Winter-autumn air pollution control plan in North China modified the PM2.5 compositions and sources in Central China

The additional impact of emission-reduction measures in North China (NC) during autumn and winter on the air quality of downwind regions is an interesting but less addressed topic. The mass concentrations of routine air pollutants, the chemical compositions, and sources of fine particles (PM2.5) for January 2018, 2019, and 2020 at a megacity of Central China were identified, and meteorology-isolated by a machine-learning technique. Their variations were classified according to air mass direction. An unexpectedly sharp increase in emission-related PM2.5 by 22.7% (18.0 mug m-3) and 25.7% (19.4 mug m-3) for air masses from local and NC in 2019 was observed compared to those of 2018. Organic materials exhibited the highest increase in PM2.5 compositions by 6.90 mug m-3 and 6.23 mug m-3 for the air masses from local and NC. PM2.5 source contributions related to emission showed an upsurge from 1.39 mug m-3 (biomass burning) to 24.9 mug m-3 (secondary inorganic aerosol) in 2019 except for industrial processes, while all reduced in 2020. From 2018 to 2020, the emission-related contribution of coal combustion to PM2.5 increased from 10.0% to 19.0% for air masses from the local area. To support the priority natural gas quotas in northern China, additional coal in cities of southern China was consumed, raising related emissions from transportation activities and road dust in urban regions, as well as additional biofuel consumption in suburban or rural regions. All these activities could explain the increased primary PM2.5 and related precursor NO2. This study gave substantial evidence of air pollution control measures impacting the downwind regions and promote the necessity of air pollution joint control across the administration.Copyright © 2023 Elsevier Ltd

Community outreach and engagement to increase colorectal cancer screening in ethnic minority communities

PURPOSE: Colorectal cancer (CRC) is preventable with screening, yet remains the second leading cause of cancer deaths in the U.S. Nationally, CRC screening substantially declined during the COVID-19 pandemic and is underutilized by ethnic minorities and in safety-net systems. Therefore, City of Hope partnered with Federally Qualified Health Centers (FQHCs) and community and faithbased organizations to improve CRC screening among medically underserved communities. METHOD(S): Between October 2020 and October 2022, we implemented a multi-component intervention that included community outreach and education (a multi-ethnic multimedia campaign and community training adapted from the NCI Screen2Save (S2S) program) and clinic-based interventions (provider/staff training and patient education). Intervention reach and training participant surveys were assessed. Within our four FQHC sites, we also compared clinic-level CRC screening rates among age-eligible patients before (June 2021) and after implementation of the clinic-based intervention (June 2022). RESULT(S): Our reach assessment showed that our multi-ethnic multimedia campaign reached 35.4 million impressions, our S2S education training reached 300 diverse community members, and our provider/staff training reached 150 medical providers. Among the 100 providers surveyed, >80% felt confident they could get their patients to complete their CRC screening test and follow-up care. For the clinic-based intervention component, our baseline sample included 11,259 age-eligible patients across the four FQHC sites. Overall CRC screening rates increased from 45% to 52% before vs. after the intervention implementation period. The site with the highest CRC screening rate (>62%) maintained steady rates over the observation period, whereas three sites with lower baseline rates showed greater pre-post improvements (average 15 percentage-point increase). CONCLUSION(S): An overall increase in CRC screening rates was achieved across FQHCs, despite clinic staffing challenges during the COVID-19 pandemic. Intervention implementation is ongoing with attempts to document individual, clinic improvements by race/ethnicity.

The Burden of Being a Bridge: Analysing Subjective Well-Being of Twitter Users During the COVID-19 Pandemic

The outbreak of the COVID-19 pandemic triggers infodemic over online social media, which significantly impacts public health around the world, both physically and psychologically. In this paper, we study the impact of the pandemic on the mental health of influential social media users, whose sharing behaviours significantly promote the diffusion of COVID-19 related information. Specifically, we focus on subjective well-being (SWB), and analyse whether SWB changes have a relationship with their bridging performance in information diffusion, which measures the speed and wideness gain of information transmission due to their sharing. We accurately capture users' bridging performance by proposing a new measurement. Benefiting from deep-learning natural language processing models, we quantify social media users' SWB from their textual posts. With the data collected from Twitter for almost two years, we reveal the greater mental suffering of influential users during the COVID-19 pandemic. Through comprehensive hierarchical multiple regression analysis, we are the first to discover the strong relationship between social users' SWB and their bridging performance. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Liver injury caused by antiviral agents for COVID-19.

