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1.
Comb Chem High Throughput Screen ; 25(4): 634-641, 2022.
Article in English | MEDLINE | ID: covidwho-1817778

ABSTRACT

BACKGROUND: Drug development requires a lot of money and time, and the outcome of the challenge is unknown. So, there is an urgent need for researchers to find a new approach that can reduce costs. Therefore, the identification of drug-target interactions (DTIs) has been a critical step in the early stages of drug discovery. These computational methods aim to narrow the search space for novel DTIs and to elucidate the functional background of drugs. Most of the methods developed so far use binary classification to predict the presence or absence of interactions between the drug and the target. However, it is more informative but also more challenging to predict the strength of the binding between a drug and its target. If the strength is not strong enough, such a DTI may not be useful. Hence, the development of methods to predict drug-target affinity (DTA) is of significant importance Method: We have improved the GraphDTA model from a dual-channel model to a triple-channel model. We interpreted the target/protein sequences as time series and extracted their features using the LSTM network. For the drug, we considered both the molecular structure and the local chemical background, retaining the four variant networks used in GraphDTA to extract the topological features of the drug and capturing the local chemical background of the atoms in the drug by using BiGRU. Thus, we obtained the latent features of the target and two latent features of the drug. The connection of these three feature vectors is then inputted into a 2 layer FC network, and a valuable binding affinity is the output. RESULT: We used the Davis and Kiba datasets, using 80% of the data for training and 20% of the data for validation. Our model showed better performance when compared with the experimental results of GraphDTA Conclusion: In this paper, we altered the GraphDTA model to predict drug-target affinity. It represents the drug as a graph and extracts the two-dimensional drug information using a graph convolutional neural network. Simultaneously, the drug and protein targets are represented as a word vector, and the convolutional neural network is used to extract the time-series information of the drug and the target. We demonstrate that our improved method has better performance than the original method. In particular, our model has better performance in the evaluation of benchmark databases.


Subject(s)
Drug Development , Neural Networks, Computer , Amino Acid Sequence , Drug Interactions , Molecular Structure
2.
Life Sci ; 301: 120602, 2022 May 01.
Article in English | MEDLINE | ID: covidwho-1814923

ABSTRACT

Megakaryocytes (MKs) are typical cellular components in the circulating blood flowing from the heart into the lungs. Physiologically, MKs function as an important regulator of platelet production and immunoregulation. However, dysfunction in MKs is considered a trigger in various diseases. It has been described that the lung is an important site of platelet biogenesis from extramedullary MKs, which may play an essential role in various pulmonary diseases. With detailed studies, there are different degrees of numerical changes of MKs in coronavirus disease 2019 (COVID-19), acute respiratory distress syndrome (ARDS), chronic obstructive pulmonary disease (COPD), lung cancer, pulmonary fibrosis (PF), and other pulmonary diseases. Also, MKs inhibit or promote the development of pulmonary diseases through various pathways. Here, we summarize the current knowledge of MKs in pulmonary diseases, highlighting the physiological functions and integrated molecular mechanisms. We aim to shine new light on not only the subsequent study of MKs but also the diagnosis and treatment of pulmonary diseases.

3.
Annu Int Conf IEEE Eng Med Biol Soc ; 2021: 2286-2289, 2021 11.
Article in English | MEDLINE | ID: covidwho-1799329

ABSTRACT

The use of network models to study the spread of infectious diseases is gaining increasing interests. They allow the flexibility to represent epidemic systems as networks of components with complex and interconnected structures. However, most of previous studies are based on networks of individuals as nodes and their social relationships (e.g., friendship, workplace connections) as links during the virus spread process. Notably, the transmission and spread of infectious viruses are more pertinent to human dynamics (e.g., their movements and interactions with others) in the spatial environment. This paper presents a novel network-based simulation model of human traffic and virus spread in community networks. We represent spatial points of interests (POI) as nodes where human subjects interact and perform activities, while edges connect these POIs to form a community network. Specifically, we derive the spatial network from the geographical information systems (GIS) data to provide a detailed representation of the underlying community network, on which human subjects perform activities and form traffics that impact the process of virus transmission and spread. The proposed framework is evaluated and validated in a community of university campus. Experimental results showed that the proposed simulation model is capable of describing interactive human activities at an individual level, as well as capturing the spread dynamics of infectious diseases. This framework can be extended to a wide variety of infectious diseases and shows strong potentials to aid the design of intervention policies for epidemic control.


