bluuebunny/crossref_metadata_2025_split huggingface dataset:
Revisiting MERS-CoV Outbreak Chest Radiographic Initial Findings, Temporal Progression and Correlation to Outcomes: Insights for the COVID-19 Pandemic
Authors: Ajlan Amr M. , Abourokbah Nesreen H. , Alturkistany Samira , Alharthy Ahmed , Ahyad Rayan A. , Ashour Majed , Madani Tariq A. 
Published: June 2020

Abstract
Objective:Accounts of initial and follow-up chest X-rays (CXRs) of the Middle East respiratory coronavirus (MERS-CoV) patients, and correlation with outcomes, are sparse. We retrospectively evaluated MERS-CoV CXRs initial findings, temporal progression and outcomes correlation. Such insights may be valuable during the current coronavirus disease 2019 (COVID-19) pandemic, given the clinical and imaging similarities described in both entities.Methods:Fifty-three real-time reverse-transcriptase-polymerase chain reaction (rRT-PCR)-confirmed MERS-CoV patients with CXRs were retrospectively identified from November 2013 to October 2014. Initial and follow-up CXR imaging findings and distribution were evaluated over 75 days. Findings were correlated with outcomes.Results:Twenty-two of 53 (42%) initial CXRs were normal. In 31 (68%) abnormal initial CXRs, 15 (48%) showed bilateral non-diffuse involvement, 16 (52%) had ground-glass opacities (GGO), and 13 (42%) had peripheral distribution. On follow-up CXRs, mixed airspace opacities prevailed, seen in 16 (73%) of 22 patients 21-30 days after the initial CXRs. Bilateral non-diffuse involvement was the commonest finding throughout follow-up, affecting 16 (59%) of 27 patients 11-20 days after the initial CXRs. Bilateral diffuse involvement was seen in 5 (63%) of 8 patients 31-40 days after the initial CXRs. A bilateral diffuse CXR pattern had an odds ratio for mortality of 13 (95% CI=2-78) on worst and 18 (95% CI=3-119) on final CXRs (P-value <0.05).Conclusion: Initially, normal CXRs are common in MERS-CoV patients. Peripherally located ground-glass and mixed opacities are common on initial and follow-up imaging. The risk of mortality is higher when bilateral diffuse radiographic abnormalities are detected.
​Quantitative Evaluation of Hepatic Integrin Αvβ3 Expression by Positron-emission Tomography Imaging Using 18F-FPP-RGD2 in Rats With Non-alcoholic Steatohepatitis
Authors: Hiroyama Shuichi , Rokugawa Takemi , Ito Miwa , Iimori Hitoshi , Morita Ippei , Maeda Hiroki , Fujisawa Kae , Matsunaga Keiko , Shimosegawa Eku , Abe Kohji 
Published: June 2020

Abstract
Background Integrin αvβ3, which are expressed by activated hepatic stellate cells in non-alcoholic steatohepatitis (NASH), play an important role in the fibrosis. Recently, we reported that an RGD peptide positron emission tomography (PET) probe is useful as a predictor of hepatic fibrosis. Kinetic analysis of the RGD PET probe has been performed in tumours, but not in hepatic fibrosis. Therefore, we aimed to quantify hepatic integrin αvβ3 in a model of NASH by kinetic analysis using 18F-FPP-RGD2, an integrin αvβ3 PET probe.Methods 18F-FPP-RGD2 PET/CT scans were performed in control and NASH rats. Tissue kinetic analyses were performed using a one-tissue, two-compartment (1T2C) and a two-tissue, three-compartment (2T3C) model using an image-derived input function (IDIF) for the left ventricle. We then conducted correlation analysis between standard uptake values (SUVs) or volume of distribution (VT), evaluated using compartment kinetic analysis, and integrin αv or β3 protein expression.Results Biochemical and histological evaluation confirmed the development of NASH rats. Integrin αvβ3 protein expression and hepatic SUV were higher in NASH- than normal rats. The hepatic activity of 18F-FPP-RGD2 peaked rapidly after administration and then gradually decreased, whereas left ventricular activity rapidly disappeared. The 2T3C model was found to be preferable for 18F-FPP-RGD2 kinetic analysis in the liver. The VT (IDIF) for 18F-FPP-RGD2, calculated using the 2T3C model, was significantly higher in NASH- than normal rats and correlated strongly with hepatic integrin αv and β3 protein expression. The strengths of these correlations were similar to those between SUV60–90 min and hepatic integrin αv or β3 protein expression.Conclusions We have demonstrated that the VT (IDIF) of 18F-FPP-RGD2, calculated using kinetic modelling, positively correlates with integrin αv and β3 protein in the liver of NASH rats. These findings suggest that hepatic VT (IDIF) provides a quantitative assessment of integrin αvβ3 protein in liver.
Utilization of Maternity Waiting Homes to Increase Uptake of Immediate Postpartum Family Planning in Primary Health Care Facilities in Ethiopia
Authors: Asnake Mengistu , Tefera Bekele Belayihun , Tilahun Yeowndwossen , Zerihun Habtamu , Moges Fisseha , Tasissa Adeba , Tilahun Zerihun , Taddelle Atsede , Workneh Wondimagegnehu 
Published: June 2020

