Target area distillation and section attention segmentation network for accurate 3D medical image segmentation.

3D medical image segmentation has an essential role in medical image analysis, while attention mechanism has improved the performance by a large margin. However, existing methods obtained the attention coefficient in a small receptive field, resulting in possible performance limitations. Radiologists usually scan all the slices first to have an overall idea of the target, and then analyze regions of interest in multiple 2D views in clinic practice. We simulate radiologists' recognition process and propose to exploit the 3D context information in a deeper manner for accurate 3D medical images segmentation. Due to the similarity of human body structure, medical images of different populations have highly similar shape and location information, so we use target region distillation to extract the common segmented region information. Particularly, we proposed two optimizations including Target Area Distillation and Section Attention. Target Area Distillation adds positions information to the original input to let the network has an initial attention of the target, while section attention performs attention extraction in three 2D sections thus with large range of receptive field. We compare our method against several popular networks in two public datasets including ImageCHD and COVID-19. Experimental results show that our proposed method improves the segmentation Dice score by 2-4% over the state-of-the-art methods. Our code has been released to the public (Anonymous link).

Spectrum and Style Transformation Framework for Omni-Domain COVID-19 Diagnosis

Coronavirus disease 2019 (COVID-19) is an ongoing global pandemic and profoundly affects almost all people around the world. Thus, many automatic diagnosis methods based on computed tomography (CT) images have been proposed to reduce the workload of radiologists. Most of the existing methods focus on the in-domain predictions, i.e., the training and testing have similar distributions, which is impractical in real-world situations, since the CT images can be collected from different devices and in different hospitals. To improve the diagnosis performance of COVID-19 for both in-domain and out-of-domain data, this paper proposes a spectrum and style transformation framework for omni-domain COVID-19 diagnosis. To achieve this, we first present a spectrum transform module, which helps to discover the discriminating features of each domain to recognize the in-domain data. Then, we formulate a cross-domain stylization module, which learns the cross-domain knowledge to enhance the model generalization capability to deal with out-of-domain data. Moreover, our framework is a plug-and-play module that can be easily integrated into existing deep models. We evaluate our framework on four COVID-19 datasets and show our method consistently improves the diagnosis performance of various methods on both in-domain and out-of-domain data.

COVID-19 and Inequalities.

This paper brings together evidence from various data sources and the most recent studies to describe what we know so far about the impacts of the COVID-19 crisis on inequalities across several key domains of life, including employment and ability to earn, family life and health. We show how these new fissures interact with existing inequalities along various key dimensions, including socio-economic status, education, age, gender, ethnicity and geography. We find that the deep underlying inequalities and policy challenges that we already had are crucial in understanding the complex impacts of the pandemic itself and our response to it, and that the crisis does in itself have the potential to exacerbate some of these pre-existing inequalities fairly directly. Moreover, it seems likely that the current crisis will leave legacies that will impact inequalities in the long term. These possibilities are not all disequalising, but many are.

A look back at the first wave of COVID-19 in China: A systematic review and meta-analysis of mortality and health care resource use among severe or critical patients.