Subject(s)
Epidemics , Virus Diseases/transmission , Computer Simulation , Humans
4.
Adv Sci (Weinh) ; 9(14): e2104333, 2022 05.
Article in English | MEDLINE | ID: covidwho-1782562

ABSTRACT

Coronavirus disease 2019 (COVID-19) remains a global public health threat. Hence, more effective and specific antivirals are urgently needed. Here, COVID-19 hyperimmune globulin (COVID-HIG), a passive immunotherapy, is prepared from the plasma of healthy donors vaccinated with BBIBP-CorV (Sinopharm COVID-19 vaccine). COVID-HIG shows high-affinity binding to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike (S) protein, the receptor-binding domain (RBD), the N-terminal domain of the S protein, and the nucleocapsid protein; and blocks RBD binding to human angiotensin-converting enzyme 2 (hACE2). Pseudotyped and authentic virus-based assays show that COVID-HIG displays broad-spectrum neutralization effects on a wide variety of SARS-CoV-2 variants, including D614G, Alpha (B.1.1.7), Beta (B.1.351), Gamma (P.1), Kappa (B.1.617.1), Delta (B.1.617.2), and Omicron (B.1.1.529) in vitro. However, a significant reduction in the neutralization titer is detected against Beta, Delta, and Omicron variants. Additionally, assessments of the prophylactic and treatment efficacy of COVID-HIG in an Adv5-hACE2-transduced IFNAR-/- mouse model of SARS-CoV-2 infection show significantly reduced weight loss, lung viral loads, and lung pathological injury. Moreover, COVID-HIG exhibits neutralization potency similar to that of anti-SARS-CoV-2 hyperimmune globulin from pooled convalescent plasma. Overall, the results demonstrate the potential of COVID-HIG against SARS-CoV-2 infection and provide reference for subsequent clinical trials.


Subject(s)
COVID-19 Vaccines , COVID-19 , Globulins , Animals , COVID-19/therapy , Globulins/therapeutic use , Humans , Immunization, Passive , Mice , SARS-CoV-2 , Spike Glycoprotein, Coronavirus
5.
Clin Rheumatol ; 2022 Mar 30.
Article in English | MEDLINE | ID: covidwho-1767510

ABSTRACT

OBJECTIVES: COVID-19 pandemic has already had a tremendous impact on the process of human society; the survival of mankind and the healthy living environment deterioration with the influence will last for many years. This meta-analysis aims to assess the risk of COVID-19 in patients with rheumatic diseases. METHODS: PubMed, Web of Science, Embase, China National Knowledge Infrastructure (CNKI), and Chinese Biomedical Database (CBM) were systematically searched with no language restriction up to July 5, 2021. The pooled rates were synthesized by fixed effect model or random effect model depending on heterogeneity. RESULTS: A total of 83 articles were included in this meta-analysis. The incidence of COVID-19 in patient with rheumatic diseases was 0.0190 (95% CI: 0.0136-0.0252), and the hospitalization rate, intensive care unit admission rate, mechanical ventilation rate, and case fatality rate of patients with rheumatic diseases infected with COVID-19 were 0.4396 (95% CI: 0.3899-0.4898), 0.0635 (95% CI: 0.0453-0.0836), 0.0461 (95% CI: 0.0330-0.0609), and 0.0346 (95% CI: 0.0218-0.0493), respectively. CONCLUSIONS: Our research shows that patients with rheumatic diseases have great risk of COVID-19. Differences in COVID-19 incidence, hospitalization rates, and mortality rates in regions were statistically significant. We still need to pay attention to the risk of COVID-19 in patients with rheumatic diseases. KEY POINTS: • Although the risk of COVID-19 in patients with rheumatic diseases has been discussed in previous meta-analysis, their research directions were inconsistent, and few studies focus on prevalence or serious outcomes of COVID-19 in patient with rheumatic diseases, while the quality of these articles was variable. • The incidence of COVID-19 and serious clinical outcomes in patients with rheumatic diseases were still high along with differential risks in most regions. • The use of glucocorticoids and conventional synthetic disease-modifying antirheumatic drugs did not affect the hospitalization rate and mortality in rheumatism patients with COVID-19.

