and early warning are promoted by both policy and academic research. The institutions that issue policies at the national level include the State Council and relevant departments, the Ministry of Emergency Management, the Ministry of Water Resources, and other national ministries and commissions. The leading affiliated institutions of scholars' international research include China University of Mining and Technology, Chinese Academy of Sciences, Wuhan University, Shandong University of Science and Technology, and other institutions. The disciplines involved are mainly multidisciplinary geosciences, environmental sciences, electrical and electronic engineering, remote sensing, etc. It is worth noting that in the past two to three years, research and policies focusing on COVID-19, public health, epidemic prevention, environmental governance, and emergency management have gradually increased.

COVID-19 vaccination influences subtypes of γδ-T cells during pregnancy

Up to now, there has been insufficient clinical data to support the safety and effects of vaccination on pregnancy post COVID-19 vaccination. The γδ-T cells are considered an important component in the immune system to fight against viral infection and exhibit critical roles throughout the pregnancy period. However, the immunological roles of γδ-T cells in pregnant women with the COVID-19 vaccination remain unclear. Therefore, the objective of this study is to investigate the alteration of frequency and expression pattern of activation receptors and inhibitory receptors in γδ-T cell and its subsets in peripheral blood samples collected from non-pregnant vaccinated women, vaccinated pregnant women, and unvaccinated pregnant women. Our findings indicated that the frequency of CD3+γδ-T+ cells is lower in vaccinated pregnant women than in unvaccinated pregnant women. But no significant difference was found in the frequency of CD3+γδ-T+ cells between non-pregnant vaccinated women and vaccinated pregnant women. In addition, there were no significant differences in the frequencies of CD3+γδ-T+Vδ1+T cells, CD3+γδ-T+Vδ2+T cells, CD3+γδ-T+Vδ1-Vδ2-T cells, and Vδ1+T cell/Vδ2+T cell ratio between the pregnant women with or without COVID-19 vaccination. Similar results were found after comparing non-pregnant and pregnant women who received the COVID-19 vaccine. However, there was a significant difference in the fraction of Vδ1-Vδ2-T cells in CD3+γδ-T+ cells between non-pregnant vaccinated women and vaccinated pregnant women. The frequency of NKG2D+ cells in Vδ2+T cells was not significantly different in the vaccinated pregnant women when compared to that in unvaccinated pregnant women or non-pregnant vaccinated women. But the percentage of NKG2D+ cells in Vδ1+T cells was the lowest in pregnant women after COVID-19 vaccination. Furthermore, down-regulation of NKP46 and NKP30 were found in Vδ2+T and Vδ1+T cells in the vaccinated pregnant women, respectively. After the vaccination, up-regulation of PD-1 expression in Vδ1+T cells and Vδ2+T cells indicated γδ-T cells could respond to COVID-19 vaccination and display an exhausted phenotype following activation. In conclusion, COVID-19 vaccination influences subtypes of γδ-T cells during pregnancy, but the side effects might be limited. The phenotypical changes of Vδ1+T cells and Vδ2+T cells will be a promising predictor for evaluating the clinical outcome of the COVID-19 vaccine.

Investigation of the influence of mineral dust on airborne particulate matter during the COVID-19 epidemic in spring 2020 over China.

A regional air quality model system (RAQMS) driven by the Weather Research and Forecasting model (WRF) is applied to investigate the distribution and evolution of mineral dust and anthropogenic aerosols over China in April 2020, when air quality was improved due to reduced human activity during the COVID-19 epidemic, whereas dust storms began to attack China and deteriorated air quality. A dust deflation model was developed and improved mineral dust prediction. Model validation demonstrated that RAQMS was able to reproduce PM10, PM2.5 and aerosol components reasonably well. China suffered from three dust events in April 2020, with the maximum hourly PM10 concentrations exceeding 700 µg m-3 in downwind cities over the North China Plain (NCP). Mineral dust dominated PM10 mass (>80%) over the Gobi deserts in north and west China, while it comprised approximately 30-50% of PM10 over wide areas of east China. The domain and monthly mean dust mass fractions in PM10 were estimated to be 47% and 43% over the North China Plain and east China, respectively. On average, mineral dust contributed up to 22% and 21% of PM2.5 mass over the North China Plain and east China in April 2020, respectively. Sulfate and nitrate produced by heterogeneous chemical reactions on dust surface accounted for approximately 9% and 13% of secondary inorganic aerosols (SIA) concentration over the North China Plain and east China, respectively. The results from this study demonstrated that mineral dust made an important contribution to particulate matter mass during the COVID-19 epidemic in spring 2020 over China.

Molecular basis of receptor binding and antibody neutralization of Omicron.