Antiviral therapy is important for COVID-19. Currently, the anti-2019-nCoV drugs in clinical trials include broad-spectrum antiviral drugs (alpha interferon and ribavirin), hemagglutinin inhibitors (arbidol), human immunodeficiency virus protease inhibitors (lopinavir/ritonavir and darunavir/cobicistat), nucleoside analogues (favipiravir and remdesivir) and antimalarial drug (chloroquine);while liver damage may occur in some patients with the medication. This article reviews the research on liver damage associated with anti-2019-nCoV drugs, aiming at promoting the safe and effective antiviral therapy for COVID-19 patients.Copyright © 2020 by the Chinese Medical Association.

Liver injury caused by antiviral agents for COVID-19.

Antiviral therapy is important for COVID-19. Currently, the anti-2019-nCoV drugs in clinical trials include broad-spectrum antiviral drugs (alpha interferon and ribavirin), hemagglutinin inhibitors (arbidol), human immunodeficiency virus protease inhibitors (lopinavir/ritonavir and darunavir/cobicistat), nucleoside analogues (favipiravir and remdesivir) and antimalarial drug (chloroquine);while liver damage may occur in some patients with the medication. This article reviews the research on liver damage associated with anti-2019-nCoV drugs, aiming at promoting the safe and effective antiviral therapy for COVID-19 patients.Copyright © 2020 by the Chinese Medical Association.

Comparing Pre-trained Models Through Classifying Lung Conditions Using Chest X-ray

In classifying chest X-rays (CXR), machine learning, particularly transfer learning, has been widely implemented and has demonstrated an excellent range of accuracy. In this study, ResNet-50, ResNet-101, and VGG16, three popular transfer learning models, were compared in a CXR classification task. This study used a dataset containing CXRs of 3616 COVID-19-positive cases, CXRs of 1345 viral pneumonia cases, CXRs of 6012 lung opacity (non-Covid lung infection) cases, as well as CXRs of 10,192 normal individuals and corresponding lung masks. The study used Keras and TensorFlow to import the pre-trained models and compare them using the exact same training sets, testing sets, and validation sets. The ResNet-50 achieves a classification accuracy of 90%, the ResNet-101 achieves a classification accuracy of 91%, and the VGG16 achieves a classification accuracy of 89%. This research reached the conclusion that ResNet-101 performs better in such image multiclassification tasks. We speculate that this is because ResNet-101 was trained based on residual learning which is easy to optimize. © 2022 SPIE. All rights reserved.

Transcriptomic Profiling of Ganoderic Acid Me-Mediated Prevention of Sendai Virus Infection

Objectives: Ganoderic acid Me [GA-Me], a major bioactive triterpene extracted from Ganoderma lucidum, is often used to treat immune system diseases caused by viral infections. Although triterpenes have been widely employed in traditional medicine, the comprehensive mechanisms by which GA-Me acts against viral infections have not been reported. Sendai virus [SeV]-infected host cells have been widely employed as an RNA viral model to elucidate the mechanisms of viral infection. Method(s): In this study, SeV-and mock-infected [Control] cells were treated with or without 54.3 muM GA-Me. RNA-Seq was performed to identify differentially expressed mRNAs, followed by qRT-PCR validation for selected genes. GO and KEGG analyses were applied to investigate potential mechanisms and critical pathways associated with these genes. Result(s): GA-Me altered the levels of certain genes' mRNA, these genes revealed are associated pathways related to immune processes, including antigen processing and presentation in SeV-infected cells. Multiple signaling pathways, such as the mTOR pathway, chemokine signaling pathway, and the p53 pathways, significantly correlate with GA-Me activity against the SeV infection process. qRT-PCR results were consistent with the trend of RNA-Seq findings. Moreover, PPI network analysis identified 20 crucial target proteins, including MTOR, CDKN2A, MDM2, RPL4, RPS6, CREBBP, UBC, UBB, and NEDD8. GA-Me significantly changed transcriptome-wide mRNA profiles of RNA polymerase II/III, protein posttranslational and immune signaling pathways. Conclusion(s): These results should be further assessed to determine the innate immune response against SeV infection, which might help in elucidating the functions of these genes affected by GA-Me treatment in virus-infected cells, including cells infected with SARS-CoV-2. Copyright © 2022 Bentham Science Publishers.