6.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-325534

ABSTRACT

Objective: Both 2019 novel coronavirus disease (COVID-19) and avian influenza A (H1N1) are serious acute respiratory diseases with a predisposition to acute respiratory distress syndrome (ARDS). Our aim was to compare the clinical characteristics of patients with COVID-19 and H1N1 influenza complicated with ARDS. Methods: : We retrospectively studied data of 12 patients with ARDS (7 with COVID-19;5 with H1N1 influenza) who were managed at The Second Affiliated Hospital of Xiamen Medical College and Xinglin Branch of the First Affiliated Hospital of Xiamen University between December 20, 2019 and February 29, 2020. We extracted the clinical information and outcomes from the hospital medical charts. Results: : Patients with COVID-19 were older and were more likely to have underlying diseases. Low-to-moderate fever was more frequent and upper respiratory tract symptoms were less common in COVID-19 patients. Chest computed tomography of patients with COVID-19 more frequently revealed bilateral nodular patchy ground-glass opacities in the subpleural and central lobular regions. Heart disorders and pleural effusion were less frequent, and coagulopathy was more common in patients with COVID-19. The average duration of stay in the respiratory intensive care unit was longer in patients with COVID-19. The disease severity and clinical outcomes did not differ significantly between the two groups. Conclusion: Older age, higher comorbidity frequency, abnormal coagulation responses, longer hypoxemia duration, pulmonary fibrosis, and poorer clinical outcomes are the main characteristics in patients with COVID-19 who have ARDS. This calls for closer dynamic monitoring and more rigorous follow-up.

7.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-324292

ABSTRACT

Recently emerging SARS-CoV-2 virus has caused a global pandemic, with millions of infections and over 200, 000 deaths1. However, development of effective anti-coronavirus treatments has lagged behind. Competitive co-evolution between microbes and viruses has led to the diversification of microbe’s CRISPR/Cas defense systems against infectious viruses2,3. Among class-2 single effector systems, Cas13 is effective in combating RNA phages4. Previous studies have discovered novel Cas9 and Cas12 systems from metagenomic sequence of natural microbes5-7. Here we report the identification of two additional compact Cas13 families from natural microbes that are effective in degrading RNA viruses in mammalian cells. Using metagenomic terabase data sets, we searched for previously uncharacterized Cas13 genes proximal to the CRISPR array with a customized computational pipeline, and identified two most compact families (775 to 803 amino acids) of CRISPR-Cas ribonucleases, named hereafter as CRISPR/Cas type VI-E and VI-F. Out of seven Cas13 proteins, we found that Cas13e.1 was the smallest and could be engineered for efficient RNA interference and base editing in cultured mammalian cell lines. Moreover, Cas13e.1 has a high activity for degrading SARS-CoV-2 sequences and the genome of live influenza A virus (IAV). Together with a minimal pool of 10 crRNAs, Cas13e.1 could target over 99% of all known 3,137 coronavirus genomes for achieving antiviral defense. Overall, our results demonstrated there exist untapped bacterial defense systems in natural microbes that can function efficiently in mammalian cells, thus potentially useful for preventing viral infection in humans such as COVID-19.

