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Objective The objective of this study is to evaluate the acceptance of pregnant women with regards to coronavirus disease 2019 (COVID-19) vaccination during pregnancy and to identify any significant changes in their anxiety and knowledge on COVID-19 compared to our previous study. Methods This cross-sectional survey was performed in the antenatal clinics of United Christian Hospital and Tseung Kwan O Hospital of Hong Kong, China. Questionnaires were distributed to pregnant women for self-completion when attending follow-up from August to October 2021. Apart from basic demographic data, the questionnaire comprised of questions including knowledge on COVID-19 and its vaccines in pregnancy as well as attitudes and behaviors of pregnant women and their partners toward COVID-19. Continuous variables were analyzed by Student's test and Levene's test was used to confirm normal distribution and homogeneity of variance for continuous variables, whereas categorical variables were analyzed by the Chi-squared test or Fisher's exact test as appropriate. A P value of <0.05 was considered to be statistically significant. Results A total of 816 completed questionnaires were included for analysis. Pregnant women were less worried about COVID-19 in the current survey as compared to the last survey (393/816, 48.2% vs. 518/623, 83.1%, P?<?0.001). Fewer pregnant women believed that pregnancy were more susceptible to contract SARS-CoV-2 as compared to the last survey (265/816, 32.5% vs. 261/623, 41.9%, P?<?0.001). They have significant knowledge gap and concerns about COVID-19 vaccines. Nearly half of the participants believed that pregnant women cannot have COVID-19 vaccination (402/816, 49.3%) and it is unsafe to fetus (365/816, 44.7%). Around a third of women perceived that they were more prone to the side effects and complications of COVID-19 vaccines than the general population (312/816, 38.2%) and did not recognize that maternal COVID-19 vaccination could effect transferral of antibodies to the fetus to promote postnatal passive immunity (295/816, 36.2%). Most of them had not been vaccinated (715/816, 87.6%) and only (12/715) 1.7% of them would consider vaccination during pregnancy. Conclusion Despite the local and international recommendations for pregnant women to be vaccinated, the uptake of COVID-19 vaccines during pregnancy remained extremely low. Efforts should be made to effectively provide information about the safety and benefits of COVID-19 vaccines during pregnancy. There is an urgent need to booster vaccination rates in pregnant women to avoid excessive adverse pregnancy outcomes related to COVID-19.Copyright © the Author(s). Published by Wolters Kluwer Health, Inc.
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The outbreak of corona virus disease 2019 (COVID-19) has aroused great attention around the world. SARS-CoV-2 possesses characteristics of faster transmission, immune escape, and occult transmission by many mutation, which caused still grim situation of prevention and control. Early detection and isolation of patients are still the most effective measures at present. So, there is an urgent need for new rapid and highly sensitive testing tools to quickly identify infected patients as soon as possible. This review briefly introduces general characteristics of SARS-CoV-2, and provides recentl overview and analysis based on different detection methods for nucleic acids, antibodies, antigens as detection target. Novel nano-biosensors for SARS-CoV-2 detection are analyzed based on optics, electricity, magnetism, and visualization. In view of the advantages of nanotechnology in improving detection sensitivity, specificity and accuracy, the research progress of new nano-biosensors is introduced in detail, including SERS-based biosensors, electrochemical biosensors, magnetic nano-biosensors and colorimetric biosensors. Functions and challenges of nano-materials in construction of new nano-biosensors are discussed, which provides ideas for the development of various coronavirus biosensing technologies for nanomaterial researchers.
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Considering the millions of COVID-19 patients worldwide, a global critical challenge of low-cost and efficient anti-COVID-19 drug production has emerged. Favipiravir is one of the potential anti-COVID-19 drugs, but its original synthetic route with 7 harsh steps gives a low product yield (0.8%) and has a high cost ($68 per g). Herein, we demonstrated a low-cost and efficient synthesis route for favipiravir designed using improved retrosynthesis software, which involves only 3 steps under safe and near-ambient air conditions. A yield of 32% and cost of $1.54 per g were achieved by this synthetic route. We also used the same strategy to optimize the synthesis of sabizabulin. We anticipate that these synthetic routes will contribute to the prevention and treatment of COVID-19.