BACKGROUND: To investigate the mortality and health care resource use among patients with severe or critical coronavirus disease of 2019 (COVID-19) in the first wave of pandemic in China. METHODS: We performed a systematic review and meta-analysis to investigate the mortality, discharge rate, length of hospital stay, and use of invasive ventilation in severe or critical COVID-19 cases in China. We searched electronic databases for studies from China with no restrictions on language or interventions patients received. We screened records, extracted data and assessed the quality of included studies in duplicate. We performed the meta-analysis using random-effect models through a Bayesian framework. Subgroup analyses were conducted to examine studies by disease severity, study location and patient enrolment start date. We also performed sensitivity analysis using various priors, and assessed between-study heterogeneity and publication bias for the primary outcomes. RESULTS: Out of 6,205 titles and abstracts screened, 500 were reviewed in full text. A total of 42 studies were included in the review, of which 95% were observational studies (n = 40). The pooled 28-day and 14-day mortalities among severe or critical patients were 20.48% (7,136 patients, 95% credible interval (CrI), 13.11 to 30.70) and 10.83% (95% CrI, 6.78 to 16.75), respectively. The mortality declined over time and was higher in patients with critical disease than severe cases (1,235 patients, 45.73%, 95% CrI, 22.79 to 73.52 vs. 3,969 patients, 14.90%, 95% CrI, 4.70 to 39.57) and patients in Hubei compared to those outside Hubei (6,719 patients, 26.62%, 95% CrI, 13.11 to 30.70 vs. 244 patients, 5.88%, 95% CrI 2.03 to 14.11). The length of hospital stay was estimated at 18.48 days (6,847 patients, 95% CrI, 17.59 to 21.21), the 28-day discharge rate was 50.48% (3,645 patients, 95% CrI, 26.47 to 79.53), and the use of invasive ventilation rate was 13.46% (4,108 patients, 95% CrI, 7.61 to 22.31). CONCLUSIONS: Our systematic review and meta-analysis found high mortality among severe and critical COVID-19 cases. Severe or critical COVID-19 cases consumed a large amount of hospital resources during the outbreak.

Diet related Weibo texts posted during the COVID-19 outbreak

Objective: To investigate the diet-related topics posted on social media during the COVID-19 outbreak.

Cytokine Signature Associated With Disease Severity in COVID-19.

Coronavirus disease 2019 (COVID-19) broke out and then became a global epidemic at the end of 2019. With the increasing number of deaths, early identification of disease severity and interpretation of pathogenesis are very important. Aiming to identify biomarkers for disease severity and progression of COVID-19, 75 COVID-19 patients, 34 healthy controls and 23 patients with pandemic influenza A(H1N1) were recruited in this study. Using liquid chip technology, 48 cytokines and chemokines were examined, among which 33 were significantly elevated in COVID-19 patients compared with healthy controls. HGF and IL-1ß were strongly associated with APACHE II score in the first week after disease onset. IP-10, HGF and IL-10 were correlated positively with virus titers. Cytokines were significantly correlated with creatinine, troponin I, international normalized ratio and procalcitonin within two weeks after disease onset. Univariate analyses were carried out, and 6 cytokines including G-CSF, HGF, IL-10, IL-18, M-CSF and SCGF-ß were found to be associated with the severity of COVID-19. 11 kinds of cytokines could predict the severity of COVID-19, among which IP-10 and M-CSF were excellent predictors for disease severity. In conclusion, the levels of cytokines in COVID-19 were significantly correlated with the severity of the disease in the early stage, and serum cytokines could be used as warning indicators of the severity and progression of COVID-19. Early stratification of disease and intervention to reduce hypercytokinaemia may improve the prognosis of COVID-19 patients.

Are Socially Responsible Firms Associated with Socially Responsible Citizens? A Study of Social Distancing During the Covid-19 Pandemic.

The literature on the interplay between geographic communities and organizations has largely ignored the role of individual residents. In adopting a meso-perspective, we examine a potentially vital relationship between corporate conduct and pro-social behavior demanding sacrifice from individuals. Drawing on Weber (Economy and society: an outline of interpretive sociology. University of California Press, Berkeley, 1978 (Translation of Wirtschaft und Gesellschaft, Grundriss der verstehenden Soziologie, 1922)), we theorize that organizations in a community legitimize personal social conduct in three ways-by serving as role models, imparting norms and values, and routinizing forms of interaction. We study the relationship between corporate social responsibility (CSR) behavior by local firms and the social distancing (SD) of citizens in US counties during the Covid-19 pandemic, a core ethical outcome. We argue and find that the residents of communities in which firms exhibit higher levels of CSR engaged in more SD during the Covid-19 pandemic. This was true when firms were (a) long-established, (b) isomorphic in their CSR, and (c) major employers and vendors. Moreover, CSR relating to the treatment of employees as well as positive and negative extremes in CSR bore especially strong relationships with SD. Implications are drawn for the study of business ethics, as modeled by CSR, as a force for ethical personal behavior and public health in communities.