Abstract
Background: Maternity waiting home (MWH) within primary health care facilities is an ideal platform to reach women with family planning education and counseling. MWH users interact with health care providers on a regular basis throughout their waiting period and can prepare to initiate family planning method of their choice immediately after childbirth. However, to date, there has been no clear evidence about the use of MWHs to increase uptakes of immediate postpartum family planning (IPPFP). Therefore, the aim of this study is to assess the contribution of MWHs to increase IPPFP uptake among women who deliver in health facilities in Ethiopia.Methods: A comparative cross-sectional study design was conducted to collect quantitative data from women who gave birth in the past 12 months. Multi-stage random sampling procedures were employed to select 884 women. Descriptive summary and logistic regression with 95% confidence intervals were conducted in Stata 14 to analyze the data.Result: The prevalence of IPPFP use among women who used MWHs was 44% and 36% among those who did not use MWHs. The use of MWHs significantly contributed to increase immediate postpartum family planning uptake (OR = 1.48, 95% CI = 1.08- 2.04, p=.016).Conclusions: This study showed that, MWHs significantly contributed for improving IPPFP uptake by an average of 8%-point difference for beneficiaries within 48 hours after delivery. Developing a comprehensive package of service for maternal care has the potential to improve family planning uptake of postpartum women. Women’s who have used maternity waiting home more likely utilized postpartum family planning than women who have not used maternity waiting home. Providing a comprehensive package of services, including family planning information and counseling, in maternity waiting homes can have a positive impact on improving postpartum family planning use.
Traditional medicinal plants in South Tyrol (Northern Italy, Southern Alps) – biodiversity and use
Authors: Petelka Joshua , Plagg Barbara , Säumel Ina , Zerbe Stefan 
Published: June 2020

Abstract
Background: Worldwide mountain regions are recognized as hotspots of ethnopharmacologically relevant species diversity. The high plant diversity and isolated population of South Tyrol (Southern Alps, Italy) have prospered a unique traditional botanical knowledge of medicinal plants, which dates back to prehistoric times. However, changes in rural life and culture may threaten the unique biodiversity and cultural heritage. Our study aims to collect and analyze information on native plants used in traditional folk medicine, focusing on the preservation of botanical and cultural diversity.Methods: Data was collected by a review of published material documenting traditionally used medicinal plants of South Tyrol, to capture the total diversity of plants and their usage. We evaluated different parameters, comprising the Ethnobotanicity Index (EI), Ethnophytonomic Index (EPI), relative frequency of citation (RFC), Red List status, and regional legislation with regard to the plant species.Results: A total of 275 species, including 3 mushrooms and 3 lichens were identified, belonging to 72 families, most frequently to the Asteraceae, Rosaceae, and Lamiaceae. Most cited species were Hypericum perforatum L., Urtica dioica L., and Plantago lanceolata L. According to 12 ICPC-2 disease categories most frequently treated for human health were digestive and respiratory systems as well as the skin. A total of 27 species were listed as endangered, of which 16 are not protected and two are already extinct. Among the 59 predominantly Alpine species, 11 species are restricted to the high altitudes of the Alps and might be threatened by global warming.Conclusions: Our research revealed that ethnobotanical richness of South Tyrol is among the highest in Italy and throughout the Alps. Nevertheless, it is evident that biodiversity and traditional knowledge have been heavily eroded. Furthermore, we point out particularly sensitive species that should be reconsidered in legal regulations for stronger protection.
Bone Marrow Mesenchymal Stem Cells Regulate IL-4 and INF-γ Expression in AR Mouse Model
Authors: Zhao Chuanliang , Sun Jingwen , Cai Xiaojing , Zou Wentao , Zhang Jiaxiong 
Published: June 2020