8.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-323735

ABSTRACT

Background: Despite the growing number of studies on the Coronavirus Disease-19 (COVID-19), little is known about the association of menopausal status with COVID-19 outcomes. Materials: and methods: In this retrospective study, we included 336 COVID-19 in-patients between February 15, 2020 and April 30, 2020 at the Taikang Tongji Hospital (Wuhan), China. Electronic medical records, including patient demographics, laboratory results, and chest computed tomography (CT) images were reviewed. Results: : In total, 300 patients with complete clinical outcomes were included for analysis. The mean age was 65.3 years and most patients were women (n=167, 55.7%). Over 50% of patients presented with comorbidities, with hypertension (63.5%) being the most common comorbidity. After propensity-score matching, results showed that men had significantly higher odds than premenopausal women for developing severe disease type (23.7% vs. 0%, OR 17.12, 95% CI 1.00–293.60;p =0.003) and bilateral lung infiltration (86.1% vs. 64.7%, OR 3.39, 95% CI 1.08–10.64;p = 0.04), but not for mortality (2.0% vs. 0%, OR 0.88, 95% CI 0.04–19.12, p =1.00). However, non-significant difference was observed among men and post-menopause women in the percentage of severe disease type (32.7% vs. 41.7%, OR 0.68, 95% CI 0.37–1.24, p =0.21) and bilateral lung infiltration (86.1% vs. 91.7%, OR 0.56, 95% CI 0.22–1.47, p =0.24), mortality (2.0% vs. 6.0%, OR 0.32, 95% CI 0.06–1.69, p =0.25). Conclusions: : Men had higher disease severity than premenopausal women, while the differences disappeared between postmenopausal women and men. These findings support aggressive treatment for the poor-prognosis of postmenopausal women in clinical practice.

9.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-315882

ABSTRACT

Background: The outbreak and pandemic of coronavirus SARS CoV 2 caused significant threaten to global public health and economic consequences. It is extremely urgent that global people must take actions to develop safe and effective preventions and therapeutics. Nanobodies, which are derived from single‑chain camelid antibodies, had shown antiviral properties in various challenge viruses. In this study, multivalent nanobodies with high affinity blocking SARS CoV 2 spike interaction with ACE2 protein were developed. Results: Totally, four specific nanobodies against spike protein and its RBD domain were screened from a naïve VHH library. Among them, Nb91 hFc and Nb3 hFc demonstrated antiviral activity by neutralizing spike pseudotyped viruses in vitro. Subsequently, multivalent nanobodies were constructed to improve the neutralizing capacity. As a result, heterodimer nanobody Nb91 Nb3 hFc exhibited the strongest RBD binding affinity and neutralizing ability against SARS CoV 2 pseudoviruses with an IC50 value at approximately 1.54 nM. Conclusions: The present study indicated that naïve VHH library could be used as a potential resource for rapid acquisition and exploitation of antiviral nanobodies. Heterodimer nanobody Nb91 Nb3 hFc may serve as a potential therapeutic agent for the treatment of COVID 19.

10.
Nurs Open ; 9(2): 1164-1172, 2022 03.
Article in English | MEDLINE | ID: covidwho-1626656

ABSTRACT

AIM: To explore and describe nurses' self-expression media image during COVID-19 pandemic in China. BACKGROUND: Nurses play an important role in COVID-19 pandemic. Although nurses were widely reported by the media, which included praise for nurses and nursing work, the researches on how nurses expressed their self-images were limited. DESIGN: Qualitative media analysis. METHODS: Qualitative media analysis was conducted from January to April 2020, the researchers collected images and texts of 16 Chinese nurses who take care of COVID-19 patients. These images and texts were published on WeChat Moments by themselves. After analysed each image and text, researchers identified the denotative and connotative elements in each image and summarized each image in narrative way. FINDINGS: This study analysed 219 pictures and 15 short videos of 16 nurses' self-expression in WeChat moments. In this study, the media image self-expression of nurses were mostly positive. The images expressed by nurses in this study included care image; hero image; soldier image; female image; hope image and team image. Nurses rarely showed negative images in the media; The negative nurses image were expressed in hidden way, which included exhausted nurses image and fragile nurses image. Moreover, the nurse self-expression media image emphasized the nursing professionalism, but less showed the nursing connotation. CONCLUSIONS: The positive media image self-expression of nurses should be encouraged. Nurse Managers should pay attention to the deficiency of nursing image expression and guide nurses to show the essence and connotation of nursing.