Predictive Analysis of the Neutralization Activity in Convalescent Plasmas From COVID-19 Recovered Patients in Zhejiang Province, China, January-March, 2020.

Background: Convalescent plasma (CP) transfusion is considered to be the priority therapeutic option for COVID-19 inpatients when no specific drugs are available for emerging infections. An alternative, simple, and sensitive method is urgently needed for clinical use to detect neutralization activity of the CP to avoid the use of inconvenient micro-neutralization assay. Method: This study aims to explore optimal index in predicting the COVID-19 CP neutralization activity (neutralizing antibody titers, NAb titers) in an indirect ELISA format. Fifty-seven COVID-19-recovered patients plasma samples were subjected to anti-SARS-CoV-2 RBD, S1, and N protein IgG antibody by indirect ELISA. Results: ELISA-RBD exhibited high specificity (96.2%) and ELISA-N had high sensitivity (100%); while ELISA-S1 had low sensitivity (86.0%) and specificity (73.1%). Furthermore, ELISA-RBD IgG titers and pseudovirus-based NAb titers correlated significantly, with R2 of 0.2564 (P < 0.0001). Conclusion: ELISA-RBD could be a substitute for the neutralization assay in resource-limited situations to screen potential plasma donors for further plasma infusion therapy.

Thyroid Function Abnormalities in COVID-19 Patients.

Purpose: The novel coronavirus COVID-19, has caused a worldwide pandemic, impairing several human organs and systems. Whether COVID-19 affects human thyroid function remains unknown. Methods: Eighty-four hospitalized COVID-19 patients in the First Affiliated Hospital, Zhejiang University School of Medicine (Hangzhou, China) were retrospectively enrolled in this study, among which 22 cases had complete records of thyroid hormones. In addition, 91 other patients with pneumonia and 807 healthy subjects were included as controls. Results: We found that levels of total triiodothyronine (TT3) and thyroid stimulating hormone (TSH) were lower in COVID-19 patients than healthy group (p < 0.001). Besides, TSH level in COVID-19 patients was obviously lower than non-COVID-19 patients (p < 0.001). Within the group of COVID-19, 61.9% (52/84) patients presented with thyroid function abnormalities and the proportion of thyroid dysfunction was higher in severe cases than mild/moderate cases (74.6 vs. 23.8%, p < 0.001). Patients with thyroid dysfunction tended to have longer viral nucleic acid cleaning time (14.1 ± 9.4 vs. 10.6 ± 8.3 days, p = 0.088). To note, thyroid dysfunction was also associated with decreased lymphocytes (p < 0.001) and increased CRP (p = 0.002). The correlation between TT3 and TSH level seemed to be positive rather than negative in the early stage, and gradually turned to be negatively related over time. Conclusion: Thyroid function abnormalities are common in COVID-19 patients, especially in severe cases. This might be partially explained by nonthyroidal illness syndrome.

Evaluation of the safety and efficacy of using human menstrual blood-derived mesenchymal stromal cells in treating severe and critically ill COVID-19 patients: An exploratory clinical trial.

The coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was identified in December 2019 and has subsequently spread worldwide. Currently, there is no effective method to cure COVID-19. Mesenchymal stromal cells (MSCs) may be able to effectively treat COVID-19, especially for severe and critical patients. Menstrual blood-derived MSCs have recently received much attention due to their superior proliferation ability and their lack of ethical problems. Forty-four patients were enrolled from January to April 2020 in a multicenter, open-label, nonrandomized, parallel-controlled exploratory trial. Twenty-six patients received allogeneic, menstrual blood-derived MSC therapy, and concomitant medications (experimental group), and 18 patients received only concomitant medications (control group). The experimental group was treated with three infusions totaling 9 × 107 MSCs, one infusion every other day. Primary and secondary endpoints related to safety and efficacy were assessed at various time points during the 1-month period following MSC infusion. Safety was measured using the frequency of treatment-related adverse events (AEs). Patients in the MSC group showed significantly lower mortality (7.69% died in the experimental group vs 33.33% in the control group; P = .048). There was a significant improvement in dyspnea while undergoing MSC infusion on days 1, 3, and 5. Additionally, SpO2 was significantly improved following MSC infusion, and chest imaging results were improved in the experimental group in the first month after MSC infusion. The incidence of most AEs did not differ between the groups. MSC-based therapy may serve as a promising alternative method for treating severe and critical COVID-19.

The Artificial-Liver Blood-Purification System Can Effectively Improve Hypercytokinemia for COVID-19.

Since the December 2019 outbreak of coronavirus disease 2019 (COVID-19) in Wuhan, the infection has spread locally and globally resulting in a pandemic. As the numbers of confirmed diagnoses and deaths continue to rise, COVID-19 has become the focus of international public health. COVID-19 is highly contagious, and there is no effective treatment yet. New treatment strategies are urgently needed to improve the treatment success rate of severe and critically ill patients. Increasing evidence has shown that a cytokine storm plays an important role in the progression of COVID-19. The artificial-liver blood-purification system (ALS) is expected to improve the outcome of the cytokine storm. In the present study, the levels of cytokines were detected in 12 COVID-19 patients pre- and post-ALS with promising results. The present study shows promising evidence that ALS can block the cytokine storm, rapidly remove the inflammatory mediators, and hopefully, suppress the progression of the disease, thereby providing a new strategy for the clinical treatment of COVID-19.

Comparative study of deep learning models for analyzing online restaurant reviews in the era of the COVID-19 pandemic.

Online reviews remain important during the COVID-19 pandemic as they help customers make safe dining decisions. To help restaurants better understand customers' needs and sustain their business under current circumstance, this study extracts restaurant features that are cared for by customers in current circumstance. This study also introduces deep learning methods to examine customers' opinions about restaurant features and to detect reviews with mismatched ratings. By analyzing 112,412 restaurant reviews posted during January-June 2020 on Yelp.com, four frequently mentioned restaurant features (e.g., service, food, place, and experience) along with their associated sentiment scores were identified. Findings also show that deep learning algorithms (i.e., Bidirectional LSTM and Simple Embedding + Average Pooling) outperform traditional machine learning algorithms in sentiment classification and review rating prediction. This study strengthens the extant literature by empirically analyzing restaurant reviews posted during the COVID-19 pandemic and discovering suitable deep learning algorithms for different text mining tasks.

[Reuse process of positive pressure powered air-filter protective hoods].

OBJECTIVE: To establish reuse process of positive pressure powered air-filter protective hoods during coronavirus disease 2019 (COVID-19) epidemic. METHODS: The procedure of pretreatment, storage, recovery, cleaning, disinfection and sterilization process of positive pressure powered air-filter protective hoods, which were used in the treatment of COVID-19 infection patients was established in Central Sterile Supply Department of the hospital. The cleaning and disinfection effects of the protective hoods after treatment were examined by magnifying glass method, residual protein detection method, real-time PCR, and agar pour plate method. RESULTS: Twenty five used protective hoods underwent totally 135 times of washing, disinfecting and sterilizing procedures. After washing, all the protein residue tests and COVID-19 nucleic acid tests showed negative results. After sterilizing, all the protective hoods met sterility requirement. All the tested protective hoods were undamaged after reprocessing. CONCLUSIONS: The established reuse procedures for used positive pressure powered air-filter protective hoods are safe.

Clinical Study of Mesenchymal Stem Cell Treatment for Acute Respiratory Distress Syndrome Induced by Epidemic Influenza A (H7N9) Infection: A Hint for COVID-19 Treatment.

