COVID-19 Instance Segmentation Network Based on Semantic Information Generating Proposals

The CT images of lungs of COVID-19 patients have distinct pathological features, segmenting the lesion area accurately by the method of deep learning, which is of great significance for the diagnosis and treatment of COVID-19 patients. Instance segmentation has higher sensitivity and can output the Bounding Boxes of the lesion region, however, the traditional instance segmentation method is weak in the segmentation of small lesions, and there is still room for improvement in the segmentation accuracy. We propose a instance segmentation network which is called as Semantic R-CNN. Firstly, a semantic segmentation branch is added on the basis of Mask-RCNN, and utilizing the image processing tool Skimage in Python to label the connected domain for the result of semantic segmentation, extracting the rectangular boundaries of connected domain and using them as Proposals, which will replace the Regional Proposal Network in the instance segmentation. Secondly, the Atrous Spatial Pyramid Pooling is introduced into the Feature Pyramid Network, then improving the feature fusion method in FPN. Finally, the cascade method is introduced into the detection branch of the network to optimize the Proposals. Segmentation experiments were carried out on the pathological lesion segmentation data set of CC-CCII, the average accuracy of the semantic segmentation is 40.56mAP, and compared with the Mask-RCNN, it has improved by 9.98mAP. After fusing the results of semantic segmentation and instance segmentation, the Dice coefficient is 80.7%, the sensitivity is 85.8%, and compared with the Inf-Net, it has increased by 1.6% and 8.06% respectively. The proposed network has improved the segmentation accuracy and reduced the false-negatives. © 2022 ACM.

[Performance of screening of contacts of COVID-19 cases in same flight].

Objective: To understand the performance of 2019-nCoV nucleic acid detection in screening of contacts of COVID-19 cases in same flights and provide evidence for the effective screening of persons at high risk for the infection in domestic flights. Methods: The information of passengers who took same domestic flights with COVID-19 cases in China from April 1, 2020 to April 30, 2022 were retrospectively collected,and χ2 test was used to analyze positive nucleic acid detection rates in the passengers in different times before the onsets of the index cases, in different seat rows and in epidemic periods of different 2019-nCoV variants. Results: During the study period, a total of 433 index cases were identified among 23 548 passengers in 370 flights. Subsequently, 72 positive cases of 2019-nCoV nucleic acid were detected in the passengers, in whom 57 were accompanying persons of the index cases. Further analysis of the another 15 passengers who tested positive for the nucleic acid showed that 86.67% of them had onsets or positive detections within 3 days after the diagnosis of the index cases, and the boarding times were all within 4 days before the onsets of the index cases. The positive detection rate in the passengers who seated in first three rows before and after the index cases was 0.15% (95%CI: 0.08%-0.27%), significantly higher than in the passengers in other rows (0.04%, 95%CI: 0.02%-0.10%, P=0.007),and there was no significant difference in the positive detection rate among the passengers in each of the 3 rows before and after the index cases (P=0.577). No significant differences were found in the positive detection rate in the passengers, except the accompanying persons, among the epidemics caused by different 2019-nCoV variants (P=0.565). During the Omicron epidemic period, all the positive detections in the passengers, except the accompanying persons, were within 3 days before the onset of the index cases. Conclusions: The screening test of 2019-nCoV nucleic acid can be conducted in the passengers took the same flights within 4 days before the onsets of the index cases on board. Passengers who seated within 3 rows from the index cases can considered as the close contacts at high risk for 2019-nCoV, for whom screening should be conducted first and special managements are needed. The passengers in other rows can be classified as general risk persons for screening and management.

FOOD SYSTEMS TRANSFORMATION: CONCEPTS, MECHANISMS AND PRACTICES

The global food systems face significant interrelated and complex challenges, including climate change, extreme weather events, natural resource depletion, biodiversity loss, emerging plant and animal diseases, conflict and trade shocks. The number of global populations that lacked access to adequate food sharply increased during the COVID-19 pandemic. It is estimated that in 2021, about 702 million to 828 million people around the world suffer from hunger, with an increase of 150 million people alone due to the outbreak of the global COVID-19 pandemic[1]. Reduced incomes, food price inflations and continued supply chain disruptions will lead to even more severe and widespread increases in global food insecurity if urgent action is not taken, affecting vulnerable households in almost every country. © The Author(s) 2023. Published by Higher Education Press.

