Awareness and practice of preventive measures among healthcare workers in medical institutions in Beijing during influenza season on the eve of COVID-19 epidemic: A cross-sectional survey.

BACKGROUND: Influenza is an acute respiratory infectious disease caused by the influenza virus, which poses a certain threat to humans due to its short incubation period, fast transmission and strong infectivity. OBJECTIVES: To evaluate the awareness and prevention behavior against influenza among healthcare workers on the eve of the coronavirus disease 2019 (COVID-19) epidemic in Beijing, China. MATERIAL AND METHODS: Using the cross-sectional research design based on the principle of convenience sampling, an online questionnaire survey on the knowledge of flu, vaccination, medical protection behavior, and flu medication was conducted between January and February 2020. Healthcare workers from different healthcare facilities and different job positions in Beijing participated in this survey. RESULTS: A total of 1910 healthcare workers from different medical institutions and jobs were included in the study. The mean age of the participants was 32.69 ±8.72 years (range: 18-64 years). There were significant differences in knowledge about clinical signs about flu and prevention approaches among different age groups, individuals with different work experience and job titles (χ2 = 8.903-32.839; p < 0.05). Personnel with different job positions and education levels differed only in the knowledge about clinical signs of flu and identification of high-risk populations. A multivariate logistic regression analysis revealed that age (odds ratio (OR) = 0.979, 95% confidence interval (95% CI): 0.966-0.992) and education level (OR = 0.736, 95% CI: 0.588-0.921) were risk factors for hand hygiene practices, whereas job position (OR = 1.757, 95% CI: 1.146-2.695) and awareness of high-risk populations (OR = 1.405, 95% CI: 1.096-1.800) were protective factors influencing hand hygiene practices (p < 0.05). The only factor influencing mask wearing was the education level (OR = 0.610, 95% CI: 0.450-0.828). CONCLUSION: The knowledge level and preventive behavior of healthcare workers before the outbreak of COVID-19 has been insufficient.

Towards a global One Health index: a potential assessment tool for One Health performance.

BACKGROUND: A One Health approach has been increasingly mainstreamed by the international community, as it provides for holistic thinking in recognizing the close links and inter-dependence of the health of humans, animals and the environment. However, the dearth of real-world evidence has hampered application of a One Health approach in shaping policies and practice. This study proposes the development of a potential evaluation tool for One Health performance, in order to contribute to the scientific measurement of One Health approach and the identification of gaps where One Health capacity building is most urgently needed. METHODS: We describe five steps towards a global One Health index (GOHI), including (i) framework formulation; (ii) indicator selection; (iii) database building; (iv) weight determination; and (v) GOHI scores calculation. A cell-like framework for GOHI is proposed, which comprises an external drivers index (EDI), an intrinsic drivers index (IDI) and a core drivers index (CDI). We construct the indicator scheme for GOHI based on this framework after multiple rounds of panel discussions with our expert advisory committee. A fuzzy analytical hierarchy process is adopted to determine the weights for each of the indicators. RESULTS: The weighted indicator scheme of GOHI comprises three first-level indicators, 13 second-level indicators, and 57 third-level indicators. According to the pilot analysis based on the data from more than 200 countries/territories the GOHI scores overall are far from ideal (the highest score of 65.0 out of a maximum score of 100), and we found considerable variations among different countries/territories (31.8-65.0). The results from the pilot analysis are consistent with the results from a literature review, which suggests that a GOHI as a potential tool for the assessment of One Health performance might be feasible. CONCLUSIONS: GOHI-subject to rigorous validation-would represent the world's first evaluation tool that constructs the conceptual framework from a holistic perspective of One Health. Future application of GOHI might promote a common understanding of a strong One Health approach and provide reference for promoting effective measures to strengthen One Health capacity building. With further adaptations under various scenarios, GOHI, along with its technical protocols and databases, will be updated regularly to address current technical limitations, and capture new knowledge.

Clinical efficacy analysis of mesenchymal stem cell therapy in patients with COVID-19: A systematic review.