Subject(s)
COVID-19 , Communications Media , Nurse Administrators , Female , Humans , Pandemics , SARS-CoV-2
11.
IEEE Robot Autom Lett ; 7(1): 626-633, 2022 Jan.
Article in English | MEDLINE | ID: covidwho-1583802

ABSTRACT

Infectious diseases such as COVID-19 have severe impacts on both economy and public health in the US and the world. Due to the heterogeneity of virus spread, there are spatial variations in the demand for medical resources such as personal protective equipment (PPE), testing kits, and vaccines. The availability of such medical resources is critical to effective epidemic control. Although these resources can be readily transported to designated areas for fighting an epidemic, the demand is increasing and varying in space that places significant stress on the supply and allocation of medical resources. However, little has been done on the tessellation of infection distributions for resource management. In this letter, we develop new tessellation algorithms for decision support in epidemic resource allocation and management. The objective is to estimate resource locations and coverage based on the spatial analysis of heterogeneous infection distribution. First, spatial tessellation centroids are initialized through either greedy or cluster-centric approaches. Next, the locations of tessellation centroids are calibrated through a gradient learning algorithm. Lastly, the spread tessellation is computed to provide an estimation of resource coverages under the heterogeneous infection distribution. The proposed methodology is evaluated and validated using a COVID-19 case study of infection data in Pennsylvania. Experimental results show the proposed methodology effectively tessellates the spread of infectious diseases. The new spread tessellation algorithms are shown to have strong potentials for epidemic decision support in infection modelling and resource allocation.

12.
Annu Int Conf IEEE Eng Med Biol Soc ; 2021: 2278-2281, 2021 11.
Article in English | MEDLINE | ID: covidwho-1566198

ABSTRACT

Since the pandemic of COVID-19 began in January 2020, the world has witnessed drastic social-economic changes. To harness the virus spread, several studies have been done to study contributing factors that are pertinent to COVID-19 transmission risks. However, little has been done to investigate how human activities on the spatial network are correlated to the virus transmission and spread. This paper performs a statistical analysis to examine interrelationships between spatial network characteristics and cumulative cases of COVID-19 in US counties. Specifically, both county-level transportation profiles (e.g., the total number of commute workers, route miles of freight railroad) and road network characteristics of US counties are considered. Then, the lasso regression model is utilized to identify a sparse set of significant variables that are sensitive to the response variable of COVID-19 cases. Finally, the fixed-effect model is built to capture the relationship between the selected set of predictors and the response variable. This work helps identify and determine salient features from spatial network characteristics and transportation profiles, thereby improving the understanding of COVID-19 spread dynamics. These significant variables can also be utilized to develop simulation models for the prediction of real-time positions of virus spread and the optimization of intervention strategies.


Subject(s)
COVID-19 , Humans , Pandemics , Research Design , SARS-CoV-2 , Social Change
13.
Annu Int Conf IEEE Eng Med Biol Soc ; 2021: 2003-2006, 2021 11.
Article in English | MEDLINE | ID: covidwho-1566191

ABSTRACT

The COVID-19 preparedness plans by the Centers for Disease Control and Prevention strongly underscores the need for efficient and effective testing strategies. This, in turn, calls upon the design and development of statistical sampling and testing of COVID-19 strategies. However, the evaluation of operational details requires a detailed representation of human behaviors in epidemic simulation models. Traditional epidemic simulations are mainly based upon system dynamic models, which use differential equations to study macro-level and aggregated behaviors of population subgroups. As such, individual behaviors (e.g., personal protection, commute conditions, social patterns) can't be adequately modeled and tracked for the evaluation of health policies and action strategies. Therefore, this paper presents a network-based simulation model to optimize COVID-19 testing strategies for effective identifications of virus carriers in a spatial area. Specifically, we design a data-driven risk scoring system for statistical sampling and testing of COVID-19. This system collects real-time data from simulated networked behaviors of individuals in the spatial network to support decision-making during the virus spread process. Experimental results showed that this framework has superior performance in optimizing COVID-19 testing decisions and effectively identifying virus carriers from the population.