Maternal and Perinatal Outcomes of SARS-CoV-2 and Variants in Pregnancy

Pregnancy is a physiological state that predisposes women to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, a disease that can cause adverse maternal and perinatal outcomes. The severity of coronavirus disease 2019 (COVID-19) disease is known to vary by viral strain;however, evidence for the effects of this virus in pregnant women has yet to be fully elucidated. In this review, we describe maternal and perinatal outcomes, vaccination, and vertical transmission, among pregnant women infected with the different SARS-CoV-2 variants identified to date. We also summarize existing evidence for maternal and perinatal outcomes in pregnant women with specific information relating to SARS-CoV-2 variants. Our analysis showed that Omicron infection was associated with fewer severe maternal and perinatal adverse outcomes while the Delta variant was associated with worse pregnancy outcomes. Maternal deaths arising from COVID-19 were found to be rare (<1.0%), irrespective of whether the virus was a wild-Type strain or a variant. Severe maternal morbidity was more frequent for the Delta variant (10.3%), followed by the Alpha (4.7%), wild-Type (4.5%), and Omicron (2.9%) variants. The rates of stillbirth were 0.8%, 4.1%, 3.1%, and 2.3%, respectively, in pregnancies infected with the wild-Type strain, Alpha, Delta, and Omicron variants, respectively. Preterm birth and admission to neonatal intensive care units were more common for cases with the Delta infection (19.0% and 18.62%, respectively), while risks were similar for those infected with the wild-Type (14.7% and 11.2%, respectively), Alpha (14.9% and 13.1%), and Omicron variants (13.2% and 13.8%, respectively). As COVID-19 remains a global pandemic, and new SARS-CoV-2 variants continue to emerge, research relating to the specific impact of new variants on pregnant women needs to be expanded.Copyright © Wolters Kluwer Health, Inc. All rights reserved.

Identifying Empathic Opportunities During Care Planning Discussions with Chinese Patients with Advanced Cancer and Their Caregivers (Sch434)

Outcomes: 1. Describe unique barriers that Chinese North American patients with advanced cancer face in expressing emotions and discussing future planning. 2. Identify empathic opportunities (ie, topics associated with emotional expression) during care planning discussions with Chinese North American patients. Introduction: Recognizing emotions in intercultural contexts represents a core competency in palliative care. Yet, a paucity of literature describes the types, patterns, and contexts of patient-expressed emotions during high-stakes conversations with patients from linguistically marginalized communities. We sought to address this gap by analyzing the emotional content during care planning conversations with Chinese patients with advanced cancer and their caregivers. Method(s): We conducted a secondary analysis of 22 semistructured interviews of Chinese patients (n=20) with metastatic cancer and their caregivers (n=8) recruited at one American comprehensive cancer centre. Informed by the Empathic Communication Coding System and existing literature, we conducted template analysis to code the transcripts for patients' and caregivers' expressed emotions. We also thematically analyzed the patterns and contexts in which emotions arose. Result(s): Participants were middle-aged (55.6+/-13.5 years), born in China (89.3%), 60.7% female, 85.7% partnered/married, and 89.3% college educated. Most of the interviews were conducted with patients alone (72.7%). Happiness was the most prevalent emotion (62%) followed by gratitude (43%), fear (43%), sadness (38%), anger (14%), surprise (14%), and humour (5%). When a caregiver was present, the interviews trended toward lower frequency of emotional expression. Regarding intensity, only one instance (anger) was categorized as most severe. Regarding context, emotions were only expressed in discussions about the past or present. Specifically, participants expressed positive emotions when discussing clinician attributes, symptom relief, and immigration to North America. Participants expressed negative emotions when discussing burdensome symptoms, diagnostic journey, the COVID-19 pandemic, and experiences with linguistic or cultural discordance. Discussion(s): Emotional expression during high-stakes care planning conversations with Chinese patients and caregivers may be infrequent and grounded in social, topical, and temporal context. Future work is necessary to understand how clinicians could best respond to distressing emotions during naturally occurring palliative care conversations with Chinese patients and their caregivers.Copyright © 2023