Measuring COVID-19 Vaccine Hesitancy: Consistency of Social Media with Surveys

We validate whether social media data can be used to complement social surveys to monitor the public’s COVID-19 vaccine hesitancy. Taking advantage of recent artificial intelligence advances, we propose a framework to estimate individuals’ vaccine hesitancy from their social media posts. With 745,661 vaccine-related tweets originating from three Western European countries, we compare vaccine hesitancy levels measured with our framework against that collected from multiple consecutive waves of surveys. We successfully validate that Twitter, one popular social media platform, can be used as a data source to calculate consistent public acceptance of COVID-19 vaccines with surveys at both country and region levels. In addition, this consistency persists over time although it varies among socio-demographic sub-populations. Our findings establish the power of social media in complementing social surveys to capture the continuously changing vaccine hesitancy in a global health crisis similar to the COVID-19 pandemic. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Physical Therapist Impressions of Telehealth and Virtual Reality Needs Amidst a Pandemic

Most physical therapists would agree that physical rehabilitation is difficult to perform remotely. Consequently, the global COVID-19 pandemic has forced many physical therapists and their clients to adapt to telehealth, especially with video conferencing. In this article, we ask: How has telehealth for physical rehabilitation evolved with the global pandemic and what are the largest technological needs, treatment methodologies, and patient barriers? With the increased widespread use of telehealth for physical therapy, we present a qualitative study towards examining the shortcomings of current physical therapy mediums and how to steer future virtual reality technologies to promote remote patient evaluation and rehabilitation. We interviewed 130 physical rehabilitation professionals across the United States through video conferencing during the COVID19 pandemic from July—August 2020. Interviews lasted 30–45 min using a semi-structured template developed from an initial pilot of 20 interviews to examine potential barriers, facilitators, and technological needs. Our findings suggest that physical therapists utilizing existing telehealth solutions have lost their ability to feel their patients’ injuries, easily assess range of motion and strength, and freely move about to examine their movements when using telehealth. This makes it difficult to fully evaluate a patient and many feel that they are more of a “life coach” giving advice to a patient rather than a traditional in-person rehabilitation session. The most common solutions that emerged during the interviews include: immersive technologies which allow physical therapists and clients 1) to remotely walk around each other in 3D, 2) enable evidence-based measures, 3) automate documentation, and 4) provider clinical practice operation through the cloud. We conclude with a discussion on opportunities for immersive virtual reality towards telehealth for physical rehabilitation. Copyright © 2022 Elor, Conde, Powell, Robbins, Chen and Kurniawan.

An emergency department-based response to the hepatitis A outbreak in Michigan: improving hepatitis A vaccination rates in high-risk populations

Study Objectives: A non-food-borne hepatitis A outbreak occurred in Michigan between August 2016 and September 2019, resulting in 920 cases, 738 hospitalizations, and 30 deaths. To support the Michigan Department of Health and Human Services' efforts to increase hepatitis A vaccination rates among high-risk individuals, our multicenter health system implemented an electronic medical record (EMR)-based vaccination intervention across its nine emergency departments (ED). The primary objective of this retrospective cohort and survey analysis was to quantitatively determine whether this intervention was successful in increasing vaccination rates. The secondary objective was to qualitatively assess the attitudes towards, and barriers to use of, the computerized vaccine reminder system.

Discursive construction in multilingual crisis risk communication: An analysis of ‘A letter to foreign nationals’ messages in China’s COVID-19 fight

This article examines the discursive construction of a specific letter-style multilingual crisis message released by local governmental institutions during China’s battle against the COVID-19 pandemic. Based on a sociocognitive analysis of a collection of 33 English-language messages, the analysis revealed the structural features of the message and the discursive strategies in constructing and negotiating the identities of the message’s addresser and the addressee. It was found that the discursive relationship between the addresser and the addressee was established on an ingroup-outgroup distinction mediated by neutralising strategies to reduce authoritative imposition and image-enhancing strategies to promote a responsible government. The findings suggest that multilingual crisis communication is a multivocal, complex social practice shaped by genre, textual, media and contextual factors. These findings will provide insights into the crisis discourse as an emerging topic of interest and help inform multilingual communication strategies in and beyond the context of a public health emergency. © The Author(s) 2022.

