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Understanding particle settlement in channeled fluids has wide applications, such as fine particulate matter, coronavirus particle transport, and the migration of solid particles in water. Various factors have been investigated but few studies have acknowledged the channel's effect on settlement dynamics. This study developed a coupled interpolated bounce-back lattice Boltzmann-discrete element model and examined how a channel's width affects particle settlement. A factor k denoting the ratio of the channel's width and the particle diameter was defined. The terminal settling velocity for a single particle is inversely proportional to k, and the time that the particle takes to reach the terminal velocity is positively related to k. When k is greater than 15, the channel width's effects are negligible. For dual particles of the same size, the drafting-kissing-tumbling (DKT) process occurs infinitely in a periodic pattern, with the two particles swapping positions and settling around the channel's centerline. The smaller the k, the sooner the DKT process occurs. The particles collide with the channel wall when k <= 10. For dual particles of different sizes, the DKT process occurs once so that the bigger particle leads the settlement. Both particles settle along the channel's centerline in a steady state. The bigger the k, the bigger the difference in their terminal settling velocities until k = 15. The small particle collides with the channel wall if released under the big particle when k = 6. The findings of this study are expected to inform channeling or pipeline design in relevant engineering practices.
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Background & Aim: The long-term effects of human mesenchymal stem cell (MSC) treatment on COVID-19 patients have not been fully characterized. The aim of this study was to evaluate the safety and efficacy of a MSC treatment administered to severe COVID-19 patients enrolled in a randomized, double-blind, placebo-controlled clinical trial (NCT 04288102). Methods, Results & Conclusion(s): A total of 100 patients experiencing severe COVID-19 received either MSC treatment (n = 65, 4x107 cells per infusion) or a placebo (n = 35) combined with standard of care on days 0, 3, and 6. Patients were subsequently evaluated 18 and 24 months after treatment to evaluate the long-term safety and efficacy of the MSC treatment. The outcomes measured included: 6-minute walking distance (6-MWD), lung imaging, quality of life according to the Short Form 36 questionnaire, COVID-19-related symptoms, titers of SARS-CoV-2 neutralizing antibodies, MSC-related adverse events (AEs), and tumor markers. Two years after treatment, a marginally smaller proportion of patients had a 6-MWD below the lower limit of the normal range in the MSC group than in the placebo group (OR = 0.19, 95% CI: 0.04-0.80, Fisher's exact test, p = 0.015). On the SF-36 questionnaire, a marginally higher general health score was received by the MSC group at month 18 compared with the placebo group (50.00 vs. 35.00;95% CI: 0.00-20.00, Wilcoxon rank sum test, p = 0.016). In contrast, there were no differences in the total severity score of lung imaging or the titer of neutralizing antibodies between the two groups. Meanwhile, there were no MSC-related AEs reported at the 18- or 24-month follow-ups. The serum levels of most of the tumor markers examined remained within normal ranges and were similar between the MSC and placebo groups. Long-term safety was observed for the COVID-19 patients who received MSC treatment. Yet few sustained efficacy of MSC treatment was observed at the end of the 2-year follow-up period. Funding(s): The National Key Research and Development Program of China (2022YFA1105604, 2020YFC0860900), the specific research fund of The Innovation Platform for Academicians of Hainan Province (YSPTZX202216) and the Fund of National Clinical Center for Infectious Diseases, PLA General Hospital (NCRCID202105,413FZT6). [Figure presented]Copyright © 2023 International Society for Cell & Gene Therapy
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During the COVID-19 epidemic, blood samples are usually processed at 56 to attenuate the virus before pathogen detection. 71 blood samples of malaria patients reported by Shanghai Center for Disease Control and Prevention in 2017-2019 were collected, including 38 with Plasmodium falciparum infection, 8 P. malariae, 11 P. ovale and 14 P. vivax. The effect of inactivation on the thermal stability of P. falciparum histidine rich protein II (PfHRPII) and Plasmodium lactate dehydrogenase (pLDH) in blood samples was assessed before and after incubation at 56 for 30 min using the rapid diagnostic test (RDT) kit. The results showed that among the 38 P. falciparum T1-positive (PfHRPII) blood samples before heat treatment, 35 samples remained to be T1-positive (92.11%, 35/38, chi2=3.123, P>0.05) after heat treatment;while 54 blood samples (26 P. falciparum, 6 P. vivax, 10 P. ovale and 12 P. vivax) that were T2-positive (pLDH) before heat treatment turned to be T2-negative (positive rate 0, 0/54, chi2=87.755, P<0.01) after heat treatment. It was demonstrated that PfHRPII is stable during incubation at 56 for 30 min, while pLDH is unstable and degraded or inactivated during the heating. Therefore, the detection results of P. falciparum will not be affected by RDT, but diagnosis of the parasites other than P. falciparum in blood samples may be missed.Copyright © 2021, National Institute of Parasitic Diseases. All rights reserved.