Abstract
Background:

Bone marrow mesenchymal stem cells can promote the recovery of immune balance and regulate the balance of Th1/2 cells. Allergic rhinitis is a disease with Th1/2 imbalance mediated by IgE. It’s unclear whether BMSCs could regulate AR disease. In this study, the possible role of BMSCs was explored.
Methods :

AR mouse model was established by ovalbumin (OVA). 18 models were randomly divided into three groups: AR-sensitized, Stem-cell-returned, Medium-returned; six unsensitized mouses named normal-control. IgE, IL-4 and INF-γ levels were measured by Elisa. Observing migration of BMSCs by immunofluorescence. Flow cytometry used to detect changes of Th1/2. STAT 4/6 protein level was detected by Western-blot.
Results :

After OVA-sensitization, IgE, IL-4 and STAT6 levels were higher, INF-γ and STAT4 level was lower. Flow cytometry revealed a decrease in Th1 cell and an increase in Th2 cell in AR group. After BMSCs treatment, t IgE, IL-4 and STAT6 levels in SCRg and MRg were lower than that in AR group, and tINF-γ and STAT4 level were higher than hat inAR group. Flow cytometry showed that the content of Th1 cell increased while Th2 cell decreased.
Conclusions:

BMSCs return treatment could decrease the expression of IL-4, promote the expression of INF-γ and regulate the balance of Th cell, and the mechanism was closely related to STAT4/6 signaling pathway. However there was no statistical difference between SCRg and MRg, so the role of BMSCs maybe achieved through paracrine function rather than multi-directional differentiation potential.
Searching for target-specific and multi-targeting organics for Covid-19 in the Drugbank database with a double scoring approach
Authors: Arul Murugan Natarajan , Kumar Sanjiv , Jeyakanthan Jeyaraman , Srivas Vaibhav 
Published: June 2020

Abstract
The current outbreak of Covid-19 infection due to SARS-CoV-2, a virus from the coronavirus family, has become a major threat to human healthcare. The virus has already infected more than 5 M people and the number of deaths reported has reached more than 330 K which may be attributed to lack of medicine. The traditional drug discovery approach involves many years of rigorous research and development and demands for a huge investment which cannot be adopted for the ongoing pandemic infection. Rather we need a swift and cost-effective approach to inhibit and control the viral infection. With the help of computational screening approaches and by choosing appropriate chemical space, it is possible to identify lead drug-like compounds for Covid-19. In this study, we have used the Drugbank database to screen compounds against the most important viral targets namely 3C-like protease (3CLpro), papain-like protease (PLpro), RNA-dependent RNA polymerase (RdRp) and the spike (S) protein. These targets play a major role in the replication/transcription and host cell recognition, therefore, are vital for the viral reproduction and spread of infection. As the structure based computational screening approaches are more reliable, we used the crystal structures for 3C-like main protease and spike protein. For the remaining targets, we used the structures based on homology modeling. Further, we employed two scoring methods based on binding free energies implemented in AutoDock Vina and molecular mechanics - Generalized Born surface area approach. Based on these results, we propose drug cocktails active against the three viral targets namely 3CL-pro, PLpro and RdRp. Interestingly, one of the identified compounds in this study i.e. Baloxavir marboxil has been under clinical trial for the treatment of Covid-19 infection. In addition, we identified a few compounds such as phthalocyanine, Tadalafil, Lonafarnib, Nilotinib, Dihydroergotamine, R-428 which can bind to all three targets simultaneously and can serve as multi-targeting drugs. Our study also included calculation of binding energies for various compounds currently under drug trials. Among these compounds, it is found that Remdesivir binds to targets, 3CLpro and RdRp with high binding affinity. Moreover, Baricitinib and Umifenovir were found to have superior target-specific binding while Darunavir is found to be a potential multi-targeting drug. As far as we know this is the first study where the compounds from the Drug-bank database are screened against four vital targets of SARS-CoV-2 and illustrates that the computational screening using a double scoring approach can yield potential drug-like compounds against Covid-19 infection.
Fast Deep Learning Computer-Aided Diagnosis against the Novel COVID-19 pandemic from Digital Chest X-ray Images
Authors: Al-antari Mugahed A. , Hua Cam-Hao , Lee Sungyoung 
Published: June 2020

