bluuebunny/crossref_metadata_2025_split huggingface dataset:
Influences of Acid Catalysts on The Microstructure, Bioactivity and Cytotoxicity of Bioactive Glass Nanoparticles Prepared by Spray Pyrolysis
Authors: Kuo Chao-Kuang , Chen Liu-Gu , Tseng Chun-Fu , Chou Yu-Jen 
Published: September 2020

Abstract
Bioactive glasses have received considerable attention in the fields of medical and material science and have been applied in applications such as bone implants, tooth fillings, and drug carriers due to their high bioactivity, biocompatibility and biodegradability. Numerous studies applying either conventional glass processes or the sol-gel method have employed Hench's protocol for the fabrication of bioactive glass. However, the effects of various acid catalysts when using spray pyrolysis to synthesize bioactive glass remain unclear. Therefore, in this study, we synthesized bioactive glass nanoparticles using spray pyrolysis and then treated them with the acid catalysts hydrochloric acid, lactic acid and acetic acid. By characterizing the phase information and morphologies of the bioactive glass particles and examining their bioactivity and cytotoxicity, we found that the bioactive glass treated with hydrochloric acid yielded greater cell viability than the lactic acid- and acetic acid-treated specimens; the corresponding mechanisms are discussed in this paper.
Rapid Ethnographic Assessment for Potential Anti-Malarial Mass Drug Administration in an Outbreak Area of Santo Domingo, Dominican Republic
Authors: Keys Hunter , Ureña Keyla , Reyes Jhefres , Bardosh Kevin , Pell Christopher , Puello Jose , Blount Stephen , Noland Gregory S. 
Published: September 2020

Abstract
Background: In the Dominican Republic, a recent outbreak of malaria in the capital, Santo Domingo, threatens efforts to eliminate the disease. Mass drug administration (MDA) has been proposed as one strategy to reduce transmission. The success of MDA is contingent upon high levels of acceptance among the target population. To inform the design of future MDA campaigns, this rapid ethnographic assessment examined malaria-related knowledge and attitudes toward malaria MDA among residents of a transmission focus in Santo Domingo.Methods: In October 2019, a rapid ethnographic assessment was conducted in the Los Tres Brazos transmission focus, which had not previously received MDA. National malaria program staff conducted 61 structured interviews with key informants, recorded observations, and held 72 informal conversations. Using a grounded theory approach, data were analyzed during three workshop sessions with research team members. Results: Among those who had heard of malaria in the structured interviews (n=39/61; 64%), understanding of the disease was largely based on personal experience from past outbreaks or through word-of-mouth. Community health workers (promotores) were trusted for health information and malaria diagnosis more so than professional clinicians. Neighborhood associations (juntas de vecinos) were seen as self-serving and politically corrupt. No participant (0%) was familiar with malaria MDA. After learning about MDA, almost all study participants (92%) said that they would participate, seeing it as a way to care for their community. Reasons for not participating in future MDA included not trusting drug administrators, feeling reluctant to take unprescribed medicine, and fear of missing work. Additional identified challenges to MDA included reaching specific demographic groups, disseminating effective MDA campaign messages, and managing misinformation and political influence. Conclusion: Residents appear accepting of MDA despite a lack of prior familiarity. Successful MDA will depend on several factors: fostering relationships among community-based health workers, clinicians, community leaders, and others; developing clear health messages that use local terms and spreading them through a variety of media and social networks; and contextualizing MDA as part of a broader effort to promote community health.
Is It Time to Recast India's Fiscal and Monetary Policy Frameworks?
Authors: Srivast Dinesh Kumar , Bharadwaj Muralikrishna , Kapur Tarrung , Trehan Ragini 
Published: September 2020

Abstract
With the onset of the Coronavirus disease (COVID-19) pandemic, a number of macro parameters of the Indian economy have been thrown out of gear. The fiscal deficit on the combined account of centre and state governments in 2020-21 may increase to 11-12% of estimated GDP. Consequently, the combined debt-GDP ratio of the central and state government may reach close to 81% of GDP at the end of 2020-21, more than 20% points above the targeted threshold of 60% as per centre’s 2018 amendment to the Fiscal Responsibility and Budget Management Act (FRBMA). The CPI inflation rate breached the upper tolerance limit of the monetary policy framework (MPF) in the last quarter of 2019-20 and the first quarter of 2020-21. In fact, India’s economic crisis predates the pandemic. The infirmities of the FRBMA and the MPF had already started becoming visible with 2019-20 real and nominal GDP growth rates plummeting to 4.2% and 7.2% respectively. It is high time that we consider recasting India’s fiscal and monetary policy frameworks. In this article, we review these frameworks, identify their inconsistencies, and consider remedial changes so as to serve India’ future needs and compulsions.
Do Informal Sector Businesses Support Women’s Livelihood? A Case of Butwal Sub-Metropolitan in Nepal
Authors: Devkota Jeevanath , Devkota Devaka 
Published: September 2020