The SARS-CoV-2 Omicron variant exhibits striking immune evasion and is spreading rapidly worldwide. Understanding the structural basis of the high transmissibility and enhanced immune evasion of Omicron is of high importance. Here, using cryo-electron microscopy, we present both the closed and the open states of the Omicron spike (S) protein, which appear more compact than the counterparts of the G614 strain1, potentially related to enhanced inter-protomer and S1-S2 interactions induced by Omicron residue substitution. The closed state showing dominant population may indicate a conformational masking mechanism for the immune evasion of Omicron. Moreover, we captured three states for the Omicron S-ACE2 complex, revealing that the substitutions on the Omicron RBM result in new salt bridges and hydrogen bonds, more favourable electrostatic surface properties, and an overall strengthened S-ACE2 interaction, in line with the observed higher ACE2 affinity of Omicron S than of G614. Furthermore, we determined the structures of Omicron S in complex with the Fab of S3H3, an antibody that is able to cross-neutralize major variants of concern including Omicron, elucidating the structural basis for S3H3-mediated broad-spectrum neutralization. Our findings shed light on the receptor engagement and antibody neutralization or evasion of Omicron and may also inform the design of broadly effective vaccines against SARS-CoV-2.

Automated lung ultrasound scoring for evaluation of coronavirus disease 2019 pneumonia using two-stage cascaded deep learning model.

Coronavirus disease 2019 (COVID-19) pneumonia has erupted worldwide, causing massive population deaths and huge economic losses. In clinic, lung ultrasound (LUS) plays an important role in the auxiliary diagnosis of COVID-19 pneumonia. However, the lack of medical resources leads to the low using efficiency of the LUS, to address this problem, a novel automated LUS scoring system for evaluating COVID-19 pneumonia based on the two-stage cascaded deep learning model was proposed in this paper. 18,330 LUS images collected from 26 COVID-19 pneumonia patients were successfully assigned scores by two experienced doctors according to the designed four-level scoring standard for training the model. At the first stage, we made a secondary selection of these scored images through five ResNet-50 models and five-fold cross validation to obtain the available 12,949 LUS images which were highly relevant to the initial scoring results. At the second stage, three deep learning models including ResNet-50, Vgg-19, and GoogLeNet were formed the cascaded scored model and trained using the new dataset, whose predictive result was obtained by the voting mechanism. In addition, 1000 LUS images collected another 5 COVID-19 pneumonia patients were employed to test the model. Experiments results showed that the automated LUS scoring model was evaluated in terms of accuracy, sensitivity, specificity, and F1-score, being 96.1%, 96.3%, 98.8%, and 96.1%, respectively. They proved the proposed two-stage cascaded deep learning model could automatically score an LUS image, which has great potential for application to the clinics on various occasions.

Running online experiments using web-conferencing software

We report the results of a novel protocol for running online experiments using a combination of an online experimental platform in parallel with web-conferencing software in two formats—with and without subject webcams—to improve subjects’ attention and engagement. We compare the results between our online sessions with the offline (lab) sessions of the same experiment. We find that both online formats lead to comparable subject characteristics and performance as the offline (lab) experiment. However, the webcam-on protocol has less noisy data, and hence better statistical power, than the protocol without a webcam. The webcam-on protocol can detect reasonable effect sizes with a comparable sample size as in the offline (lab) protocol. Supplementary Information The online version contains supplementary material available at 10.1007/s40881-021-00112-w.

Quantitative Analysis and Automated Lung Ultrasound Scoring for Evaluating COVID-19 Pneumonia With Neural Networks.

As being radiation-free, portable, and capable of repetitive use, ultrasonography is playing an important role in diagnosing and evaluating the COVID-19 Pneumonia (PN) in this epidemic. By virtue of lung ultrasound scores (LUSS), lung ultrasound (LUS) was used to estimate the excessive lung fluid that is an important clinical manifestation of COVID-19 PN, with high sensitivity and specificity. However, as a qualitative method, LUSS suffered from large interobserver variations and requirement for experienced clinicians. Considering this limitation, we developed a quantitative and automatic lung ultrasound scoring system for evaluating the COVID-19 PN. A total of 1527 ultrasound images prospectively collected from 31 COVID-19 PN patients with different clinical conditions were evaluated and scored with LUSS by experienced clinicians. All images were processed via a series of computer-aided analysis, including curve-to-linear conversion, pleural line detection, region-of-interest (ROI) selection, and feature extraction. A collection of 28 features extracted from the ROI was specifically defined for mimicking the LUSS. Multilayer fully connected neural networks, support vector machines, and decision trees were developed for scoring LUS images using the fivefold cross validation. The model with 128×256 two fully connected layers gave the best accuracy of 87%. It is concluded that the proposed method could assess the ultrasound images by assigning LUSS automatically with high accuracy, potentially applicable to the clinics.

A longitudinal and geospatial analysis of COVID-19 tweets during the early outbreak period in the United States.