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[] Atherosclerosis (As) is the pathological basis of coronary heart disease, and vascular endothelial injury is the initiating factor of coronary atherosclerosis. Vascular endothelial cells are a single layer of cells located in the inner layer of blood vessels and regulates exchanges between the blood stream and the surrounding tissues, and their integrity is very important. Many active monomers and the derivatives in natural products of traditional Chinese medicine modulate the function of endothelial cells by intervening oxidative stress, regulating the release of vasoactive substances, reducing inflammation, and equilibrating coagulation and anticoagulant system. They have the advantages of multi-pathway, multi-link and multi-target regulation in protecting from endothelial injury and attenuating atherogenesis. They have also been used to protect against corona virus disease 2019 (COVID-19) induced endothelial injury and atheroslerosis. This article reviews the research progress of the above issues in this field. © 2023, Editorial Office of Chinese Journal of Arteriosclerosis. All rights reserved.
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Human mobility nowcasting is a fundamental research problem for intelligent transportation planning, disaster responses and management, etc. In particular, human mobility under big disasters such as hurricanes and pandemics deviates from its daily routine to a large extent, which makes the task more challenging. Existing works mainly focus on traffic or crowd flow prediction in normal situations. To tackle this problem, in this study, disaster-related Twitter data is incorporated as a covariate to understand the public awareness and attention about the disaster events and thus perceive their impacts on the human mobility. Accordingly, we propose a Meta-knowledge-Memorizable Spatio-Temporal Network (MemeSTN), which leverages memory network and meta-learning to fuse social media and human mobility data. Extensive experiments over three real-world disasters including Japan 2019 typhoon season, Japan 2020 COVID-19 pandemic, and US 2019 hurricane season were conducted to illustrate the effectiveness of our proposed solution. Compared to the state-of-the-art spatio-temporal deep models and multivariate-time-series deep models, our model can achieve superior performance for nowcasting human mobility in disaster situations at both country level and state level. © 2023 ACM.
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Objective To explore the causes and feelings of delayed experience of seeking medical treatment in patients with advanced lung cancer, and to provide new insights for more targeted health education and medical care services. Methods A semi-structured in depth interview based on the theory of planned behavior was conducted among 30 patients with advanced lung cancer who experienced medical delay from November to December in 2021 admitted to First Affiliated Hospital of Guangxi Medical University. The interview content was analyzed and ed by using Colaizzi phenomenological analysis method and Nvivo11.0 software. Results The delay duration of 30 patients with advanced lung cancer ranged from 90 to 213 days. Four subject groups were extracted by generic analysis: the cause of delay, the cause to seek medical help, the worry about the disease, and solutions. Conclusions The delay behavior of patients with advanced lung cancer is affected by external situational factors such as symptom severity, family economic capacity, social support, accessibility of health services, prevalence of novel coronavirus, and subjective psychological factors such as sense of stigma and burden of disease, it is necessary to reduce the occurrence of medical delay in patients with advanced lung cancer through the comprehensive management strategy of multiple channels. © 2023 Chinese Medical Journals Publishing House Co.Ltd. All rights reserved.