H7N9 viruses quickly spread between mammalian hosts and carry the risk of human-to-human transmission, as shown by the 2013 outbreak. Acute respiratory distress syndrome (ARDS), lung failure, and acute pneumonia are major lung diseases in H7N9 patients. Transplantation of mesenchymal stem cells (MSCs) is a promising choice for treating virus-induced pneumonia, and was used to treat H7N9-induced ARDS in 2013. The transplant of MSCs into patients with H7N9-induced ARDS was conducted at a single center through an open-label clinical trial. Based on the principles of voluntariness and informed consent, 44 patients with H7N9-induced ARDS were included as a control group, while 17 patients with H7N9-induced ARDS acted as an experimental group with allogeneic menstrual-blood-derived MSCs. It was notable that MSC transplantation significantly lowered the mortality of the experimental group, compared with the control group (17.6% died in the experimental group while 54.5% died in the control group). Furthermore, MSC transplantation did not result in harmful effects in the bodies of four of the patients who were part of the five-year follow-up period. Collectively, these results suggest that MSCs significantly improve the survival rate of H7N9-induced ARDS and provide a theoretical basis for the treatment of H7N9-induced ARDS in both preclinical research and clinical studies. Because H7N9 and the coronavirus disease 2019 (COVID-19) share similar complications (e.g., ARDS and lung failure) and corresponding multi-organ dysfunction, MSC-based therapy could be a possible alternative for treating COVID-19.

Clinical study using mesenchymal stem cells for the treatment of patients with severe COVID-19.

The Coronavirus disease 2019 (COVID-19) caused by SARS-CoV-2 was identified in December 2019. The symptoms include fever, cough, dyspnea, early symptom of sputum, and acute respiratory distress syndrome (ARDS). Mesenchymal stem cell (MSC) therapy is the immediate treatment used for patients with severe cases of COVID-19. Herein, we describe two confirmed cases of COVID-19 in Wuhan to explore the role of MSC in the treatment of COVID-19. MSC transplantation increases the immune indicators (including CD4 and lymphocytes) and decreases the inflammation indicators (interleukin-6 and C-reactive protein). High-flow nasal cannula can be used as an initial support strategy for patients with ARDS. With MSC transplantation, the fraction of inspired O2 (FiO2) of the two patients gradually decreased while the oxygen saturation (SaO2) and partial pressure of oxygen (PO2) improved. Additionally, the patients' chest computed tomography showed that bilateral lung exudate lesions were adsorbed after MSC infusion. Results indicated that MSC transplantation provides clinical data on the treatment of COVID-19 and may serve as an alternative method for treating COVID-19, particularly in patients with ARDS.

The Challenges for Labour Market Policy during the COVID-19 Pandemic.

The COVID-19 pandemic is having a dramatic economic impact in most countries. In the UK, it has led to sharp falls in labour demand in many sectors of the economy and to initial acute labour shortages in other sectors. Much more than in a typical downturn, the current crisis is not simply a general slowdown in economic activity but also a radical short-term shift in the mix of economic activities - of which an unknown, but possibly significant, amount will be persistent. The initial policy response has focused on cushioning the blow to families' finances and allowing the majority of workers and firms to resume their original activities once the crisis subsides. These are crucial priorities. But there should also be a focus on reallocating some workers, either temporarily if working in shut-down sectors or permanently by facilitating transitions to sectors and jobs offering better prospects and facing labour shortages. The phasing-out of the furlough subsidies, which is projected to happen in Autumn 2020, brings this into even sharper focus since the alternative for many workers will be unemployment. Active labour market policy will need to be front and centre.

A Deep Learning System to Screen Novel Coronavirus Disease 2019 Pneumonia.