INTRODUCTION: Early reports of COVID-19 cases and deaths may not accurately convey community-level concern about the pandemic during early stages, particularly in the United States where testing capacity was initially limited. Social media interaction may elucidate public reaction and communication dynamics about COVID-19 in this critical period, during which communities may have formulated initial conceptions about the perceived severity of the pandemic. METHODS: Tweets were collected from the Twitter public API stream filtered for keywords related to COVID-19. Using a pre-existing training set, a support vector machine (SVM) classifier was used to obtain a larger set of geocoded tweets with characteristics of user self-reporting COVID-19 symptoms, concerns, and experiences. We then assessed the longitudinal relationship between identified tweets and the number of officially reported COVID-19 cases using linear and exponential regression at the U.S. county level. Changes in tweets that included geospatial clustering were also assessed for the top five most populous U.S. cities. RESULTS: From an initial dataset of 60 million tweets, we analyzed 459,937 tweets that contained COVID-19-related keywords that were also geolocated to U.S. counties. We observed an increasing number of tweets throughout the study period, although there was variation between city centers and residential areas. Tweets identified as COVID-19 symptoms or concerns appeared to be more predictive of active COVID-19 cases as temporal distance increased. CONCLUSION: Results from this study suggest that social media communication dynamics during the early stages of a global pandemic may exhibit a number of geospatial-specific variations among different communities and that targeted pandemic communication is warranted. User engagement on COVID-19 topics may also be predictive of future confirmed case counts, though further studies to validate these findings are needed.

A COVID-19 risk score combining chest CT radiomics and clinical characteristics to differentiate COVID-19 pneumonia from other viral pneumonias.

With the continued transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) throughout the world, identification of highly suspected COVID-19 patients remains an urgent priority. In this study, we developed and validated COVID-19 risk scores to identify patients with COVID-19. In this study, for patient-wise analysis, three signatures, including the risk score using radiomic features only, the risk score using clinical factors only, and the risk score combining radiomic features and clinical variables, show an excellent performance in differentiating COVID-19 from other viral-induced pneumonias in the validation set. For lesion-wise analysis, the risk score using three radiomic features only also achieved an excellent AUC value. In contrast, the performance of 130 radiologists based on the chest CT images alone without the clinical characteristics included was moderate as compared to the risk scores developed. The risk scores depicting the correlation of CT radiomics and clinical factors with COVID-19 could be used to accurately identify patients with COVID-19, which would have clinically translatable diagnostic and therapeutic implications from a precision medicine perspective.

Characterizing twitter user topics and communication network dynamics of the “Liberate” movement during COVID-19 using unsupervised machine learning and social network analysis

This paper analyzes online user conversation topics and discourse on Twitter related to the “Liberate” Protest movement in reaction to social distancing guidelines at the early stages of the COVID-19 pandemic. Interdisciplinary approaches in big data, machine learning, content analysis, and social network analysis (SNA) were used to characterize the communicative behavior, conversation themes, and network structures of Liberate protest supporters and non-supporters. Tweets were content coded and grouped within topic clusters produced from an unsupervised machine learning algorithm using natural language processing. An analysis of topic clusters found that tweets that support the protests are highly concentrated and have higher volumes of replicated tweets. Protest Supporters were also more likely to retweet other users while Non-Supporters were more likely to include a URL from an outside media source and produce a unique tweet. SNA was also used to assess the characteristics of retweet networks and found that the Protester Supporter network had a more centralized structure and was strongly influenced by a political organization, in contrast to the Non-Supporter network that had a larger number of smaller and more evenly-sized nodes and more driven by media personalities and commentators. Collectively, these characteristics indicate that protest supporters had more centralized, consistent and disseminated discourse protesting COVID-19 social distancing requirements compared to non-supporters who were more diverse in their criticism of the Liberate movement and generally more fragmented in their support of public health measures. Results from this study provide important insights into pandemic communication dynamics of opposing twitter communities, including in the context of those who oppose and support public health measures in a highly politicized social and online environment. Results are important in the context of assessing the messages, communication propagation and overall activities of social media communities in response to basic public health measures needed to contain this post-digital era global pandemic.

Traditional Chinese medicine combined with pulmonary drug delivery system and idiopathic pulmonary fibrosis: Rationale and therapeutic potential.

Idiopathic pulmonary fibrosis (IPF) is a progressive pulmonary interstitial inflammatory disease of unknown etiology, and is also a sequela in severe patients with the Coronavirus Disease 2019 (COVID-19). Nintedanib and pirfenidone are the only two known drugs which are conditionally recommended for the treatment of IPF by the FDA. However, these drugs pose some adverse side effects such as nausea and diarrhoea during clinical applications. Therefore, it is of great value and significance to identify effective and safe therapeutic drugs to solve the clinical problems associated with intake of western medicine. As a unique medical treatment, Traditional Chinese Medicine (TCM) has gradually exerted its advantages in the treatment of IPF worldwide through a multi-level and multi-target approach. Further, to overcome the current clinical problems of oral and injectable intakes of TCM, pulmonary drug delivery system (PDDS) could be designed to reduce the systemic metabolism and adverse reactions of the drug and to improve the bioavailability of drugs. Through PubMed, Google Scholar, Web of Science, and CNKI, we retrieved articles published in related fields in recent years, and this paper has summarized twenty-seven Chinese compound prescriptions, ten single TCM, and ten active ingredients for effective prevention and treatment of IPF. We also introduce three kinds of inhaling PDDS, which supports further research of TCM combined with PDDS to treat IPF.