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Objective To understand the knowledge of COVID-19 and plague prevention and control in Qinghai Province, so as to carry out targeted health education and improve people's ability to prevent and control COVID–19, plague and other publichealth emergencies. Methods Six counties were randomly selected from three cities (states) by two-stage sampling. A self- designed questionnaire was randomly distributed to the public to investigate the awareness and behavior of COVID-19 and plague prevention and control. The Chinese version of Epidate was used for database construction and data entry. After checking and verifying, the data was exported as an Excel file and analyzed by SPSS 21.0 software. Results Accordign to the recovered questionnaires, the passing rate of knowledge of COVID-19 prevention and control was 78.46%, and the average score was (75. 82±16.43). The passing rate of plague prevention and control knowledge was 91.89%, and the average score was (86.46±15.94). The survey area, occupation category, gender and education level affected the knowledge of COVID-19 prevention and control. The average score was statistically significant (P<0.05). The survey area, occupation category, age and education level affected the knowledge of plague prevention and control, and the average score was statistically significant (P<0. 05). Conclusion People in Qinghai have poor knowledge of COVID - 19 prevention and control, but have good knowledge of plague prevention and control. Health education and health promotion activities on COVID - 19 and plague prevention and control should be increased in the future. © 2023, Editorial Department of Medical Pest Control. All rights reserved.
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The COVID-19 epidemic has caused serious and long-lasting health and social harm. Vaccination is considered as the most effective way to prevent the COVID-19 epidemic. Patients with mental disorders are at high risk of COVID-19 infection who are in urgent need to get protection. However, due to the particularity of their conditions, whether these patients should be vaccinated has become a tough issue that obsesses doctors, patients with mental disorders, and their families. In light of this issue, this article provides expert advice on the safety, legal and ethical issues of vaccination for patients with mental disorders to regulate the vaccination of these vulnerable populations against COVID-19.Copyright © 2022 Chinese Journal of Psychiatry. All rights reserved.
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The massive spread of COVID-19 and the crash of China Eastern Airlines MU5735 have negatively impacted the public's perception of civil aviation safety, which further affects the progress of the civil aviation industry and economic growth. The aim of research is to investigate the public's perception of China's civil aviation safety and give the authorities corresponding suggestions. First, we use online comment collection and sentiment analysis techniques to construct a novel evaluation index system reflecting the public's greatest concern for civil aviation safety. Then, we propose two novel large-scale group decision-making (LSGDM) models for aggregating evaluation: (1) K-means clustering with a novel distance measure for evaluators combined with unsupervised K-means clustering in two-stage, (2) unsupervised K-means clustering for evaluators combined with unsupervised K-means clustering for processing evaluation in two-stage. Finally, we compare the characteristics of different models and use the average of the two models as the final evaluation results. © 2023, The Author(s) under exclusive licence to Taiwan Fuzzy Systems Association.
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The COVID-19 epidemic has caused serious and long-lasting health and social harm. Vaccination is considered as the most effective way to prevent the COVID-19 epidemic. Patients with mental disorders are at high risk of COVID-19 infection who are in urgent need to get protection. However, due to the particularity of their conditions, whether these patients should be vaccinated has become a tough issue that obsesses doctors, patients with mental disorders, and their families. In light of this issue, this article provides expert advice on the safety, legal and ethical issues of vaccination for patients with mental disorders to regulate the vaccination of these vulnerable populations against COVID-19. © 2022 Chinese Journal of Psychiatry. All rights reserved.
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There is enormous attention surrounding COVID-19 these years. Artificial Intelligence are rising in the medical field. However, the next revolutionary steps lie in the upsurge of deep learning methodology. In this study, we propose a optimal Resnet50 based deep learning network for proper CT diagnosis of the COVID-19. The learning model was then adopted on the CT scan images with COVID-19 dataset (provided by Zhuhai HuiYu Medical Technology) and eventually achieves the autonomous diagnosis of new images by reading and analyzing more than 5000 CT pictures. The preliminary results of the core system show that the detecting sensitivity of the algorithm is 98.7, and the accuracy of the COVID-19 case detection is up to 97.4%. The CT detection for one COVID-19 case is expected to take 5.3 seconds, which greatly reduces the working intensity of doctors and the number of misdiagnoses significantly. In addition, the system can be extended to other more extensive medical image perception in the future. © 2022 IEEE.