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Objective To explore the clinical effect and safety of Suhexiang Pills () in the treatment of patients infected with SARS-CoV-2. Methods A total of 192 patients infected with SARS-CoV-2 admitted to 17 hospitals including Beijing Hospital of Traditional Chinese Medicine Affiliated to Capital Medical University from December 2022 to January 2023 were randomly divided into control group and treatment group, with 89 patients in the treatment group and 103 in the control group. The patients in control group received basic treatment according to the Diagnosis and Treatment Protocol for COVID-19 (Trial Version 10). The patients in treatment group were oral administered with Suhexiang Pills on the basis of the control group, one pill each time, twice day. The patients in two groups were treated for 5 d. The clinical efficacy of the two groups after treatment was compared. The differences in scores of headache, chest pain, limb pain and inflammatory indexes before and after treatment were compared. Results After treatment, the total clinical effective rate of the treatment group was 95.51%, which was significantly higher than that of the control group (81.55%, P < 0.05). After treatment, headache, chest pain and limb pain scores were significantly decreased in both groups (P < 0.05), the headache score of the treatment group was significantly lower than that of the control group from the first day of treatment (P < 0.05), the chest pain score of the treatment group was significantly lower than that of the control group on the fifth day of treatment (P < 0.05), the limb pain score of the treatment group was significantly lower than that of the control group from the third day of treatment (P < 0.05). After treatment, the levels of C-reactive protein (CRP), procalcitonin (PCT), interleukin-6 (IL-6) in the two groups were decreased significantly (P < 0.05) and the levels of CRP and IL-6 in the treatment group were significantly lower than those of the control group (P < 0.05). There was no significant difference in the incidence of adverse events between the two groups. Conclusion Suhexiang Pills have a certain effect on headache, chest pain and limb pain, inhibiting the inflammatory response in patients infected with SARS-CoV-2, with good safety.Copyright © 2023 Editorial Office of Chinese Traditional and Herbal Drugs. All rights reserved.
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Understanding indoor occupancy patterns is crucial for energy model calibration, efficient operations of fresh air systems, and COVID-19 exposure risk assessment. University libraries, as one of centers of campus life, due to the high mobility and "foot-voting” nature of them, i.e., occupants pick seats in the micro-environments they prefer, provide a non-intrusive opportunity to carry out post-occupancy evaluations. We conducted a long-term online monitoring of occupancy in libraries of a university in China by web-crawling the online seat reservation system, based on which, we constructed two sets of databases consisting of around 70 million records of nearly 3, 000 seats in 4 library sections, with seat-level resolution and sampling frequency up to every 10 seconds. The informative data set depicts not only the overall spatio-temporal occupancy patterns, but also nuances hidden within seats and visits. The daily flow of the main libraries exceeded two visits per seat. Half of the visitors stayed at the libraries for 3-6 hours during a single occupancy. Semester schedules and campus accessibility together influence students' decisions on when and which library to go, while even within the same zone, some seats were always more popular than their neighbours. "Semi-isolation” is one of the candidate attractive features proposed to understand the underlying patterns. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.
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During the novel coronavirus pneumonia (COVID-19) pandemic from 2020 to 2021, lung transplantation entered a new stage of development worldwide. Globally, more than 70 000 cases of lung transplantation have been reported to the International Society for Heart and Lung Transplantation (ISHLT). With the development of medical techniques over time, the characteristics of lung transplant donors and recipients and the indications of pediatric lung transplantation recipients have undergone significant changes. Application of lung transplantation in the treatment of COVID-19-related acute respiratory distress syndrome (ARDS) has also captivated worldwide attention. Along with persistent development of lung transplantation, it will be integrated with more novel techniques to make breakthroughs in the fields of artificial lung and xenotransplantation. In this article, research progresses on the characteristics of lung transplant donors and recipients around the world were reviewed and the development trend was predicted, enabling patients with end-stage lung disease to obtain more benefits from the development of lung transplantation technique.Copyright © 2022 Organ Transplantation. All rights reserved.