BACKGROUND: Currently, ongoing trials of mesenchymal stem cells (MSC) therapies for coronavirus disease 2019 (COVID-19) have been reported. AIM: In this study, we investigated whether MSCs have therapeutic efficacy in novel COVID-19 patients. METHODS: Search terms included stem cell, MSC, umbilical cord blood, novel coronavirus, severe acute respiratory syndrome coronavirus-2 and COVID-19, applied to PubMed, the Cochrane Controlled Trials Register, EMBASE and Web of Science. RESULTS: A total of 13 eligible clinical trials met our inclusion criteria with a total of 548 patients. The analysis showed no significant decrease in C-reactive protein (CRP) levels after stem cell therapy (P = 0.11). A reduction of D-dimer levels was also not observed in patients after stem cell administration (P = 0.82). Furthermore, interleukin 6 (IL-6) demonstrated no decrease after stem cell therapy (P = 0.45). Finally, we investigated the overall survival (OS) rate after stem cell therapy in COVID-19 patients. There was a significant improvement in OS after stem cell therapy; the OS of enrolled patients who received stem cell therapy was 90.3%, whereas that of the control group was 79.8% (P = 0.02). CONCLUSION: Overall, our analysis suggests that while MSC therapy for COVID-19 patients does not significantly decrease inflammatory markers such as CRP, D-dimer and IL-6, OS is improved.

Consideration of COVID-19 beyond the human-centred approach of prevention and control: the ONE-HEALTH perspective.

Most of the new emerging and re-emerging zoonotic virus outbreaks in recent years stem from close interaction with dead or alive infected animals. Since late 2019, the coronavirus disease 2019 (COVID-19) has spread into 221 countries and territories resulting in close to 300 million known infections and 5.4 million deaths in addition to a huge impact on both public health and the world economy. This paper reviews the COVID-19 prevalence in animals, raise concerns about animal welfare and discusses the role of environment in the transmission of COVID-19. Attention is drawn to the One Health concept as it emphasizes the environment in connection with the risk of transmission and establishment of diseases shared between animals and humans. Considering the importance of One Health for an effective response to the dissemination of infections of pandemic character, some unsettled issues with respect to COVID-19 are highlighted.

Explainable COVID-19 Infections Identification and Delineation Using Calibrated Pseudo Labels (preprint)

The upheaval brought by the arrival of the COVID-19 pandemic has continued to bring fresh challenges over the past two years. During this COVID-19 pandemic, there has been a need for rapid identification of infected patients and specific delineation of infection areas in computed tomography (CT) images. Although deep supervised learning methods have been established quickly, the scarcity of both image-level and pixel-level labels as well as the lack of explainable transparency still hinder the applicability of AI. Can we identify infected patients and delineate the infections with extreme minimal supervision? Semi-supervised learning has demonstrated promising performance under limited labelled data and sufficient unlabelled data. Inspired by semi-supervised learning, we propose a model-agnostic calibrated pseudo-labelling strategy and apply it under a consistency regularization framework to generate explainable identification and delineation results. We demonstrate the effectiveness of our model with the combination of limited labelled data and sufficient unlabelled data or weakly-labelled data. Extensive experiments have shown that our model can efficiently utilize limited labelled data and provide explainable classification and segmentation results for decision-making in clinical routine. The code is available at https://github.com/ayanglab/XAI COVID-19.

Robust Weakly Supervised Learning for COVID-19 Recognition Using Multi-Center CT Images (preprint)

The world is currently experiencing an ongoing pandemic of an infectious disease named coronavirus disease 2019 (i.e., COVID-19), which is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Computed Tomography (CT) plays an important role in assessing the severity of the infection and can also be used to identify those symptomatic and asymptomatic COVID-19 carriers. With a surge of the cumulative number of COVID-19 patients, radiologists are increasingly stressed to examine the CT scans manually. Therefore, an automated 3D CT scan recognition tool is highly in demand since the manual analysis is time-consuming for radiologists and their fatigue can cause possible misjudgment. However, due to various technical specifications of CT scanners located in different hospitals, the appearance of CT images can be significantly different leading to the failure of many automated image recognition approaches. The multi-domain shift problem for the multi-center and multi-scanner studies is therefore nontrivial that is also crucial for a dependable recognition and critical for reproducible and objective diagnosis and prognosis. In this paper, we proposed a COVID-19 CT scan recognition model namely coronavirus information fusion and diagnosis network (CIFD-Net) that can efficiently handle the multi-domain shift problem via a new robust weakly supervised learning paradigm. Our model can resolve the problem of different appearance in CT scan images reliably and efficiently while attaining higher accuracy compared to other state-of-the-art methods.