Discharging Low-risk Pulmonary Embolism from An Emergency Room

Background and Objectives: Patients with PE are traditionally admitted on parenteral agents, despite increasing literature that sPESI negative patients can be safely discharged from the ED. Our quality improvement initiative is focused on outpatient treatment for ED-diagnosed pulmonary emboli (OTPE) and our objective is to assess LOS, readmissions and to describe findings of our follow-up phone calls. Methods: This is an actively enrolling prospective study from 7/2020 at a single site with >500 PE cases per year with a PE Response Team (PERT). All ED PE patients are screened for OTPE. Exclusion criteria include sPESI ≥ 1, ESC high or intermediate, bleeding ≤ 30 days, hemoglobin < 8, platelet < 50,000, pregnancy, prior VTE, concomitant COVID-19, recent major surgery and social factors. Patients identified are discussed with PERT and ED physicians. If agreed upon, patients are discharged on DOAC with follow-up within one week. Patients receive calls on days 3, 7, and 30 from the OTPE team to assess AE relating to the DOAC or PE. LOS metrics are reported as mean with standard deviations, and readmissions are reported as percentages. Results: Ninety-eight low-risk patients were identified, of which 50 were OTPE-eligible with mean age 44.5 ± 16.9 years of age and 58% female. When comparing OTPE to low-risk admissions, there are no differences in age (p=0.35) and sex (p=0.72). For OTPE, the follow-up calls on day 3, 7, and 30 revealed no patient reported recurrent VTE, major bleeding or death. There was a similar ED provider to disposition LOS (p=0.74). Low-risk admissions had a higher rate of readmission than OTPE (p=0.19). Conclusion: Our OTPE process does not increase ED provider to disposition LOS, readmissions, or adverse outcomes. Future work will examine financial implications of OTPE and barriers to adoption of the process. As this is actively enrolling quality improvement initiative, we will continue to track postimplementation to optimize our process.

Online student engagement and place attachment to campus in the new service marketplace: an exploratory study

Purpose The pandemic has accelerated the use of virtual learning spaces and led to rethinking post-pandemic course delivery. However, it remains unclear whether students' online engagement in e-servicescapes can influence attachment to a place, i.e. a physical servicescape. This study conducted an exploratory study to inform place attachment and actor engagement literature in an online service context. Design/methodology/approach Quantitative survey design was used and 98 usable responses were collected from undergraduate and postgraduate students at a major New Zealand university during the COVID-19 pandemic in 2020. The questionnaire consisted of 23 items relating to three dimensions of online student engagement and 19 items referring to six dimensions of campus attachment. Findings Results of the exploratory study indicate that classmate community in online lectures, referring to student-student interactions, can positively influence five of the dimensions of campus attachment, including place identity, place dependence, affective attachment, social bonding and place memory, even though students are physically not on campus. However, it cannot influence place expectation. Moreover, instructor community (student-instructor interaction) and learning engagement (student-content interaction) in online lectures have insignificant impact on campus attachment. Research limitations/implications This study emphasises the social dimension when interacting in e-servicescapes. Person-based interactions are more influential than content-based interactions for student engagement. Educational service providers should integrate the e-servicescape and the physical servicescape by encouraging more student-student interactions to contribute to service ecosystem well-being at the micro, meso and macro levels. Originality/value This study indicates that customer-to-customer interaction serves to integrate customer engagement across the digital and physical realms for process-based services like education.

Characterizing Chinese online public opinions towards the COVID-19 recovery policy

Purpose: The online users’ characteristic information can provide decision support for policy-designing and construction of public strategies. Hence, this paper aims to conduct online public opinion mining on the recovery policy stimulating the economies stroked by COVID-19 epidemic. Also, sentimental analysis is performed to uncover the posters’ emotion towards the target policy. Design/methodology/approach: This paper adopts bidirectional encoder representations from transformers (BERT) as classifier in classification tasks, including misinformation detection, subject analysis and sentimental analysis. Meanwhile, latent Dirichlet allocation method and sentiment formulations are implemented in topic modelling and sentiment analysis. Findings: The experimental results indicate that public opinion is mainly non-negative to the target policy. The positive emotions mainly focus on the benefits that the recovery policy might bring to stimulate economy. On the other hand, some negative opinions concerned about the shortcomings and inconvenience of the target policy. Originality/value: The authors figured out the key factors focused by the public opinion on the target recovery policy. Also, the authors indicated pros and cons of the recovery policy by analysing the emotion and the corresponding topics of the public opinion on social media. The findings of the paper can be generalized in other countries theoretically to help them design recovery policy against COVID-19. © 2022, Emerald Publishing Limited.