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As the COVID-19 pandemic continued unabatedly, many global supply chains involved in manufacturing and distributing personal protective equipment often failed to meet surge demand due to production capacity limits. Before the COVID-19 pandemic, the existing medical mask supply chain in Taiwan was decentralized, but immediately following the outbreak in 2020, the government of Taiwan established a centralized virtual company that integrated production, distribution, and sales. We use an exploratory empirical case study to gain insights into Taiwan's innovative public-private collaboration and the relationship between collaborative activities and supply chain resilience. This paper examines how a ten-fold growth, from 1.88 million to 20 million, in the daily production of medical masks, and their equitable distribution was achieved within four months of the onset of the COVID-19 pandemic. The results indicate that the public-private collaboration through a government-led centralized supply chain mitigated the impacts of unpredictable disruptions, built supply chain resilience, and ensured mask availability to the public. © 2023 Informa UK Limited, trading as Taylor & Francis Group.
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Conventional X-ray imaging architectures feature data redundancy and hardware consumption due to the separated sensory terminal and computing units. In-sensor computing architectures is promising to overcome such drawbacks. However, its realization in X-ray range remains elusive. We propose ion distribution induced reconfigurable mechanism, and demonstrate the first X-ray band in-sensor computing array based on Pb-free perovskite. Redistribution of Br- ion in perovskite induces the switching of PN and NP modes under electrical pooling. X-ray detection sensitivity can be switched between two stable self-power sensing modes with 4373±298 and -7804±429 mu mathrm{CGy}-{ mathrm{a} mathrm{i} mathrm{r}}{}{-1} mathrm{cm}{-2} respectively, which are superior than that of commercial a-Se detectors (20 mu mathrm{C} mathrm{G} mathrm{y}-{ mathrm{a} mathrm{i} mathrm{r}}{}{-1} mathrm{c} mathrm{m}{-2}). Both modes exhibit low detection limit of 48.4 mathrm{n} mathrm{G} mathrm{y}-{ mathrm{a} mathrm{i} mathrm{r}} mathrm{s}{-1}, which is two orders lower than typical medical dose rate of 5.5 mu mathrm{G} mathrm{y}-{ mathrm{a} mathrm{i} mathrm{r}} mathrm{s}{-1}. The perovskite array sensors can integrate with thin film transistors (TFTs) with low-temperature (80oC) process with good uniformity. An in-sensor computing algorithm of attention mechanism is performed on array sensors for chest X-ray images COVID-19 recognition, which enables an accuracy improvement up to 98.2%. Our results can pave the way for future intelligent X-ray imaging. © 2022 IEEE.
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The outbreak of corona virus disease 2019 (COVID-19) has aroused great attention around the world. SARS-CoV-2 possesses characteristics of faster transmission, immune escape, and occult transmission by many mutation, which caused still grim situation of prevention and control. Early detection and isolation of patients are still the most effective measures at present. So, there is an urgent need for new rapid and highly sensitive testing tools to quickly identify infected patients as soon as possible. This review briefly introduces general characteristics of SARS-CoV-2, and provides recentl overview and analysis based on different detection methods for nucleic acids, antibodies, antigens as detection target. Novel nano-biosensors for SARS-CoV-2 detection are analyzed based on optics, electricity, magnetism, and visualization. In view of the advantages of nanotechnology in improving detection sensitivity, specificity and accuracy, the research progress of new nano-biosensors is introduced in detail, including SERS-based biosensors, electrochemical biosensors, magnetic nano-biosensors and colorimetric biosensors. Functions and challenges of nano-materials in construction of new nano-biosensors are discussed, which provides ideas for the development of various coronavirus biosensing technologies for nanomaterial researchers.