Chinese Society of Anesthesiology expert consensus on anesthetic management of cardiac surgical patients with suspected or confirmed Coronavirus disease 2019

The outbreak of a new coronavirus (severe acute respiratory syndrome coronavirus 2 [SARS-CoV-2]) in China in December 2019 has brought serious challenges to disease prevention and public health. Patients with severe coronavirus disease 2019 (COVID-19) who undergo cardiovascular surgery necessitate extremely high demands from anesthesia personnel, and face high risks of mortality and morbidity. Based on the current understanding of COVID-19 and the clinical characteristics of cardiovascular surgical patients, the authors provide anesthesia management guidelines for cardiovascular surgery along with the prevention and control of COVID-19.

Clinical Efficacy of Corticosteroids in the Early Stages of Deterioration in COVID-19 Pneumonia.

BACKGROUND: The World Health Organization (WHO) strongly suggests using corticosteroids in patients with severe coronavirus disease 2019 (COVID-19). Similarly, a large randomized controlled clinical trial (RCT) in the UK found that dexamethasone effectively reduced the mortality rate in severe COVID-19 patients. However, the safety profile of corticosteroids has been a controversial area of study. CASE DESCRIPTION: A case of a COVID-19 patient is described and the clinical characteristics are observed as the mildly symptomatic patient progresses into a critically ill patient and during their dramatic improvement with corticosteroid therapy in the early stage of the deterioration process with COVID-19 pneumonia. CONCLUSION: The most suitable timing and dosage for the use of corticosteroids to maximize its effect during the worsening of COVID-19 pneumonia are discussed. One of the main pathophysiological hypotheses for severe COVID-19 patients is related to cytokine storm and virus load, which can be effectively treated with corticosteroid therapy.

Explainable AI For COVID-19 CT Classifiers: An Initial Comparison Study (preprint)

Artificial Intelligence (AI) has made leapfrogs in development across all the industrial sectors especially when deep learning has been introduced. Deep learning helps to learn the behaviour of an entity through methods of recognising and interpreting patterns. Despite its limitless potential, the mystery is how deep learning algorithms make a decision in the first place. Explainable AI (XAI) is the key to unlocking AI and the black-box for deep learning. XAI is an AI model that is programmed to explain its goals, logic, and decision making so that the end users can understand. The end users can be domain experts, regulatory agencies, managers and executive board members, data scientists, users that use AI, with or without awareness, or someone who is affected by the decisions of an AI model. Chest CT has emerged as a valuable tool for the clinical diagnostic and treatment management of the lung diseases associated with COVID-19. AI can support rapid evaluation of CT scans to differentiate COVID-19 findings from other lung diseases. However, how these AI tools or deep learning algorithms reach such a decision and which are the most influential features derived from these neural networks with typically deep layers are not clear. The aim of this study is to propose and develop XAI strategies for COVID-19 classification models with an investigation of comparison. The results demonstrate promising quantification and qualitative visualisations that can further enhance the clinician's understanding and decision making with more granular information from the results given by the learned XAI models.