Abstract
Background and Objective: The novel coronavirus 2019 (COVID-19) is a harmful lung disease that rapidly attacks people worldwide. At the end of 2019, COVID-19 was discovered as mysterious lung disease in Wuhan, Hubei province of China. World health organization (WHO) declared the coronavirus outbreak a pandemic in the second week of March 2020. Simultaneous deep learning detection and classification of COVID-19 from the entire digital X-ray images is the key to efficiently assist patients and physicians for a fast and accurate diagnosis.Methods: In this paper, a deep learning computer-aided diagnosis (CAD) based on the YOLO predictor is proposed to simultaneously detect and diagnose COVID-19 among the other eight lung diseases: Atelectasis, Infiltration, Pneumothorax, Mass, Effusion, Pneumonia, Cardiomegaly, and Nodule. The proposed CAD system is assessed via five-fold tests for multi-class prediction problem using two different databases of chest X-ray images: COVID-19 and ChestX-ray8. The proposed CAD system is trained using an annotated training set of 50,490 chest X-ray images.Results: The suspicious regions of COVID-19 from the entire X-ray images are simultaneously detected and classified end-to-end via the proposed CAD predictor achieving overall detection and classification accuracies of 96.31% and 97.40%, respectively. The most testing images of COVID-19 and other lunge diseases are correctly predicted achieving intersection over union (IoU) with their GTs greater than 90%. Applying deep learning regularizers of data balancing and augmentation improve the diagnostic performance by 6.64% and 12.17% in terms of overall accuracy and F1-score, respectively. Meanwhile, the proposed CAD system presents its feasibility to diagnose the individual chest X-ray image within 0.009 second. Thus, the presented CAD system could predict 108 frames/second (FPS) at the real-time of prediction.Conclusion: The proposed deep learning CAD system shows its capability and reliability to achieve promising COVID-19 diagnostic performance among all other lung diseases. The proposed deep learning model seems reliable to assist health care systems, patients, and physicians in their practical validations.
COVID-19 and Non-Pharmaceutical Interventions: The Greek Paradigm
Authors: Mavragani Amaryllis , Gillas Konstantinos 
Published: June 2020

Abstract
Due to the COVID-19 pandemic originating in China in December 2019, apart from the grave concerns on the exponentially increasing casualties, the affected countries are called to deal with severe repercussions in all aspects of everyday life; from economic recession to national and international movement restrictions. Several regions managed to handle the pandemic more successfully than others in terms of life loss, while ongoing heated debates as to the right course of action for battling COVID-19 have divided the academic community as well as public opinion. To this direction, in this paper, an autoregressive COVID-19 prediction model with heterogeneous explanatory variables is proposed for Greece, taking past COVID-19 data, non-pharmaceutical interventions (NPIs), and Google query data as independent variables, from the day of the first confirmed case -February 26th- to the day before the announcement for the quarantine measures’ softening -April 24th. The analysis indicates that the early measures taken by the Greek officials positively affected the flattening of the curve, with Greece having recorded significantly decreased COVID-19 casualties compared to other countries. In specific, the prediction model identifies not only the seven-day lag that is needed in order for the measures’ results to actually show, that is, in sense, the optimal time-intervention framework for managing the disease’s spread, but the appropriate point during the disease spread where restrictive measures should be applied. Present results have significant implications for effective policy making and in the designing of the NPIs, as the second wave of COVID-19 is expected in fall 2020, and such multidisciplinary analyses are crucial in order to understand the evolution of the Daily Deaths to Daily Cases ratio along with its determinants as soon as possible, for the assessment of the respective domestic health authorities’ policy interventions as well as for the timely health resources allocation.
Machine learning prediction for mortality of patients diagnosed with COVID-19: a nationwide Korean cohort study
Authors: An Chansik , Lim Hyunsun , Kim Dong-Wook , Chang Jung Hyun , Choi Yoon Jung , Kim Seong Woo 
Published: June 2020