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To investigate the effect of COVID-19 control measures on aerosol chemistry, the chemical compositions, mixing states, and formation mechanisms of carbonaceous particles in the urban atmosphere of Liaocheng in the North China Plain (NCP) were compared before and during the pandemic using a single particle aerosol mass spectrometry (SPAMS). The results showed that the concentrations of five air pollutants including PM2.5, PM10, SO2, NO2, and CO decreased by 41.2%-71.5% during the pandemic compared to those before the pandemic, whereas O3 increased by 1.3 times during the pandemic because of the depressed titration of O3 and more favorable meteorological conditions. The count and percentage contribution of carbonaceous particles in the total detected particles were lower during the pandemic than those before the pandemic. The carbonaceous particles were dominated by elemental and organic carbon (ECOC, 35.9%), followed by elemental carbon-aged (EC-aged, 19.6%) and organic carbon-fresh (OCfresh, 13.5%) before the pandemic, while EC-aged (25.3%), ECOC (17.9%), and secondary ions-rich (SEC, 17.8%) became the predominant species during the pandemic. The carbonaceous particle sizes during the pandemic showed a broader distribution than that before the pandemic, due to the condensation and coagulation of carbonaceous particles in the aging processes. The relative aerosol acidity (Rra) was smaller before the pandemic than that during the pandemic, indicating the more acidic particle aerosol during the pandemic closely related to the secondary species and relative humidity (RH). More than 95.0% and 86.0% of carbonaceous particles in the whole period were internally mixed with nitrate and sulfate, implying that most of the carbonaceous particles were associated with secondary oxidation during their formation processes. The diurnal variations of oxalate particles and correlation analyses suggested that oxalate particles before the pandemic were derived from aqueous oxidation driven by RH and liquid water content (LWC), while oxalate particles during the pandemic were originated from O3dominated photochemical oxidation.(c) 2022 Chinese Society of Particuology and Institute of Process Engineering, Chinese Academy of Sciences. Published by Elsevier B.V. All rights reserved.
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Since the outbreak of the COVID-19 pandemic, fires occurred frequently in hospitals managing COVID-19, and caused over 279 deaths. Fire safety in hospitals should be identified clearly and taken seriously. Fire probability and fire service coverage for hospitals from a national perspective in China were analyzed in this study. Calculated with the generalized Barrois model, the annual fire frequency of hospital building exceeds 0.5, when its floor area reaches approximately 180,000 m2. Based on the number of hospital fires in Changsha and that of hospitals in China from 2014 to 2017, the average annual fire probability of a hospital in China was calculated to be 0.017. The total effective coverage rate (TECR) of fire service for hospitals in Changsha, China was esti-mated to be between 11.82 % and 25.74 %, based on real-time travel times extracted from the Baidu Map. The TECR of national fire service for hospitals was estimated to be between 14.18 % and 30.89 %, according to the ratio of the number of hospitals and the number of fire stations in China to that in Changsha. Currently, recruiting medical staff as fire volunteers can quickly improve fire safety in hospitals for a low cost.(c) 2022 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).
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PurposeThis study aims to examine the impact of information communication technology-enabled work during non-working hours (ICT-enabled WNWHs), as a source of stress, on employee behavioral outcomes -in-role job performance, organizational citizenship behaviors (OCBs) that benefit organizations and OCBs that benefit individuals, through emotional responses - work exhaustion, nonwork exhaustion and organization-based self-esteem. As the coronavirus disease 2019 (COVID-19) lockdowns demonstrated that employees frequently engage in ICT-enabled WNWHs, studying stress induced by ICT-enabled WNWHs is essential for understanding employee adaptation to the work-from-home trend that emerged from COVID-19 lockdowns.Design/methodology/approachA quantitative survey comprising 1,178 employees in China was conducted, and the data reliability and validity were confirmed. Partial least squares structural equation modeling analysis was employed to test the hypotheses.FindingsThe study results empirically proved that, although ICT-enabled WNWHs had significant effects on employee behavioral outcomes, the related emotional responses were the mediators of the stress transmission mechanism that directly affected employee behavioral outcomes. Notably, work exhaustion and organization-based self-esteem partially mediate the stress transmission mechanism, while nonwork exhaustion exerts a full mediating effect.Originality/valueThis study proposes the stress transmission mechanism of ICT-enabled WNWHs and delineates emotional responses regarding the work environment attributes of ICT-enabled WNWHs, an approach rarely seen in prior IS studies. To our best knowledge, this study is the first to identify and empirically demonstrate organization-based self-esteem as one among the emotional responses to ICT-enabled WNWHs. Furthermore, it expands understanding of the holistic impacts of ICT-enabled WNWHs, which is lacking in information systems (IS) literature.