Subject(s)
COVID-19 , Epidemics , COVID-19 Testing , Humans , SARS-CoV-2 , United States
14.
Clinical Complementary Medicine and Pharmacology ; : 100009, 2021.
Article in English | ScienceDirect | ID: covidwho-1509628

ABSTRACT

Backgroud : The outbreak of COVID-19 has brought unprecedented perils to human health and raised public health concerns in more than two hundred countries. Safe and effective treatment scheme is needed urgently. Objective : To evaluate the effects of integrated TCM and western medicine treatment scheme on COVID-19. Methods : A single-armed clinical trial was carried out in Hangzhou Xixi Hospital, an affiliated hospital with Zhejiang Chinese Medical University. 102 confirmed cases were screened out from 725 suspected cases and 93 of them were treated with integrated TCM and western medicine treatment scheme. Results : 83 cases were cured, 5 cases deteriorated, and 5 cases withdrew from the study. No deaths were reported. The mean relief time of fever, cough, diarrhea, and fatigue were (4.78±4.61) days, (7.22±4.99) days, (5.28± 3.39) days, and (5.28± 3.39) days, respectively. It took (14.84±5.50) days for SARS-CoV-2 by nucleic acid amplification-based testing to turn negative. Multivariable cox regression analysis revealed that age, BMI, PISCT, BPC, AST, CK, BS, and UPRO were independent risk factors for COVID-19 treatment. Conclusion : Our study suggested that integrated TCM and western medicine treatment scheme was effective for COVID-19.

15.
Biomed Hub ; 6(3): 102-110, 2021.
Article in English | MEDLINE | ID: covidwho-1484151

ABSTRACT

INTRODUCTION: COVID-19, a continuously emerging human-to-human infectious disease, has exerted a significant impact on the mental health of college students. However, little is known regarding the variations in the mental health issues experienced by college students during the peak versus reopening stages of the COVID-19 epidemic in China. METHODS: To assess these issues, an online longitudinal survey was conducted via a WeChat applet. Undergraduates (n = 300) were recruited from 26 universities throughout Jinan in February 2020 (T1 - the epidemic peak stage) and in January 2021 (T2 - the society reopening stage). Their mental status was determined using the Patient Health Questionnaire-9, the Generalized Anxiety Disorder-7 item, and the Insomnia Severity Index. RESULTS: Of the original 300 college students recruited for this survey, 294 responses at T1 and 285 at T2 were analyzed. Compared with responses obtained at T1, college students at T2 showed a greater prevalence of depression (65.3 vs. 51.0%; p = 0.001) and anxiety (47.7 vs. 38.1%, p = 0.019), and experienced more severe depression (p < 0.001) and anxiety (p < 0.001). Both males (p = 0.03) and females (p < 0.01) showed higher levels of depression at T2 versus T1, while no differences were obtained with regard to anxiety and insomnia. At T1, Grade 4 students showed greater levels of depression (p = 0.005) and anxiety (p = 0.008) than that of Grade 1 students. While at T2, only greater levels of depression (p = 0.004) were present when compared with that of Grade 1 students. Additionally, Grade 4 college students demonstrated a greater prevalence of depression at T2 versus T1 (p = 0.03), but no statistically differences were present for anxiety and insomnia. No statistically significant differences were obtained among the 4 grades of college students for insomnia at either the T1 or T2. CONCLUSION: With progression of the COVID-19 epidemic, college students showed increasing levels of depression and anxiety, with Grade 4 college students being most seriously affected. It is imperative that intervention strategies be implemented to mitigate against these mental health issues resulting from the COVID-19 epidemic.

16.
Stem Cells Int ; 2021: 2263469, 2021.
Article in English | MEDLINE | ID: covidwho-1443669

ABSTRACT

The coronavirus disease of 2019 (COVID-19) has evolved into a worldwide pandemic. Although CT is sensitive in detecting lesions and assessing their severity, these works mainly depend on radiologists' subjective judgment, which is inefficient in case of a large-scale outbreak. This work focuses on developing a CT-based radiomics model to assess whether COVID-19 patients are in the early, progressive, severe, or absorption stages of the disease. We retrospectively analyzed the CT images of 284 COVID-19 patients. All of the patients were divided into four groups (0-3): early (n = 75), progressive (n = 58), severe (n = 75), and absorption (n = 76) groups, according to the progression of the disease and the CT features. Meanwhile, they were split randomly to training and test datasets with the fixed ratio of 7 : 3 in each category. Thirty-eight radiomic features were nominated from 1688 radiomic features after using select K-best method and the ElasticNet algorithm. On this basis, a support vector machine (SVM) classifier was trained to build this model. Receiver operating characteristic (ROC) curves were generated to determine the diagnostic performance of various models. The precision, recall, and f 1-score of the classification model of macro- and microaverage were 0.82, 0.82, 0.81, 0.81, 0.81, and 0.81 for the training dataset and 0.75, 0.73, 0.73, 0.72, 0.72, and 0.72 for the test dataset. The AUCs for groups 0, 1, 2, and 3 on the training dataset were 0.99, 0.97, 0.96, and 0.93, and the microaverage AUC was 0.97 with a macroaverage AUC of 0.97. On the test dataset, AUCs for each group were 0.97, 0.86, 0.83, and 0.89 and the microaverage AUC was 0.89 with a macroaverage AUC of 0.90. The CT-based radiomics model proved efficacious in assessing the severity of COVID-19.