The real-time reverse transcription-polymerase chain reaction (RT-PCR) detection of viral RNA from sputum or nasopharyngeal swab had a relatively low positive rate in the early stage of coronavirus disease 2019 (COVID-19). Meanwhile, the manifestations of COVID-19 as seen through computed tomography (CT) imaging show individual characteristics that differ from those of other types of viral pneumonia such as influenza-A viral pneumonia (IAVP). This study aimed to establish an early screening model to distinguish COVID-19 from IAVP and healthy cases through pulmonary CT images using deep learning techniques. A total of 618 CT samples were collected: 219 samples from 110 patients with COVID-19 (mean age 50 years; 63 (57.3%) male patients); 224 samples from 224 patients with IAVP (mean age 61 years; 156 (69.6%) male patients); and 175 samples from 175 healthy cases (mean age 39 years; 97 (55.4%) male patients). All CT samples were contributed from three COVID-19-designated hospitals in Zhejiang Province, China. First, the candidate infection regions were segmented out from the pulmonary CT image set using a 3D deep learning model. These separated images were then categorized into the COVID-19, IAVP, and irrelevant to infection (ITI) groups, together with the corresponding confidence scores, using a location-attention classification model. Finally, the infection type and overall confidence score for each CT case were calculated using the Noisy-OR Bayesian function. The experimental result of the benchmark dataset showed that the overall accuracy rate was 86.7% in terms of all the CT cases taken together. The deep learning models established in this study were effective for the early screening of COVID-19 patients and were demonstrated to be a promising supplementary diagnostic method for frontline clinical doctors.

Contingency management strategies of the Nursing Department in centralized rescue of patients with coronavirus disease 2019.

OBJECTIVES: This article aims to summarize a series of contingency management strategies of the Nursing Department in the centralized treatment of patients with coronavirus disease 2019 (COVID-19). METHODS: The strategies of the Nursing Department included an early warning for prevention and control, taking functions of vertically commanding and horizontally coordinating, and reasonably allocating nursing workforce, to facilitate centralized treatment work in the in-hospital fever clinic, isolation wards and ICU, and referral and admission of critical patients. Five special groups were established in charge of training and examination, management and supervision, psychological support, logistical support, and reporting and publicity, respectively. RESULTS: It was achieved that no deaths from critical patients and no medical staff, no other patients were infected. CONCLUSION: Through the implementation of these strategies, safe and efficient centralized treatment was ensured timely, orderly and sustainably.

Clinical findings in a group of patients infected with the 2019 novel coronavirus (SARS-Cov-2) outside of Wuhan, China: retrospective case series.

OBJECTIVE: To study the clinical characteristics of patients in Zhejiang province, China, infected with the 2019 severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) responsible for coronavirus disease 2019 (covid-2019). DESIGN: Retrospective case series. SETTING: Seven hospitals in Zhejiang province, China. PARTICIPANTS: 62 patients admitted to hospital with laboratory confirmed SARS-Cov-2 infection. Data were collected from 10 January 2020 to 26 January 2020. MAIN OUTCOME MEASURES: Clinical data, collected using a standardised case report form, such as temperature, history of exposure, incubation period. If information was not clear, the working group in Hangzhou contacted the doctor responsible for treating the patient for clarification. RESULTS: Of the 62 patients studied (median age 41 years), only one was admitted to an intensive care unit, and no patients died during the study. According to research, none of the infected patients in Zhejiang province were ever exposed to the Huanan seafood market, the original source of the virus; all studied cases were infected by human to human transmission. The most common symptoms at onset of illness were fever in 48 (77%) patients, cough in 50 (81%), expectoration in 35 (56%), headache in 21 (34%), myalgia or fatigue in 32 (52%), diarrhoea in 3 (8%), and haemoptysis in 2 (3%). Only two patients (3%) developed shortness of breath on admission. The median time from exposure to onset of illness was 4 days (interquartile range 3-5 days), and from onset of symptoms to first hospital admission was 2 (1-4) days. CONCLUSION: As of early February 2020, compared with patients initially infected with SARS-Cov-2 in Wuhan, the symptoms of patients in Zhejiang province are relatively mild.