Post-viral parenchymal lung disease of COVID-19 and viral pneumonitis: A systematic review and meta-analysis. (preprint)

BackgroundApproximately half of COVID-19 survivors present persisting breathlessness, which may include development of pulmonary fibrosis. Research QuestionWhat is the prevalence of long-term radiological and functional pulmonary sequelae of parenchymal lung disease following hospitalisation with COVID-19 and other viral pneumonia? Study design and methodsWe performed systematic review and random effects meta-analysis of studies in adults hospitalised with SARS-CoV-2, SARS-CoV, MERS-CoV, or Influenza pneumonia and followed within 12 months from discharge. Searches were run on MEDLINE and Embase, updated 29 July 2021. Primary outcomes were proportion of 1) radiologic sequelae at CT scans; 2) restrictive impairment; 3) impaired gas transfer. Heterogeneity was explored in meta-regression. ResultsNinety-five studies were included for qualitative synthesis, of which 70 were suitable for meta-analysis, including 60 studies of SARS-CoV-2 with a median follow up of 3 months. In SARS-CoV-2 the overall estimated proportion of inflammatory changes during follow up was 0.50 (95%CI 0.41 to 0.58, I2=94.6%), whilst fibrotic changes were estimated at 0.29 (95%CI 0.22 to 0.37, I2=94.1%). Inflammatory changes reduced compared with CTs performed during hospitalisation (-0.47; 95%CI -0.56 to -0.37), whereas no significant resolution was observed in fibrotic changes (-0.09; 95%CI -0.25 to 0.07). Impaired gas transfer was estimated at 0.38 (95%CI 0.32 to 0.44, I2=92.1%), which was greater than estimated restrictive impairment (0.17; 95%CI 0.13 to 0.23, I2=92.5%). High heterogeneity means that estimates should be interpreted with caution. Confidence in the estimates was deemed low due to the heterogeneity and because studies were largely observational without controls. InterpretationA substantial proportion of radiological and functional sequelae consistent with parenchymal lung disease are observed following COVID-19 and other viral pneumonitis. Estimates of prevalence are limited by differences in case mix and initial severity. This highlights the importance of extended radiological and functional follow-up post hospitalisation. PROSPERO registrationCRD42020183139 (April 2020)

A new method to calculate the instant case fatality rate of COVID-19 in Wuhan and Hubei of China (preprint)

The worst-hit area of coronavirus disease 2019 (COVID-19) in China was Wuhan City and its affiliated Hubei Province, where the outbreak has been well controlled. The case fatality rate (CFR) is the most direct indicator to evaluate the hazards of an infectious disease. However, most reported CFR on COVID-19 represent a large deviation from reality. We aimed to establish a more accurate way to estimate the CFR of COVID-19 in Wuhan and Hubei and compare it to the reality. The daily case notification data of COVID-19 from December 8, 2019, to May 1, 2020, in Wuhan and Hubei were collected from the bulletin of the Chinese authorities. The instant CFR of COVID-19 was calculated from the numbers of deaths and the number of cured cases, the two numbers occurred on the same estimated diagnosis dates. The instant CFR of COVID-19 was 1.3%-9.4% in Wuhan and 1.2%-7.4% in Hubei from January 1 to May 1, 2020. It has stabilized at 7.69% in Wuhan and 6.62% in Hubei since early April. The cure rate was between 90.1% and 98.8% and finally stabilized at 92.3% in Wuhan and stabilized at 93.5% in Hubei. The mortality rates were 34.5/100 000 in Wuhan and 7.61/100 000 in Hubei. In conclusion, this approach reveals a way to accurately calculate the CFR, which may provide a basis for the prevention and control of infectious diseases.

Estimating the instant case fatality rate of COVID-19 in China.

OBJECTIVE: The outbreak of coronavirus disease 2019 (COVID-19) in China has been basically controlled. However, the global epidemic of COVID-19 is worsening. We established a method to estimate the instant case fatality rate (CFR) and cure rate of COVID-19 in China. METHODS: A total of 82 735 confirmed cases released officially by the Chinese authorities from December 8, 2019 to April 18, 2020 were collected. The estimated diagnosis dates of deaths and cured cases were calculated based on the median cure time or median death time of individual cases. Following this, the instant CFR was calculated according to the number of deaths and cured cases on the same estimated diagnosis date. RESULTS: In China, the instant CFR of COVID-19 was 3.8-14.6% from January 1 to January 17; it then declined gradually and stabilized at 5.7% in April. The average CFR in China was 6.1±2.9%, while the CFR was 1.0±0.4% in China except Hubei Province. The cure rate of COVID-19 was 93.9±2.9% in China, and stabilized at 94.3%, while it was 99.0±0.4% in China except Hubei Province. CONCLUSIONS: The instant CFR of COVID-19 in China overall was much higher than that in China except Hubei Province. The CFR of COVID-19 in China was underestimated.