Research progress in high value-added utilization technology of waste plastics

The low degradability of waste plastics will continue to pollute the environment, and the spread of the COVID-19 has exacerbated the use and accumulation of plastics, and thus the efficient treatment of waste plastic resources has become an urgent technical problem to be solved. By analyzing several mainstream waste plastics treatment technologies, it was clear that resourceful and high value-added utilization technology was the most competitive and environmentally friendly waste plastics treatment route in the market. The research progress of high value-added utilization technology of waste plastics at home and abroad in recent years were reviewed. The development and variation of conventional thermal cracking technology were discussed. Through this route, the highest yield of waste plastics into fuel products can reach 97%—98%. It was pointed out that the conversion of waste plastics into jet fuel, high value-added chemicals and functional materials for special applications through chemical, catalytic and biological technologies was the mainstream research direction and development trend in this field. Among them, the yield of conversion to high value-added monomer could reach more than 97%, so as to realize the upgrading of plastic waste from the primary treatment stage of "waste clearance” to "turning waste into use” and "turning waste into treasure”, and help China achieve the goal of "double carbon”。. © 2023 Chemical Industry Press. All rights reserved.

Computer-Aided Diagnosis of COVID-19 with Joint Instance Segmentation and Classification

To address the difficulty of small lesion area detection of COVID-19 patients in their lung CT images, the author has proposed an end-to-end network which using semantic segmentation to guide instance segmentation, and extending transfer learning to the classification of COVID-19 pneumonia, Common pneumonia and Normal. Firstly, in order to extract richer multi-scale features and increase the weight of low-level features, we have introduced the Atrous Spatial Pyramid Pooling(ASPP) into the Feature Pyramid Network(FPN), and proposed Multi-scale Reverse Attention Feature Pyramid Network, then having added a semantic segmentation branch to guide instance segmentation after the output of ASPP, finally, we have extracted the object category score by detector for auxiliary classification. Segmentation experiments were carried out on the dataset of CC-CCII and COVID-19 infection segmentation dataset, the mean average precision(mAP) is 39.57%, 35.36%, Compared with the COVID-CT-Mask-Net, it has improved by 5.52%, 2.33%, we also carried out classification experiments on the dataset that is from COVIDX-CT, the sensitivity and specificity of the model for detecting COVID-19 in test data are 95.88% and 98.95% respectively. Also, the sensitivity and specificity of the model for detecting Common pneumonia in test data are 98.62% and 99.25% respectively, the sensitivity and specificity of the model for detecting Normal in test data are 99.61% and 99.11% respectively, which are the best results based on this dataset and indicators, this shows that the proposed method can quickly and effectively help the clinician identify and diagnose COVID-19 patient through their lung CT images. © 2022 IEEE.

A Bibliometric Analysis of the Literature on Postgraduate Teaching

The sustainable development of human society and economy needs the support of senior talents. Postgraduate teaching is one of the crucial components of higher education, and the priority method to cultivate senior talents. The 7th United Nations STI Forum in 2022 will focus on open science and postgraduate teaching;the theme of the forum is "While comprehensively implementing the 2030 Agenda for Sustainable Development, strengthen science, technology, and innovation, and promote the world's recovery from the COVID-19 pandemic". Therefore, the analysis of the field of postgraduate teaching is of great theoretical and practical significance to the cultivation of postgraduate students, the research of researchers, and the management of postgraduate teaching by the education sector. This research has carried out a bibliometric analysis to better obtain the knowledge structure in the field of postgraduate teaching and research, and help other researchers obtain the characteristics of the field of postgraduate teaching and research. VOSviewer and CiteSpace are used to analyze 4816 scientific core collection articles related to postgraduate teaching. These publications are from the Web of Science database. The dates of the articles range from 1995-2022. This research intuitively introduces a systematic overview of postgraduate teaching literature research, covering a number of publications, major categories, the most significant nations, groups, publications, writers, significant literature, and academic trends. The goal of this article is to create a map of the postgraduate teaching knowledge structure, while also examining the research collaboration across organizations, authors, nations, and areas. For scholars and practitioners in the field of graduate education, objective advice and helpful ideas are given through the analysis and discussion of the data acquired.

[Progress in the practice of surveillance and early warning of infectious diseases in major countries and regions].