Abstract
Informal sector business is rapidly increasing in Nepal. This paper examines the effect of informal businesses on women’s livelihood, which is not documented in the literature. This study uses primary data collected from the Butwal sub-metropolitan, Nawalparasi District, Province 5 of Nepal. Sample size is 300 women entrepreneurs. There is a positive correlation between income level and married as well as migrated women entrepreneurs. Logistic Regression result shows more educated women entrepreneurs are more likely to earn from their businesses in comparison to the less educated women. Qualitative analysis shows women are learning business doing skills. Most important, old aged women (around/over 60 years) should not depend on their son or daughter for bread and butter and for pocket money after starting such business.
A rare case of chest wall lipoma growing into the pleural cavity: a case report
Authors: Jang Hyo Joon , Choi Bu Hyeon , Park Seong Oh 
Published: September 2020

Abstract
Background: Several cases of lipoma in unusual locations in the thorax have been reported. Appropriate surgical treatment depending on the location and shape is often required.Case presentation: We herein report an extremely rare case of a chest wall lipoma growing into the pleural cavity. The tumor was successfully removed without damaging the capsule by a combination of direct and thoracoscopic approaches.Conclusions: Chest wall lipomas growing into pleural cavity can be successfully treated by a combination of direct and thoracoscopic approaches.
Hybrid Stepwise External Wrapping for Type A Acute Aortic Dissection with Cerebral Malperfusion
Authors: Suematsu Yoshihiro , Nishi Satoshi , Arima Daisuke , Yoshimoto Akihiro 
Published: September 2020

Abstract
Background: Acute aortic dissection (AAD) is a life-threatening condition which can lead to coronary, brachiocephalic or branch vessel malperfusion, as well as aortic valve insufficiency, or aortic rupture. Mortality of surgical treatment in high-risk or elderly patients with Type A AAD (TAAAD) still remains high, and treatment for such patients remains controversial. We report a successful treatment of TAAAD with a communicating false lumen in a 60-year-old man with acute hemi-cerebral malperfusion. Case presentation: The ascending aorta was wrapped with stepwise external wrapping (SEW) procedure, and subsequent thoracic endovascular aortic repair (TEVAR) was successfully performed. The patient was discharged in good physical condition without any complications. Conclusions: Hybrid therapy with SEW and TEVAR with TAAAD associated with major cerebral malperfusion should be considered, especially in patients for whom open surgery is extremely risky.
Identification of the Neuropeptide Precursor Genes Potentially Involved in the Larval Settlement in the Echiuran Worm Urechis unicinctus
Authors: Hou Xitan , Qin Zhenkui , Wei Maokai , Fu Zhong , Liu Ruonan , Lu Li , Bai Shumiao , Ma Yubin , Zhang Zhifeng 
Published: September 2020

Abstract
Background: In marine invertebrate life cycles, which often consist of planktonic larval and benthonic adult stages, settlement of the free-swimming larva to the sea ﬂoor in response to environmental cues is a key life cycle transition. Settlement is regulated by a specialized sensory–neurosecretory system, the larval apical organ. The neuroendocrine mechanisms through which the apical organ transduces environmental cues into behavioral responses during settlement are not fully understood yet.Results: In this study, a total of 54 neuropeptide precursors (pNPs) were identified in the Urechis unicinctus larva and adult transcriptome databases using local BLAST and de novo prediction, of which 10 pNPs belonging to the ancient eumetazoa, 23 pNPs belonging to the ancient bilaterian, 3 pNPs belonging to the ancient protostome, 10 pNPs exclusive in lophotrochozoa, 3 pNPs exclusive in annelid, and 5 pNPs only found in U. unicinctus. Furthermore, four pNPs (MIP, FRWamide, FxFamide and FILamide) which may be associated with the settlement and metamorphosis of U. unicinctus larvae were verified successfully by qRT-PCR. Whole-mount in situ hybridization results showed that all the four pNPs were expressed in the region of the apical organ of the larva, and the positive signals were also detected in the ciliary band and abdomen chaetae. We speculated that these pNPs may regulate the movement of larval cilia and chaeta by sensing external attachment signals.Conclusions: This study represents the ﬁrst comprehensive identiﬁcation of neuropeptides in Echiura, and would contribute to a complete understanding on the roles of various neuropeptides in larval settlement of most marine benthonic invertebrates.
Kawasaki Disease Outbreak in Children During COVID-19 Pandemic.
Authors: Gowin Ewelina , Wysocki Jacek , Frydrychowicz Magdalena , Januszkiewicz-Lewandowska Danuta 
Published: September 2020