Clinical Features of COVID‐19 in a Young Man with Massive Cerebral Hemorrhage: Case Report (preprint)

COVID-19 is currently a pandemic in the world, can invade multiple systems, and has a high morbidity and mortality. So far, no cases of acute cerebrovascular disease have been reported. This article reports the clinical features of a COVID-19 patient whose first symptom was cerebral hemorrhage. More importantly, after the craniotomy, the patient had high fever and it was difficult to retreat. After cerebrospinal fluid testing, it was determined that an intracranial infection had occurred. After anti-infection and plasma infusion of the recovered person, the patient's symptoms gradually improved. This case suggests that COVID-19 may infringe on cerebral blood vessels and cause cerebral hemorrhage. Transfusion of plasma from rehabilitation patients is effective for critically ill patients.

Weakly Supervised Deep Learning for COVID-19 Infection Detection and Classification from CT Images (preprint)

An outbreak of a novel coronavirus disease (i.e., COVID-19) has been recorded in Wuhan, China since late December 2019, which subsequently became pandemic around the world. Although COVID-19 is an acutely treated disease, it can also be fatal with a risk of fatality of 4.03% in China and the highest of 13.04% in Algeria and 12.67% Italy (as of 8th April 2020). The onset of serious illness may result in death as a consequence of substantial alveolar damage and progressive respiratory failure. Although laboratory testing, e.g., using reverse transcription polymerase chain reaction (RT-PCR), is the golden standard for clinical diagnosis, the tests may produce false negatives. Moreover, under the pandemic situation, shortage of RT-PCR testing resources may also delay the following clinical decision and treatment. Under such circumstances, chest CT imaging has become a valuable tool for both diagnosis and prognosis of COVID-19 patients. In this study, we propose a weakly supervised deep learning strategy for detecting and classifying COVID-19 infection from CT images. The proposed method can minimise the requirements of manual labelling of CT images but still be able to obtain accurate infection detection and distinguish COVID-19 from non-COVID-19 cases. Based on the promising results obtained qualitatively and quantitatively, we can envisage a wide deployment of our developed technique in large-scale clinical studies.

Estimation of instant case fatality rate of COVID-19 in Wuhan and Hubei based on daily case notification data (preprint)

Background The outbreak of coronavirus disease 2019 (COVID-19) initially appeared and has most rapidly spread in Wuhan, China. The case fatality rate is the most direct indicator to assess the hazards of an infectious disease. We aimed to estimate the instant fatality rate and cure rate of COVID-19 in Wuhan City and its affiliated Hubei Province. Methods We collected the daily case notification data of COVID-19 from Dec 8, 2019 to Mar 10, 2020 in Wuhan City and Hubei Province officially announced by the Chinese authority. The numbers of daily confirmed/deaths/cured cases and the numbers of daily cumulative confirmed/deaths/cured cases were obtained. The death time and cure time of COVID-19 patients were calculated based on the dates of diagnosis, death and discharge of individual cases. Then the estimated diagnosis dates of deaths and cured cases were obtained on the basis of the median death or medium cure time, respectively. Finally, the instant fatality rate of COVID-19 was calculated according to the numbers of deaths and cured cases on the same estimated diagnosis dates. Results From Jan 1, 2020 to Feb 22, 2020 in Wuhan City, the instant case fatality rate of COVID-19 was 3.4%19.5% and the instant cured rate was 80.0%96.6%. The average fatality rate reached 11.4% while the average cure rate was 88.6%. During the same period in Hubei Province, the instant case fatality rate was 3.8%16.6% and the instant cured rate was 83.4%96.6%. The average fatality rate and the average cure rate were 9.2% and 91.8%, respectively. Conclusions The fatality rate and cure rate of COVID-19 in Wuhan City and Hubei Province were underestimated. Wuhan showed higher fatality rate and cure rate than the whole Hubei Province did.