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Health and medical researchers often give clinical and policy recommendations to inform health practice and public health policy. However, no current health information system supports the direct retrieval of health advice. This study fills the gap by developing and validating an NLP-based prediction model for identifying health advice in research publications. We annotated a corpus of 6,000 sentences extracted from structured s in PubMed publications as "strong advice", "weak advice", or "no advice", and developed a BERT-based model that can predict, with a macro-averaged F1-score of 0.93, whether a sentence gives strong advice, weak advice, or not. The prediction model generalized well to sentences in both unstructured s and discussion sections, where health advice normally appears. We also conducted a case study that applied this prediction model to retrieve specific health advice on COVID-19 treatments from LitCovid, a large COVID research literature portal, demonstrating the usefulness of retrieving health advice sentences as an advanced research literature navigation function for health researchers and the general public.
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Since the last century, animal viruses have posed great threats to the health of humans and the farming industry. Therefore, virus control is of great urgency, and regular, timely, and accurate detection is essential to it. Here, we designed a rapid on-site visual data-sharing detection method for the Newcastle disease virus with smartphone recognition-based immune microparticles. The detection method we developed includes three major modules: preparation of virus detection vectors, sample detection, and smartphone image analysis with data upload. First, the hydrogel microparticles containing active carboxyl were manufactured, which coated nucleocapsid protein of NDV. Then, HRP enzyme-labeled anti-NP nanobody was used to compete with the NDV antibody in the serum for color reaction. Then the rough detection results were visible to the human eyes according to the different shades of color of the hydrogel microparticles. Next, the smartphone application was used to analyze the image to determine the accurate detection results according to the gray value of the hydrogel microparticles. Meanwhile, the result was automatically uploaded to the homemade cloud system. The total detection time was less than 50 min, even without trained personnel, which is shorter than conventional detection methods. According to experimental results, this detection method has high sensitivity and accuracy. And especially, it uploads the detection information via a cloud platform to realize data sharing, which plays an early warning function. We anticipate that this rapid on-site visual data-sharing detection method can promote the development of virus selfchecking at home.
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Human foot traffic in urban environments provides essential information for city planners to manage the urban resources and urban residents to plan their activities. Compared to camera or mobile-based solutions, seismic sensors detect human footstep signals with fewer privacy concerns. However, seismic sensors often record signals generated from multiple sources, particularly in an urban outdoor environment. In this article, we monitor people's running activities during COVID-19 pandemic with a seismic sensor in a park in Singapore. We compare the spectra of natural and urban events in the recorded seismic data. For each 3 s seismic data, we define hierarchical screening criteria to identify footsteps based on the spectrum of the signal and its envelope. We derive the cadence of each runner by detecting the primary frequency of the footstep signals. The resulting algorithm achieves higher accuracy and higher temporal resolution for weak and overlapping signals compared to existing methods. Runner statistics based on four-month long seismic data show that urban running activities have clear daily and weekly cycles. Lockdown measures to mitigate COVID-19 pandemic promoted running activities, particularly over the weekends. Cadence statistics show that morning runners have higher cadence on average.
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The topological distance is to measure the structural difference between two graphs in a metric space. Graphs are ubiquitous, and topological measurements over graphs arise in diverse areas, including, e.g. COVID-19 structural analysis, DNA/RNA alignment, discovering the Isomers, checking the code plagiarism. Unfortunately, popular distance scores used in these applications, that scale over large graphs, are not metrics, and the computation usually becomes NP-hard. While, fuzzy measurement is an uncertain representation to apply for a polynomial-time solution for undirected multigraph isomorphism. But the graph isomorphism problem is to determine two finite graphs that are isomorphic, which is not known with a polynomial-time solution. This paper solves the undirected multigraph isomorphism problem with an algorithmic approach as NP=P and proposes a polynomial-time solution to check if two undirected multigraphs are isomorphic or not. Based on the solution, we define a new fuzzy measurement based on graph isomorphism for topological distance/structural similarity between two graphs. Thus, this paper proposed a fuzzy measure of the topological distance between two undirected multigraphs. If two graphs are isomorphic, the topological distance is 0;if not, we will calculate the Euclidean distance among eight extracted features and provide the fuzzy distance. The fuzzy measurement executes more efficiently and accurately than the current methods.