17.
Matern Fetal Med ; 2(2): 65-67, 2020 Apr.
Article in English | MEDLINE | ID: covidwho-1410265
19.
Int J Med Sci ; 18(14): 3236-3248, 2021.
Article in English | MEDLINE | ID: covidwho-1360866

ABSTRACT

Natural killer cells, one of the important types of innate immune cells, play a pivotal role in the antiviral process in vivo. It has been shown that increasing NK cell activity may promote the alleviation of viral infections, even severe infection-induced sepsis. Given the current state of the novel coronavirus (SARS-CoV-2) global pandemic, clarifying the anti-viral function of NK cells would be helpful for revealing the mechanism of host immune responses and decipher the progression of COVID-19 and providing important clues for combating this pandemic. In this review, we summarize the roles of NK cells in viral infection and sepsis as well as the potential possibilities of NK cell-based immunotherapy for treating COVID-19.


Subject(s)
COVID-19/immunology , Host-Pathogen Interactions/immunology , Killer Cells, Natural/physiology , Sepsis/immunology , COVID-19/therapy , Humans , Immunotherapy , SARS-CoV-2 , Sepsis/virology
20.
Cell Prolif ; 54(9): e13091, 2021 Sep.
Article in English | MEDLINE | ID: covidwho-1320384

ABSTRACT

OBJECTIVES: Recent studies have shown the presence of SARS-CoV-2 in the tissues of clinically recovered patients and persistent immune symptoms in discharged patients for up to several months. Pregnant patients were shown to be a high-risk group for COVID-19. Based on these findings, we assessed SARS-CoV-2 nucleic acid and protein retention in the placentas of pregnant women who had fully recovered from COVID-19 and cytokine fluctuations in maternal and foetal tissues. MATERIALS AND METHODS: Remnant SARS-CoV-2 in the term placenta was detected using nucleic acid amplification and immunohistochemical staining of the SARS-CoV-2 protein. The infiltration of CD14+ macrophages into the placental villi was detected by immunostaining. The cytokines in the placenta, maternal plasma, neonatal umbilical cord, cord blood and amniotic fluid specimens at delivery were profiled using the Luminex assay. RESULTS: Residual SARS-CoV-2 nucleic acid and protein were detected in the term placentas of recovered pregnant women. The infiltration of CD14+ macrophages into the placental villi of the recovered pregnant women was higher than that in the controls. Furthermore, the cytokine levels in the placenta, maternal plasma, neonatal umbilical cord, cord blood and amniotic fluid specimens fluctuated significantly. CONCLUSIONS: Our study showed that SARS-CoV-2 nucleic acid (in one patient) and protein (in five patients) were present in the placentas of clinically recovered pregnant patients for more than 3 months after diagnosis. The immune responses induced by the virus may lead to prolonged and persistent symptoms in the maternal plasma, placenta, umbilical cord, cord blood and amniotic fluid.


Subject(s)
Cytokines/analysis , Placenta/virology , RNA, Viral/isolation & purification , SARS-CoV-2/genetics , SARS-CoV-2/immunology , Viral Proteins/isolation & purification , Adult , Amniotic Fluid/chemistry , COVID-19/pathology , Female , Fetal Blood/chemistry , Humans , Infant, Newborn , Macrophages/immunology , Nucleic Acid Amplification Techniques , Placenta/immunology , Pregnancy , RNA, Viral/blood , RNA, Viral/genetics , SARS-CoV-2/isolation & purification , Viral Proteins/blood
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