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For the past two years, the Covid-19 pandemic has considerably paralysed public life in Europe. Planners and urban researchers have been speculating for months about the consequences of the pandemic for urban development and urban planning: for mobility in the city, for work and housing, for culture and the cultural industry, for the financial budgets of municipalities and the consequences for tourism and rural areas. Currently, the question of whether, after the pandemic, everything will remain as it was, or whether anything will change and, if so, what and where, is dominating the discussion in public and expert circles. The aim of this paper is to describe how cities in China reacted to the outbreak of Covid-19, what restrictions people in Chinese cities had to live with temporarily and how they reacted to these, what restrictions the local economy had to take on and why Chinese cities were apparently better prepared for the epidemic than European cities. From a European perspective, it is also interesting to assess what will change in China's cities after the pandemic and whether and which cities in Europe can learn from this.
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Ahead of Print article withdrawn by publisher.
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Rationale: Lesion segmentation is a critical step in medical image analysis, and methods to identify pathology without time-intensive manual labeling of data are of utmost importance during a pandemic and in resource-constrained healthcare settings. Here, we describe an unsupervised method of automatic lesion segmentation and quantification of COVID-19 lung tissue on chest Computed Tomography (CT) scans. Methods: Anonymized human COVID-19 (n=53), and non-pathologic control (n=87) inspiratory CT scans were used to train a publically available cycle-consistent generative adversarial network (CycleGAN), to convert the COVID-19 CT scans into generated "healthy" equivalents. Difference maps were created by subtracting the Hounsfield Units (HU) value for each voxel in the generated image from that of the original COVID-19 image. We then used these difference maps to construct 3D lesion segmentations to further quantitatively characterize COVID-19 lesions in an automated pipeline. Results: The CycleGAN produced lesion segmentations from COVID-19 CT scans of varying radiologic severity ranging from cases of patchy ground glass opacities to diffuse consolidative lesions. Images of COVID-19 patients showed higher HU intensity in original vs. generated images at sites of pulmonary lesions, while preserving normal parenchyma, fissures, vasculature, and airways (Figure 1, upper panels). The generated images showed larger lung gas volumes and lower tissue masses compared to their corresponding original COVID-19 images (p<0.001). Subtraction of the generated images from their corresponding original COVID-19 CT scans yielded difference maps showing the pathological tissue alone (Figure 1). Control, non-pathologic CT images were given as input to the CycleGAN, resulting in generated images nearly superimposable with the originals with no difference in gas volume or tissue mass (Figure 1, lower panel). Conclusions: To our knowledge, this is the first unsupervised COVID-19 lesion segmentation approach. Our automated lesion model performed well in mild and severe COVID-19 cases without the need for manually labelled lung segmentations as inputs. An automated lesion segmentation model can be used clinically to rapidly and objectively quantify pathologic pulmonary tissue to inform disease prognosis and treatment. Automated radiologic techniques, such as our model, circumvent the traditional bottle-neck of manually labeling data which has limited the scale and thus the impact of quantitative radiologic medical research.
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RATIONALE: Chest computed tomography (CT) has a potential role in the diagnosis, detection of complications, and prognosis of coronavirus disease 2019 (COVID-19). The value of chest CT can be further amplified when associated to physiological variables. Some studies have done efforts to correlate chest CT findings with overall oxygenation and respiratory mechanics, which although they are easily obtained may not be specifically related to COVID-19. Very few studies have tried to correlate chest CT findings with specific biomarkers related to COVID-19. For this purpose, temporal changes of chest CT were evaluated and then correlated with laboratory data in multicenter randomized clinical trial. METHODS: Adult patients who presented chest CT scan features compatible with viral pneumonia were admitted in the hospital and followed during 7 days (NCT: 04561219). CT scans and laboratory data [D-dimer, ferritin, and lactate dehydrogenase (LDH)] in blood were obtained at the moment of admission (Baseline) and on day 7 (Final). Qualitative and quantitative chest CT scan parameters were evaluated in ventral, middle and dorsal regions of interest (ROI) and classified as: hyper-, normal-, poor-, and non-aerated. RESULTS: In this study involving 45 COVID-19 patients no statistically significant differences in the overall Hounsfield Units (HU) ranges and percent of whole lung mass were found overtime. Normally aerated lung tissue reduced from Baseline to Final (p=0.004), mainly associated with a decrease in ventral (p=0.001) and middle (p=0.026) ROIs. At dorsal ROI, a reduction in CT lung mass in poorly aerated areas was observed from Baseline to Final. Poorly aerated and non-aerated lung areas were well correlated only with D-dimer blood levels (r=0.55, p<0.001;and r=0.52, p=0.001, respectively). CONCLUSION: In patients with COVID-19 pneumonia, changes in poor-and non-aerated were associated to changes in D-dimer blood levels, which may be a specific biomarker to be follow in facilities without CT as a way to infer radiologic changes.