The COVID-19 pandemic is yet another reminder that the threat of infectious disease has never really gone away. As the cornerstone of preventing and controlling infectious diseases, effective surveillance and early warning are of great significance in understanding the outbreak and epidemic of specific infectious diseases and putting forward effective prevention and control measures. Therefore, we must continue strengthening the construction of infectious disease surveillance and early warning system. We reviewed the surveillance and early warning practices of infectious diseases in major countries and regions, then discussed the development direction in the field of surveillance and early warning of infectious diseases to provide the reference for strengthening the construction and capacity of infectious disease surveillance and early warning system in China.

Multiscale codec network based CT image segmentation for human lung disease derived of COVID-19

Objective: The corona virus disease 2019 (COVID-19), also known as severe acute respiratory syndrome coronavirus (SARS-CoV-2), has rapidly spread throughout the world as a result of the increased mobility of populations in a globalized world, wreaking havoc on people's daily lives, the global economy, and the global healthcare system. The novelty and dissemination speed of COVID-19 compelled researchers around the world to move quickly, using all resources and capabilities to analyse and characterize the novel coronavirus in terms of transmission routes and viral latency. Early and effective screening of COVID-19 patients and corresponding medical treatment, care and isolation to cut off the transmission route of the novel coronavirus are the key to prevent the spread of the epidemic. Due to the rapid infection of COVID-19, it is very important to screen COVID-19 threats based on precise segmenting lesions in lung CT images, which can be a low cost and quick response method nowadays. Rapid and accurate segmentation of coronavirus pneumonia CT images is of great significance for auxiliary diagnosis and patient monitoring. Currently, the main method for COVID-19 screening is the reverse transcription polymerase chain reaction like reverse transcription-polymerase chain reaction(RT-PCR) analysis. But, RT-PCR is time consuming to provide the diagnosis results, and the false negative rate is relatively high. Another effective method for COVID-19 screening is computed tomography (CT) technology. The CT scanning technology has high sensitivity and enhanced three-dimensional representation of infection visualization. Computed tomography (CT) has been used as an important method for the diagnosis and treatment of patients with COVID-19, the chest CT images of patients with COVID-19 mostly show multifocal, patchy, peripheral distribution, and ground glass opacity (GGO) which is mostly seen in the lower lobes of both lungs;a high degree of suspicion for novel coronavirus's infection can be obtained if more GGO than consolidation is found on CT images;therefore, detection of GGO in CT slices regions can provide clinicians with important information and help in the fight against COVID-19. The current analysis of COVID-19 pneumonia lesions has low segmentation accuracy and insufficient attention to false negatives. Method: Our accurate segmentation model based on small data set. In view of the complexity and variability of the targeted area of COVID-19 pneumonia, we improved Inf-Net and proposed a multi-scale encoding and decoding network (MED-Net) based on deep learning method. The computational cost may be caused by multi-scale encoding and decoding. The network extends the encoder-decoder structure in FC-Net, in which the decoder part is on the left column;The middle column is atrous spatial pyramid pooling (ASPP) structure;The right column is a multi-scale parallel decoder which is based on the improvement of parallel partial decoder. In this network structure, HarDNet68 is adopted as the backbone in terms of high resource utilization and fast computing speed, which can be as a simplified version of DenseNet, reduces DenseNet based hierarchical connections to get cascade loss deduction. HardNet68 is mainly composed of five harmonious dense blocks (HDB). Based on 5 different scales, We extract multiscale features from the first convolution layer and the 5 HDB sequential steps of HarDNet68 via a five atrous spatial pyramid pooling (ASPP). Meanwhile, as a new decoding component, a multiscale parallel partial decoder (MPPD) is based on the parallel decoder (PPD), which can aggregate the features between different levels in parallel. By decoding the branches of three different receptive fields, we have dealt with information loss issues in the encoder part and the difficulty of small lesions segmentation. Our deep supervision mechanism has melted the multi-scale decoder into the true positive and true negative samples analyses, for improving the sensitivity of the model. Result: Current COVID-19 CT Segmentation provides compl ted segmentation labels as a small data set. This research is improved based on Inf-Net, and the model structure is simple, the edge attention module(EA) is not introduced, and the reverse attention module(RA) is not quoted, only one MPPD is used to optimize the network stricture. The quantitative results show that MED-Net can effectively cope with the problems of fewer samples in the small dataset, the texture, size and position of the segmentation target vary greatly. On the data set with only 50 training images and 50 test images, the Dice coefficient is 73.8%, the sensitivity is 77.7%, and the specificity is 94.3%. Compared with the previous work, it has increased by 8.21%, 12.28% and 7.76% respectively. Among them, Dice coefficient and sensitivity have reached the most advanced level based on the same division mode of this data set. Simultaneously the qualitative results address that the segmentation result of the proposed model is closer to ground-truth in this experiment. We also conducted ablation experiments, that the use of MPPD has obvious effects to deal with small lesions area segmentation and improving segmentation accuracy. Conclusion: Our analysis shows that the proposed method can effectively improve the segmentation accuracy of the lesions in the CT images of the COVID-19 derived lungs disease. Our segmentation accuracy of MED-Net is qualified. The quantitative and qualitative results demonstrate that MED-Net is relatively effective in controlling edges and details, which can capture rich context information, and improve sensitivity. MED-Net can also effectively resolve the small lesions segmentation issue. For COVID-19 CT Segmentation data set, it has several of qualified evaluation indicators based on end-to-end learning. The potential of automatic segmentation of COVID-19 pneumonia is further facilitated. © 2022, Editorial Office of Journal of Image and Graphics. All right reserved.