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COVID-19's impacts have spread widely in all directions such as economy, people's lifestyles and well-being. Though existing studies have highlighted such an impact, it remains unclear how the current COVID-19 situation has affected the retrenchment, vaccination and global happiness. In this paper, we present an automated tool enables the public to view various insight. In particular, we integrate and analyze the data from various data sources and show how the COVID19 has impacted Singapore and globally. We employ the regression models to identify the correlation between Human Development Index, Stringency Index, Gross Domestic Product per Capita, Total Deaths from COVID-19, and Total Cases of COVID-19;the rate of vaccination and vaccine hesitancy;and the factors to positively correlate to the global happiness. The insight provided adds values to better fight against the COVID-19 pandemic and future global crisis. © 2023 IEEE.
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At present, coronavirus disease-19 (COVID-19) caused by novel coronavirus (SARS-CoV-2) has been spreading around the world, but no specific therapeutic drug or vaccine has been developed for the virus. By collecting the latest literature and searching related database websites, the biological characteristics and main targets of SARS-CoV-2, the clinical therapeu tic drugs and the latest drug research were reviewed to provide information for clinical treatment and provide reference for the research and development of new drugs against SARS-CoV-2.Copyright © 2020 Publication Centre of Anhui Medical University. All rights reserved.
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In December 2019, Wuhan, China, found SARS-CoV-2. It causes covid-19, a worldwide respiratory illness. Its global pandemic broke out unexpectedly, and the number of infections and deaths continued to rise dramatically, causing the collapse of medical systems and disease control organizations in many countries at the start of the outbreak. Vaccine research and development must be prioritized in order to control and reduce virus spread as soon as possible. The mRNA vaccine stands out among traditional vaccines due to its rapid research and development, ability to stimulate human dual immune responses and non-infectivity. Both humoral and cellular immunity can be stimulated by mRNA vaccines, which means that the produced T cells can help eliminate antigens in time, so their number does not increase in order to protect the original cells, and they can also create long-lived plasma cells and memory B cells to continue playing an immunological role for years. mRNA vaccines may not need repeated injections, unlike inactivated vaccinations, which may enhance efficiency. the first mRNA vaccinations that were made accessible to the general population when the US FDA approved its emergency use in December 2020. The generation of mRNA for the mRNA vaccination uses cell-free expression techniques and in vitro transcription-based systems. LNPs system, a nanoscale vesicle that can enclose mRNA in their cavity and imitates the lipid structure of the cell membrane, is the most often utilized delivery system in mRNA vaccines. The most common mRNA vaccine technique involves injecting a genetic component that tells the body to make a protein fragment of a specific pathogen, which the immune system recognizes and keeps mounting a robust response if it is subsequently exposed to that pathogen. © 2023 SPIE.
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Background The global spread of coronavirus disease 2019 (COVID-19) continues to threaten human health security, exerting considerable pressure on healthcare systems worldwide. While prognostic models for COVID-19 hospitalized or intensive care patients are currently available, prognostic models developed for large cohorts of thousands of individuals are still lacking. Methods Between February 4 and April 16, 2020, we enrolled 3,974 patients admitted with COVID-19 disease in the Wuhan Huo-Shen-Shan Hospital and the Maternal and Child Hospital, Hubei Province, China. (1) Screening of key prognostic factors: A univariate Cox regression analysis was performed on 2,649 patients in the training set, and factors affecting prognosis were initially screened. Subsequently, a random survival forest model was established through machine analysis to further screen for factors that are important for prognosis. Finally, multivariate Cox regression analysis was used to determine the synergy among various factors related to prognosis. (2) Establishment of a scoring system: The nomogram algorithm established a COVID-19 patient death risk assessment scoring system for the nine selected key prognostic factors, calculated the C index, drew calibration curves and drew training set patient survival curves. (3) Verification of the scoring system: The scoring system assessed 1,325 patients in the test set, splitting them into high- and low-risk groups, calculated the C-index, and drew calibration and survival curves. Results The cross-sectional study found that age, clinical classification, sex, pulmonary insufficiency, hypoproteinemia, and four other factors (underlying diseases: blood diseases, malignant tumor;complications: digestive tract bleeding, heart dysfunction) have important significance for the prognosis of the enrolled patients with COVID-19. Herein, we report the discovery of the effects of hypoproteinemia and hematological diseases on the prognosis of COVID-19. Meanwhile, the scoring system established here can effectively evaluate objective scores for the early prognoses of patients with COVID-19 and can divide them into high- and low-risk groups (using a scoring threshold of 117.77, a score below which is considered low risk). The efficacy of the system was better than that of clinical classification using the current COVID-19 guidelines (C indexes, 0.95 vs. 0.89). Conclusions Age, clinical typing, sex, pulmonary insufficiency, hypoproteinemia, and four other factors were important for COVID-19 survival. Compared with general statistical methods, this method can quickly and accurately screen out the relevant factors affecting prognosis, provide an order of importance, and establish a scoring system based on the nomogram model, which is of great clinical significance. © Wolters Kluwer Health, Inc. All rights reserved.