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Introduction: There are a growing number of reports of persistently reduced exercise capacities, dyspnea or cough in a small fraction of Covid-19 survivors, suggesting ongoing impaired lung function long after the acute infection has resolved. The cause of these symptoms is unclear, though they likely originate in subtle damage to alveolar septa or vasculature. Here, we present the case of a patient with persistent post-COVID-19 symptoms who was evaluated with hyperpolarized xenon-129 MRI methods, which are sensitive to both ventilation and exchange in both non-specific tissue-plasma and red-blood-cell bound compartments in the lungs. Case: A 58-year-old never-smoker female patient was diagnosed COVID-19 positive in August 2020. She continued to experience nonspecific symptoms of fatigue, pins-and-needles in the feet, dyspnea, and daily productive cough (green, non-bloody sputum). Chest x-ray showed clear lungs without focal consolidation, pleural effusion, or pneumothorax. The subject underwent xenon-129 MR imaging on December 11, 2020 using a multi-breath scheme, in which sets of 6 ad libitum breaths containing 50mL of hyperpolarized xenon-129 (balance room air) were followed by four breaths of room air, and that 10-breath sequence was repeated until the polarized xenon-129 gas supply was exhausted. As shown in Figure 1, ventilated lung volumes are visually patchy, with heterogeneity corresponding to lobar structures or segmental and subsegmental volumes that are likely fed by airways with varying degrees of blockage. This is consistent with the persistent sputum production experienced by the patient. Further, saturation pulses at the frequency of hemoglobin-bound and tissue-plasma xenon-129 resonances selectively destroy signal in their respective compartments, which is subsequently exchanged to the gas phase. Compared to a healthy volunteer, the fractional depolarization achieved when applying identical saturation pulses is reduced by an average of approximately 40% in the patient. The response to saturation pulses also exhibits significant spatial heterogeneity. Discussion: Although a single case study is unable to determine the origin of alterations seen in a recovered COVID-19 patient, these changes are consistent with an overall reduction in the rate of gas exchange into dissolved compartments, as well as with a somewhat heterogeneous pattern of ventilation characteristic of mild obstructive disease. Further studies will be required to determine if these changes are associated with severe or persistent morbidity, and if correspondence to an age-matched healthy cohort increases as recovery continues.
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Data continually act as a substantial role in business and industry for its daily activities to smoothly functional. The data volume is growing with the passage of time and rising of information technology. Using data mining techniques for quality evaluation and business English teaching is essential in the modern world. These technologies are introduced in the classroom, especially in online classes during the COVID-19 pandemic. To analyze the quality of business English teaching, this paper uses multimedia and data mining technologies. Initially, the multimedia data are collected during classes, and the association rule recommendation algorithm using data mining is applied. Based on collaborative filtering algorithms in association rules, indicators for teaching quality evaluation in colleges and universities are set up. Next, the actual teaching data of a university is used. Taking business English as an example, the algorithm that has been built is tested. The application of the algorithm is tested, and the teaching process of College Business English is evaluated. Finally, the conclusion is drawn that data mining technology can describe the behavior of teaching well and evaluate it, and it has the potential of popularization. © 2021 Yanyan Xin.
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Since the end of 2019, several cases of inexplicable pneumonia have been discovered in some hospitals in Wuhan, which have been confirmed as acute respiratory infection caused by "COVID-19". COVID-19 is a respiratory infectious disease, which is mainly transmitted through the respiratory tract, such as droplet transmission, contact transmission and aerosol transmission. The spread of the virus needs to be in a relatively confined space, when the coronavirus reaches a certain concentration, healthy people inhale or the virus contacts the mucosa before it may cause infection. The scientific use of masks can effectively reduce the risk of new coronavirus infection and is an important means for the public to protect their health. Medical masks can not only prevent the patient from spraying droplets, reduce the amount and speed of droplets, but also block virus-containing droplets and prevent the wearer from inhaling. This paper proposes a real-time mask detection method, which classifies the detection of masks, hand shields, sunglasses, and glasses into the same classification model. The actual test effect of the model is more robust than the training of a single category. At the same time, the network structure by using convolutional neural network innovatively add the attention mechanism. The collected pictures contain 24,000 images, and the images are uniformly cropped with 64 64 pixels, and attained an accuracy rate of 97.2% during the training of this model. If a person who is not wearing a mask will be detected, so this study is beneficial in combating the spread of the virus and preventing contact with the virus. © 2021 ACM.