Thermal conductivity and phonon scattering of AlGaN nanofilms by elastic theory and Boltzmann transport equation

Aluminum gallium nitride (AlGaN) plays an essential role in deep ultra-violet light emitting diodes and high electron mobility transistors etc. For example, 2 nm - 5 nm AlGaN nanofilms consist of the quantum wells in ultra-violet light emitting diodes, which have been attracting extensive attention since the rise of COVID 2019. Since most photons and heat are generated in these AlGaN nanofilms, the thermal properties of AlGaN nanofilms are strongly influenced by the heat dissipation of devices. In this paper, utilizing elastic theory and the Boltzmann transport equation, the phonon dispersion relations, density of states, specific heat capacities and thermal conductivities of 2 nm Al (delta) Ga1-delta N nanofilms with various delta are theoretically calculated at different temperatures. The thermal conductivity of nanofilm is significantly smaller than that of its bulk counterpart. In contrast with bulk AlGaN, due to the dominance of boundary scattering and alloy disorder scattering, the thermal conductivity of Al (delta) Ga1-delta N exhibits a similar dependence on Al concentration to bulk Al (delta) Ga1-delta N. Meanwhile, since the screening of Umklapp scattering, the saturation temperature of thermal conductivity is delayed from 50 to 100 K in bulks to about 300 K in nanofilms. The shrinkage of nanofilms' thermal conductivity is also slower than for bulks. We believe that our work will be helpful in controlling the self-heating effect of devices based on AlGaN nanofilms.

Who's Next: Rising Star Prediction via Diffusion of User Interest in Social Networks

Finding items with potential to increase sales is of great importance in online market. In this paper, we propose to study this novel and practical problem: rising star prediction. We call these potential items Rising Star, which implies their ability to rise from low-turnover items to bestsellers in the future. Rising stars can be used to help with unfair recommendation in e-commerce platform, balance supply and demand to benefit the retailers and allocate marketing resources rationally. Although the study of rising star can bring great benefits, it also poses challenges to us. The sales trend of rising star fluctuates sharply in the short-term and exhibits more contingency caused by some external events (e.g., COVID-19 caused increasing purchase of the face mask) than other items, which cannot be solved by existing sales prediction methods. To address above challenges, in this paper, we observe that the presence of rising stars is closely correlated with the early diffusion of user interest in social networks, which is validated in the case of Taocode (an intermediary that diffuses user interest in Taobao). Thus, we propose a novel framework, RiseNet, to incorporate the user interest diffusion process with the item dynamic features to effectively predict rising stars. Specifically, we adopt a coupled mechanism to capture the dynamic interplay between items and user interest, and a special designed GNN based framework to quantify user interest. Our experimental results on large-scale real-world datasets provided by Taobao demonstrate the effectiveness of our proposed framework. IEEE