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Increasing amounts of surfactants are used and emitted into the environment due to the COVID-19 pandemic, posing potential threats to ecological health. Algal-bacterial aerobic granular sludge (A-BAGS), with the advantages of compact structure, high-efficient nutrient uptake, and high tolerance to harsh conditions, was attempted in this study to treat surfactant-containing wastewater at relatively high concentrations. The treatment performance was also compared to bacterial AGS (BAGS). Results showed that A-BAGS is preferable for treating wastewater containing a high SDS concentration (30 mg/L), achieving nutrient removal efficiency of 86.3 % for organic carbon, 60.5 % for total nitrogen, and 58.7 % for total phosphorus within a short duration, compared to 70.1 %, 52.8 % and 42.3 % in BAGS reactor. Besides, the removal rate of ammonia nitrogen by A-BAGS was much faster than that of BAGS. The above results confirmed that A-BAGS is a promising technology for treating surfactant-containing wastewater with high nutrient removal efficiency being maintained.Copyright © 2023 Elsevier Ltd
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In December 2019, an unprecedented outbreak of the novel coronavirus disease 2019 (COVID-19), an infectious disease caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) began to spread internationally, now impacting more than 293,750,692 patients with 5,454,131 deaths globally as of January 5, 2022. COVID-19 is highly pathogenic and contagious which has caused a large-scale epidemic impacting more deaths than the severe acute respiratory syndrome (SARS) epidemic in 2002-2003 or the Middle East respiratory syndrome (MERS) epidemic in 2012-2013. Although COVID-19 symptoms are mild in most people, in those with pre-existing comorbidities there is an increased risk of progression to severe disease and death. In an attempt to mitigate this pandemic, urgent public health measures including quarantining exposed individuals and social distancing have been implemented in most states, while some states have even started the process of re-opening after considering both the economic and public health consequences of social distancing measures. While prevention is crucial, both novel agents and medications already in use with other indications are being investigated in clinical trials for patients with COVID-19. The collaboration between healthcare providers, health systems, patients, private sectors, and local and national governments is needed to protect both healthcare providers and patients to ultimately overcome this pandemic. The purpose of this review is to summarize the peer-reviewed and preprint literature on the epidemiology, transmission, clinical presentation, and available therapies as well as to propose a preventive strategy to overcome the present global pandemic.
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The clustering of large numbers of heterogeneous features is a hot topic in multi-view communities. Most existing multi-view clustering (MvC) methods employ matrix factorization or anchor strategies to handle large-scale datasets. The former operates on the original data and is, therefore, sensitive to noise and feature redundancy, which is reflected in the final clustering performance. The latter requires post -processing steps to generate the clustering results, which may be suboptimal owing to the isolation steps. To address the above problems, we propose one-stage multi-view subspace clustering with dictionary learning (OSMvSC). Specifically, we integrate dictionary learning, representation coefficient matrix learning, and matrix factorization as a unified learning framework, which directly learns the dictionary and representation coefficient matrix to encode the original multi-view data, and obtains the clustering results with linear time complexity without any postprocessing step. By manipulating the class centroid with the nuclear norm, a more compact and discriminative class centroid representation can be obtained to further improve clustering performance. An effective optimization algorithm with guaranteed convergence is designed to solve the proposed method. Substantial experiments on various real-world multi-view datasets demonstrate the effectiveness and superiority of the proposed method. The source code is available at https://github.com/justcallmewilliam/OSMvSC.(c) 2022 Elsevier B.V. All rights reserved.