From #jobsearch to #mask: Improving COVID-19 cascade prediction with spillover effects

An information outbreak occurs on social media along with the COVID-19 pandemic and leads to infodemic. Predicting the popularity of online content, known as cascade prediction, allows for not only catching in advance hot information that deserves attention, but also identifying false information that will widely spread and require quick response to mitigate its impact. Among the various information diffusion patterns leveraged in previous works, the spillover effect of the information exposed to users on their decision to participate in diffusing certain information is still not studied. In this paper, we focus on the diffusion of information related to COVID-19 preventive measures. Through our collected Twitter dataset, we validated the existence of this spillover effect. Building on the finding, we proposed extensions to three cascade prediction methods based on Graph Neural Networks (GNNs). Experiments conducted on our dataset demonstrated that the use of the identified spillover effect significantly improves the state-of-the-art GNNs methods in predicting the popularity of not only preventive measure messages, but also other COVID-19 related messages. © 2021 Owner/Author.

Impact of age, race, and family history on COVID-19 related changes in breast cancer screening among the Boston Mammography Cohort Study

The COVID-19 pandemic has placed an unprecedented burden on the healthcare system, disrupting routine care including breast cancer screening. We used data from 2392 women without a history of breast cancer enrolled in the Boston Mammography Cohort Study (BMCS) to investigate whether subgroups defined by age, race, or family history of breast cancer: 1) experienced greater declines in screening or diagnostic imaging during the lockdown;or 2) had slower rebound during reopening. In this interrupted time series analysis, we used Poisson regression with robust standard errors to model expected monthly rates of breast cancer screening and diagnostic imaging from January 2019 through December 2020. We defined the pre-COVID-19 period as January 1, 2019, to February 29, 2020;the lockdown period as March 1 to May 30, 2020;and the reopening period as June 1 to December 31, 2020. We examined changes in trends overall and tested for the difference in trends by age (<50 vs ≤50), race (white vs non-white), and first-degree family history of breast cancer (yes or no). The mean monthly rate of breast cancer screening in the BMCS cohort was 45 per 1000 people during the pre-COVID-19 period, 7 per 1000 people during the lockdown period, and 50 per 1000 people during the reopening period. The mean monthly rate of breast cancer diagnostic imaging was 6 per 1000 people during the pre-COVID-19 period, 3 per 1000 people during the lockdown period, and 6 per 1000 people during the reopening period. During the pre-COVID-19 period, those who are age 50 or older had 5.3% higher monthly trend in breast cancer screening rates (p=0.005) and 9.8% higher monthly trend in diagnostic imaging rates (p=0.0389). During the lockdown period, those who were age 50 or older had a lower monthly trend in breast cancer screening rates compared to those who were younger than 50 (p<0.0001), while those who were white and those with family history have higher monthly trends of breast cancer screening rates compared to their respective counterparts (p<0.0001). During the reopening phase, those who are age 50 or older have 18.5% lower monthly trend in breast cancer screening rates in comparison to those who are younger than 50 (p=0.0008) and those who were white have 36.2% higher monthly trend in breast cancer diagnostic procedure rates in comparison to those who are non-white (p=0.018). Overall, we observed a significant decline in breast cancer screening rates with the advent of the COVID-19 pandemic. For the most part, screening and diagnostic imaging rates during the reopening phase equaled or exceeded those of the pre-COVID-19 period. However, the rate of return to screening was lower in women age 50 or older and the rebound in diagnostic imaging was lower in nonwhite women. Careful attention must be paid as the COVID-19 recovery continues to ensure equitable resumption of care. Future work will examine other factors including insurance status, breast cancer risk scores, and geographic location.

BDFNet: Boundary-Assisted and Discriminative Feature Extraction Network for COVID-19 Lung Infection Segmentation

The coronavirus disease (COVID-19) pandemic has affected billions of lives around the world since its first outbreak in 2019. The computed tomography (CT) is a valuable tool for the COVID-19 associated clinical diagnosis, and deep learning has been extensively used to improve the analysis of CT images. However, owing to the limitation of the publicly available COVID-19 imaging datasets and the randomness and variability of the infected areas, it is challenging for the current segmentation methods to achieve satisfactory performance. In this paper, we propose a novel boundary-assisted and discriminative feature extraction network (BDFNet), which can be used to improve the accuracy of segmentation. We adopt the triplet attention (TA) module to extract the discriminative image representation, and the adaptive feature fusion (AFF) module to fuse the texture information and shape information. In addition to the channel and spatial dimensions that are mainly used in previous models, the cross channel-special context is also obtained in our model via the TA module. Moreover, fused hierarchical boundary information is integrated through the application of the AFF module. According to experiments conducted on two publicly accessible COVID-19 datasets, COVID-19-CT-Seg and CC-CCII, BDFNet performs better than most cutting-edge segmentation algorithms in six widely used segmentation metrics. © 2021, Springer Nature Switzerland AG.