Practical barriers to telehealth visits in hepatology

Background: Telemedicine use increased dramatically during the height of the COVID pandemic, revealing both the potential advantages of remote patient-provider visits for managing chronic diseases and the challenges of delivering medical care with current technologies. With the long-term goal of redesigning telehealth hardware and software to enhance their performance, this study aimed to collect data about the perceived strengths and weakness of current telehealth technologies from hepatologists and other healthcare providers. Methods: A cross-sectional 11-question survey of real-life telehealth experiences was developed by a team of physicians, researchers, and engineers and remains open (https://forms.gle/KcADxamNMVbUyTA79). It was completed anonymously on-line by a convenience sample of healthcare providers who were recruited via emails and social media (Twitter). Data were analyzed using descriptive statistics. Results: Of the 72 providers who completed the survey so far, including nine hepatologists, 49% had performed more than 20 remote visits. Providers almost unanimously (94%) reported poor patient and/or provider internet or Wi-Fi connectivity, which resulted in poor quality audio and visual connection. Providers reported that 76% of patients lacked a video-capable device. Many providers felt uncomfortable using current technology for conducting video visits including 78% of hepatologists and 43% of non-hepatologists. Logistic hurdles included patients being in a public place (50%) and lack of technical support to address connectivity problems (42%). Difficulty incorporating support services, such as interpreters and/or aides, was reported by 42% of respondents. Factors identified as likely to increase the effectiveness of remote video visits include the incorporation of additional staff who could orient patients to the devices (70%) and navigate them through the visit (81%), and greater familiarity of patients with information technology, i.e., patients being more tech savvy (84%). Conclusion: This study identifies practical telehealth barriers. The difficulty of incorporating interpreters and aides reveals an important equity limitation of current approaches, which disadvantage non-English speakers and patients with disabilities who require assistance. Lack of access to functional telehealth devices with reliable connectivity, as well as lack of support staff, are barriers that need to be addressed for further equitable and effective expansion and optimization of telemedicine technology.

Air flow experiments on a train carriage—Towards understanding the risk of airborne transmission

A series of experiments was undertaken on an intercity train carriage aimed at providing a “proof of concept” for three methods in improving our understanding of airflow behaviour and the accompanied dispersion of exhaled droplets. The methods used included the following: measuring CO2 concentrations as a proxy for exhaled breath, measuring the concentrations of different size fractions of aerosol particles released from a nebuliser, and visualising the flow patterns at cross-sections of the carriage by using a fog machine and lasers. Each experiment succeeded in providing practical insights into the risk of airborne transmission. For example, it was shown that the carriage is not well mixed over its length, however, it is likely to be well mixed along its height and width. A discussion of the suitability of the fresh air supply rates on UK train carriages is also provided, drawing on the CO2 concentrations measured during these experiments. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

[Influence of SARS-CoV-2 vaccination on the epidemiological and clinical characteristics of imported COVID-19 cases in Chengdu].

Objective: To analyze the epidemiological and clinical characteristics of imported COVID-19 cases after SARS-CoV-2 vaccination and to provide evidence for the prevention and control of COVID-19. Methods: The imported COVID-19 cases in Chengdu as of April 15, 2021 were divided into the vaccinated group and unvaccinated group according to the history of SARS-CoV-2 vaccination. The epidemiological and clinical data of the cases were collected retrospectively, and the differences in epidemiological and clinical characteristics of the two groups were compared. Laboratory tests consisted of nucleic acid test, clinical index test, serum antibody test and lymphocyte test. Software WPS2019 was used for data management and software R 4.0.3 was used for statistical analysis. Results: A total of 75 COVID-19 cases were included in the analysis, in which 20 had received SARS-CoV-2 vaccination and only 4 with clinical symptoms, 55 patients did not receive SARS-CoV-2 vaccination, and 16 had clinical symptoms. In vaccinated group, the first injection time of vaccination ranged from July to November 2020, and 10 cases received two doses of vaccine simultaneously and 10 cases received two doses of vaccine at intervals of 14-57 days. The intervals between the completion of vaccination and the onset ranged from 87 days to 224 days. The differences in classification and clinical type between the two groups were significant. Significant differences were observed in case classification and clinical type between vaccinated group and unvaccinated group (P<0.05). The vaccinated group had a relatively high proportion of asymptomatic infections (40.00%, 8/20), while mild infections were mainly observed in the unvaccinated group(76.36%,42/55). The differences in Ct values (ORF1ab gene and N gene) at the diagnosis were not significant between vaccinated group and unvaccinated group (P>0.05), similar results were also observed in lymphocyte subtypes, procalcitonin and C-reactive protein level comparisons. Serum amyloid A level was higher in unvaccinated group than in vaccinated group (P<0.05). However, the SARS-CoV-2 related serum antibody of IgM, IgG and total antibody levels were significantly higher in vaccinated group (P<0.05). Conclusions: Risk of infection still exists with SARS-CoV-2 after vaccination, which can facilitate the production of specific serum antibody of IgM and IgG when people are exposed to the virus. It has a certain protective effect on SARS-CoV-2 infected persons. Vaccination can reduce the clinical symptoms and mitigate disease severity.