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The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the cause of a rapidly spreading illness, coronavirus disease 2019 (COVID-19), affecting more than seventeen million people around the world. Diagnosis and treatment guidelines for clinicians caring for patients are needed. In the early stage, we have issued "A rapid advice guideline for the diagnosis and treatment of 2019 novel coronavirus (2019-nCoV) infected pneumonia (standard version)";now there are many direct evidences emerged and may change some of previous recommendations and it is ripe for develop an evidence-based guideline. We formed a working group of clinical experts and methodologists. The steering group members proposed 29 questions that are relevant to the management of COVID-19 covering the following areas: chemoprophylaxis, diagnosis, treatments, and discharge management. We searched the literature for direct evidence on the management of COVID-19, and assessed its certainty generated recommendations using the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) approach. Recommendations were either strong or weak, or in the form of ungraded consensus-based statement. Finally, we issued 34 statements. Among them, 6 were strong recommendations for, 14 were weak recommendations for, 3 were weak recommendations against and 11 were ungraded consensus-based statement. They covered topics of chemoprophylaxis [including agents and Traditional Chinese Medicine (TCM) agents], diagnosis [including clinical manifestations, reverse transcription-polymerase chain reaction (RT-PCR), respiratory tract specimens, IgM and IgG antibody tests, chest computed tomography, chest X-ray, and CT features of asymptomatic infections], treatments [including lopinavir-ritonavir, umifenovir, favipiravir, interferon, remdesivir, combination of antiviral drugs, hydroxychloroquine/chloroquine, interleukin-6 inhibitors, interleukin-1 inhibitors, glucocorticoid, Qingfei Paidu decoction, Lianhua Qingwen granules/capsules, convalescent plasma, lung transplantation, invasive or noninvasive ventilation, and extracorporeal membrane oxygenation (ECMO)], and discharge management (including discharge criteria and management plan in patients whose RT-PCR retesting shows SARS-CoV-2 positive after discharge). We also created two figures of these recommendations for the implementation purpose. We hope these recommendations can help support healthcare workers caring for COVID-19 patients. © 2020 People's Military Medical Press. All rights reserved.
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'Recurrence' of coronavirus disease 2019 (COVID-19) has triggered numerous discussions of scholars at home and abroad. A total of 44 recurrent cases of COVID-19 and 32 control cases admitted from 11 February to 29 March 2020 to Guanggu Campus of Tongji Hospital affiliated to Tongji Medical College Huazhong University of Science and Technology were enrolled in this study. All the 44 recurrent cases were classified as mild to moderate when the patients were admitted for the second time. The gender and mean age in both cases (recurrent and control) were similar. At least one concomitant disease was observed in 52.27% recurrent cases and 34.38% control cases. The most prevalent comorbidity among them was hypertension. Fever and cough being the most prevalent clinical symptoms in both cases. On comparing both the cases, recurrent cases had markedly elevated concentrations of alanine aminotransferase (ALT) (P = 0.020) and aspartate aminotransferase (AST) (P = 0.007). Moreover, subgroup analysis showed mild to moderate abnormal concentrations of ALT and AST in recurrent cases. The elevated concentrations of ALT and AST may be recognised as predictive markers for the risk of 'recurrence' of COVID-19, which may provide insights into the prevention and control of COVID-19 in the future.
Subject(s)
Alanine Transaminase/blood , Aspartate Aminotransferases/blood , Coronavirus Infections/enzymology , Pneumonia, Viral/enzymology , COVID-19 , Case-Control Studies , Cough , Female , Fever , Humans , Male , Middle Aged , Pandemics , Recurrence , Retrospective Studies , Risk FactorsABSTRACT
The COVID-19 outbreak was declared a public health emergency of international concern by the World Health Organization on January 30, 2020. Since then, the virus has spread to affect more countries worldwide. During this period, our nuclear medicine department at Singapore General Hospital segregated our staff and patients by time, by space, or both, to minimize contact and prevent spread of the virus. Necessary changes to our clinical practices and stricter infection control measures were also enforced. We share our personal experience in managing a nuclear medicine department during this epidemic.