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We present KAAPA (Knowledge Aware Answers from PDF Analysis), an integrated solution for machine reading comprehension over both text and tables extracted from PDFs. KAAPA enables interactive question refinement using facets generated from an automatically induced Knowledge Graph. In addition, it provides a concise summary of the supporting evidence for the provided answers by aggregating information across multiple sources. KAAPA can be applied consistently to any collection of documents in English with zero domain adaptation effort. We showcase the use of KAAPA for QA on scientific literature using the COVID-19 Open Research Dataset.
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Liaocheng represents one of the most serious polluted cities in Northern China. To investigate the impact of residential heating activities on atmospheric particles, the chemical composition, size distribution, and evolution process of single particles collected during the wintertime of 2019 were investigated using a single-particle aerosol mass spectrometer (SPAMS). The results showed that the concentrations of four air pollutants including PM2.5, SO2, NO2, and CO during the heating period were 1.1-1.2 times higher than those before the heating period largely due to the increase of pollutant emissions from coal combustion, while O-3 concentration during the heating period decreased by 40.2%. The mass spectra and unscaled size distributions of single particles suggested that the particles had undergone a significant aging process during the whole observation period. The acidity of single particles was calculated by the relative acidity ratio (R-ra), which increased from 36.1 +/- 13.9 before the heating period to 64.8 +/- 43.9 during the heating period, implying that the single particles were more acidic and less aged during the heating period, mainly due to the enhanced formation of sulfate and nitrate and the decreased O-3 concentration during the heating period. Moreover, R-ra decreased from clean days to polluted days before and during the heating period, suggesting that the atmospheric particles in polluted days were less acidic and more aged. The percentage of elemental carbon (EC) particles increased by 13.6% and 11.5% from clean days to polluted days before and during the heating period, respectively, suggesting the significant contribution of EC particles to the polluted days. Source identification results showed that single particles before the heating period were mostly derived from secondary inorganic source (26.5%) and vehicle exhaust (21.4%), whereas those during the heating period were largely from coal combustion (24.0%) and secondary inorganic source (21.4%).
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Objective: To raise awareness about 2019 novel coronavirus pneumonia (NCP) and reduce missed diagnosis rate and misdiagnosis rate by comparing the clinical characteristics between RNA positive and negative patients clinically diagnosed with NCP. Methods: From January 2020 to February 2020, 54 patients who were newly diagnosed with NCP in Wuhan Fourth Hospital were included in this study. RT-PCR method was used to measure the level of 2019-nCov RNA in pharyngeal swab samples of these patients. The patients were divided into RNA positive and negative group, and the differences of clinical, laboratory, and radiological characteristics were compared. Results: There were 31 RNA of 2019-nCov positive cases, and 23 negative cases. Common clinical symptoms of two groups were fever (80.64% vs. 86.96%) , chills (61.29% vs. 52.17%) , cough (80.64% vs. 95.65%) , fatigue (61.30% vs. 56.52%) , chest distress (77.42% vs.73.91%) . Some other symptoms were headache, myalgia, dyspnea, diarrhea, nausea and vomiting. The laboratory and radiological characteristics of two groups mainly were lymphopenia, increased erythrocyte sedimentation rate, increased C-reactive protein, increased lactate dehydrogenase, decreased oxygenation index, normal white blood cell count and bilateral chest CT involvement. There was no statistically significant difference in other clinical characteristics except for dyspnea between two groups. Conclusions: RNA positive and negative NCP patients shared similar clinical symptoms, while RNA positive NCP patients tended to have dyspnea. Therefore, we should improve the understanding of NCP to prevent missed diagnosis and misdiagnosis; In addition, more rapid and accurate NCP diagnostic approaches should be further developed.