Predictive and retrospective modelling of airborne infection risk using monitored carbon dioxide

The risk of long range, herein ‘airborne', infection needs to be better understood and is especially urgent during the COVID-19 pandemic. We present a method to determine the relative risk of airborne transmission that can be readily deployed with either modelled or monitored CO2 data and occupancy levels within an indoor space. For spaces regularly, or consistently, occupied by the same group of people, e.g. an open-plan office or a school classroom, we establish protocols to assess the absolute risk of airborne infection of this regular attendance at work or school. We present a methodology to easily calculate the expected number of secondary infections arising from a regular attendee becoming infectious and remaining pre/asymptomatic within these spaces. We demonstrate our model by calculating risks for both a modelled open-plan office and by using monitored data recorded within a small naturally ventilated office. In addition, by inferring ventilation rates from monitored CO2, we show that estimates of airborne infection can be accurately reconstructed, thereby offering scope for more informed retrospective modelling should outbreaks occur in spaces where CO2 is monitored. Well-ventilated spaces appear unlikely to contribute significantly to airborne infection. However, even moderate changes to the conditions within the office, or new variants of the disease, typically result in more troubling predictions.

[Distribution and infectious characteristics of re-positive cases infected with SARS-CoV-2].

Domestic and foreign literatures related to the persistence of SARS-CoV-2 and the re-positive cases infected with SARS-CoV-2 were reviewed, and the characteristics and infectivity of the re-positive cases were analyzed to provide scientific evidence for the improvement of case management and the development of measures to stop the spread of SARS-CoV-2. Existing studies have shown that re-positive rate of SARS-CoV-2 ranged from 2.4% to 19.8%, the median of interval between re-positive detection and discharge was 4-15 days. Following the second course of the disease, the anti-SARS-CoV-2 IgM, IgG and IgA positive rates of the cases were 11.11%-86.08%, 52.00%-100.00% and 61.54%-100.00% respectively, the total antibody and neutralizing antibody positive rates were 98.72% and 88.46%. The viral load of the re-positive cases was lower than that in the initial infection. At least 3 380 re-positive cases have been reported globally. SARS-CoV-2 strains were isolated from the samples of 3 re-positive cases (1 immunodeficiency case and 2 cases with abnormal pulmonary imaging). There were close contacts that were infected by an asymptomatic case taking immunosuppressive agents. In conclusion, the infectivity of re-positive cases infected with SARS-CoV-2 is generally very low. Rare re-positive cases infected with SARS-CoV-2 might cause further transmission. The management approach for the re-positive cases can be based on the assessment of the individual transmission risk according to the pathogen detection results.

Expert consensus on emergency medicine procedure optimization guided by routine prevention and control strategy for COVID-19

The outbreak of coronavirus disease 2019 (COVID-19) was declared a global public health emergency on 31 January 2020. Emergency medicine procedures in Emergency Department should be optimized to cope with the current COVID-19 pandemic by providing subspecialty services, reducing the spread of nosocomial infections, and promoting its capabilities to handle emerging diseases. Thus, the Chinese Society of Emergency Medicine and Wuhan Society of Emergency Medicine drafted this consensus together to address concerns of medical staffs who work in Emergency Department. Based on in-depth review of COVID-19 diagnosis and treatment plans, literatures, as well as management approval, this consensus proposes recommendations for improving the rationalization and efficiency of emergency processes, reducing the risk of nosocomial infections, preventing hospital viral transmission, and ensuring patient safety. © 2021 Asian Pacific Journal of Tropical Medicine Produced by Wolters KluwerMedknow. All rights reserved.