Abstract
The rapid spread of COVID-19 is likely to result in the shortage of medical resources, which necessitates accurate prognosis prediction to triage patients effectively. This study used the nationwide cohort of South Korea to develop a machine learning model to predict prognosis based on sociodemographic and medical information. Of 10,237 COVID-19 patients, 228 (2.2%) died, 7,772 (75.9%) recovered, and 2,237 (21.9%) were still in isolation or being treated at the last follow-up (April 16, 2020). The Cox proportional hazards regression analysis revealed that age > 70, male sex, moderate or severe disability, the presence of symptoms, nursing home residence, and comorbidities of diabetes mellitus (DM), chronic lung disease, or asthma were significantly associated with increased risk of mortality (p ≤ 0.047). For machine learning, the least absolute shrinkage and selection operator (LASSO), linear support vector machine (SVM), SVM with radial basis function kernel, random forest (RF), and k-nearest neighbors were tested. In prediction of mortality, LASSO and linear SVM demonstrated high sensitivities (90.3% [95% confidence interval: 83.3, 97.3]and 92.0% [85.9, 98.1], respectively) and specificities (91.4% [90.3, 92.5] and 91.8%, [90.7, 92.9], respectively) while maintaining high specificities >90%. The most significant predictors for LASSO included old age and preexisting DM or cancer; for RF they were old age, infection route (at large clusters or from personal contact with an infected individual), and underlying hypertension. The proposed prediction model may be helpful for the quick triage of patients without having to wait for the results of additional tests such as laboratory or radiologic studies, during a pandemic when limited medical resources have to be wisely allocated without hesitation.
Association between polymorphisms (rs1800896, rs1800872, rs1800871) in IL-10 gene with human immunodeficiency virus 1-infected patients’ susceptibility
Authors: Tai Huiyu , Xiao Li , Zhou Daming , Mi Yuanyuan , Zhang Wei , Yang Song 
Published: June 2020

Abstract
Background

Interleukin-10 (IL-10) is considered to play a role in the human immunodeficiency virus-1 (HIV-1) infections, three common single nucleotide polymorphisms (SNPs), perhaps have an association with HIV-1 risk.
Methods

To find out the above doubt, a comprehensive study to evaluate the association between IL-10 polymorphisms and HIV-1 has been performed. PubMed database was chosen to identify potential articles through Feb 21, 2020. The odd ratio with 95% confidence interval (OR with 95%CI) was applied to evaluate the relationships.
Results

There have 15 different articles including 24 case-control studies including those on three different SNPs in IL-10 for HIV-1 were found. Positive increased or damaged associations were detected for rs1800872C/A with HIV-1 risk in total (such as: OR with 95%CI = 1.22 with 1.02–1.41 for A-allele vs. C-allele model), and Asians (such as: OR with 95%CI = 1.51 with 1.07–1.88 for AA vs. CC model), population-based, PCR-RFLP, sequence subgroups. Moreover, increased associations were also detected between rs1800871C/T SNP (OR with 95%CI = 1.47 with 1.08-2.00 for CC vs. CT + TT model). No association was found for rs1800896.
Conclusion

Our current study indicated that two SNPs (rs1800872C/A and rs1800871C/T) in IL-10 perhaps be potentially associated with the susceptibility for developing HIV-1 infection.