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The current ongoing COVID-19 pandemic caused by the SARS-CoV-2 virus, has severely affected our daily life routines and behavior patterns. According to the World Health Organization, there have been 93 million confirmed cases with more than 1.99 million confirmed death around 235 Countries, areas or territories until 15 January 2021, 11:00 GMT+11. People who are affected with COVID-19 have different symptoms from people to people. When large amounts of patients are affected with COVID-19, it is important to quickly identify the health conditions of patients based on the basic information and symptoms of patients. Then the hospital can arrange reasonable medical resources for different patients. However, existing work has a low recall of 15.7% for survival predictions based on the basic information of patients (i.e., false positive rate (FPR) with 84.3%, FPR: actually survival but predicted as died). There is much room for improvement when using machine learning-based techniques for COVID-19 prediction. In this paper, we propose DeCoP to train a classifier to predict the survival of COVID-19 patients with high recall and F1 score. DeCoP is a deep learning (DL)-based scheme of Bidirectional Long Short-Term Memory (BiLSTM) along with Fuzzy-based Information Decomposition (FID) to predict the survival of patients. First of all, we apply FID oversampling to redistribute the training data of the Open COVID-19 Data Working Group. Then, we employ BiLSTM to learn the high-level feature representations from the redistributed dataset. After that, the high-level feature vector will be used to train the prediction model. Experimental results show that our proposed scheme achieves outstanding performances. Precisely, the improvement achieves about 19% and 18% in terms of recall and F1-measure.
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By 2019 COVID-19, since the epidemic, the number of relevant documents exponentially level rise. Faced with a large amount of literature, this research provides convenience for exploring the connection between research topics and fields and quickly understanding relevant literature information. We pass on the data set after data cleansing using the LDA(Latent Dirichlet allocation) methods, and Berts and K-means modeling method extracting topic keywords. Use knowledge graph tools to output relevant visual graphics and systematically extract adequate information. Through text mining of biomedical research papers related to COVID-19, the improved model is used to analyze and make recommendations to respond to and prevent the COVID-19 pandemic. This research can support the rapid and in-depth analysis of a large number of relevant documents and can be used in future research to support real-time scientific disease research.
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In the wake of the COVID-19 pandemic, many studies have begun to address what some refer to as the "new normal,” comprising hybrid arrangements of employees working from home and working at the office with varying schedule arrangements. While many of the studies to date addressed how employees coped with work-from-home, we sought to investigate how employees dealt with a transition to the new normal of hybrid arrangements. To shed light on this topic, we conducted a survey-based case study at one office location of a large, multinational software corporation. The site sought to transition employees fully working from home to working two days remotely and three predefined days in their shared workspace. Our survey results indicated a substantial decline in work satisfaction since the beginning of this transition, which can be explained by diverse work preferences. Furthermore, some software developers felt frustrated during this transition time;they described challenges they underwent and proposed potential solutions. In this paper, we present our lessons learned in this case study and describe some actionable recommendations for practitioners facing such transitions. © 2023 IEEE Computer Society. All rights reserved.
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This work investigates the interactions between oil prices and exchange rates of 6 typical oil importers (China, Japan, and India) and exporters (Canada, Russia, and Saudi Arabia) from 2006 to 2022. We employ a novel method to capture their causal interactions, namely pattern causality, and compare the results to that based on the volatility spillover method. The empirical analysis supports most existing findings that oil prices are bidirectional correlated with exchange rates. However, unlike previous studies that only investigate positive and negative causalities, we highlight dark causality as a more complex interaction. Moreover, dark causality suggests that successive increases (decreases) in oil prices tend to drive the exchange rates of oil exporters to act in an oscillatory manner rather than in a purely positive or opposite trend, and vice versa. Furthermore, we also reveal that dark causality shows dominance during crises, e.g., the global financial crisis, the European debt crisis, the epidemic of COVID-19, and the Russia-Ukraine conflict. Revealing three types of causalities between oil prices and exchange rates helps policymakers develop more diversified macroeconomic policies. Moreover, the newly identified dark causality can be a useful indicator for investors to risk management. © 2023