Abstract
BackgroundIn response to the recent information about the outbreak of Kawasaki disease (KD) in children connected to SARS-Cov-2 pandemic, we would like to present a group of six patients hospitalized from March to May 2020 with an inflammatory disease similar to KD. Findings There were four girls and two boys, aged from 15 months to 16 years. They all presented with fever lasting at least five days, irritability, bilateral nonexudative conjunctivitis, lymphadenopathy, mucus membrane changes, rash, edema.Neither the patients nor the other members of the patients' households had a positive history of COVID-19 infection. None of the six children had a positive PCR result for SARS-CoV-2 or a positive results for antibodies to SARS-CoV-2. All patients received empiric antibiotic therapy. Four patients were diagnosed with KD. Three children received standard treatment. One boy did not respond and received an additional 14-days course of methylprednisolone.In two girls, the diagnosis of KD was not made. All patients survived ConclusionFinding a correlation with the Covid-19 pandemic is difficult regarding the situation in our country. According to ECDC, in May 2020 Poland wass still before the peak of the epidemy. The intention of this article is to report that increased hospitalization of children with the inflammatory syndrome is also observed in countries with low levels of transmission of the SARS-Cov-2 virus. Our observation may broaden the knowledge of new inflammatory syndrome, which is not necessarily caused by SARS-Cov-2 but may be worsened by co-infection.
Sex and APOE Genotype Differences Related to Statin Use in The Aging Population
Authors: Dagliati Arianna , Peek Niels , Brinton Roberta Diaz , Geifman Nophar 
Published: September 2020

Abstract
Background. Significant evidence suggests that the cholesterol-lowering statins can effect cognitive function, and reduce the risk for Alzheimer’s disease and dementia. These potential effects may be constrained by specific combinations of an individual’s sex and Apolipoprotein E (APOE) genotype. Methods. Here we examine data from 252,327 UK BioBank participants, aged 55 or over, and compare the effects of statin use in males and females. We identified that in this population, males were older, had a higher level of education, better cognitive scores, higher incidence of cardiovascular and metabolic diseases, and a higher rate of statin use.Results. We observed that males and those participants with an APOE4 (E4 variant of APOE) positive genotype had higher probabilities of being treated with statins; while participants with an Alzheimer’s diagnosis had slightly lower probabilities. We found that use of statins was not significantly associated with overall higher rates of survival. However, when considering the interaction of statin use with sex, the results suggest higher survival rates in males treated with statins. Finally, examination of cognitive function indicates a potential beneficial effect of statins, however this is limited to APOE4 positive genotypes. Conclusions. Our evaluation of the ageing population in a large cohort from the UK BioBank confirms sex and APOE genotype as funda mental risk stratifiers for Alzheimer’s disease and cognitive function, furthermore it extends them to the specific area of statin use, clarifying their specific interactions with treatments.
Identification of a Seven-Gene Signature and Establishment of a Nomogram Predicting Overall Survival in Head and Neck Squamous Cell Carcinoma
Authors: Zheng Haige , Wu Xiangkun , Liu Huixian , Lu Yumin , Li Hengguo 
Published: September 2020

Abstract
Background: Head and neck squamous cell carcinoma (HNSCC) is a highly heterogeneous tumor with high incidence and poor prognosis. Therefore, effective predictive models are needed to evaluate patient outcomes and optimize treatment. Methods: Ten gene microarray datasets were obtained from the gene expression omnibus (GEO) database. Level 3 mRNA expression and clinical data were obtained in The Cancer Genome Atlas (TCGA) database. We identified highly robust differentially-expressed genes (DEGs) between HNSCC and normal tissue in nine GEO and TCGA datasets using Robust Rank Aggregation (RRA) method. Univariate Cox regression analysis and lasso Cox regression analysis were performed to identify DEGs related to the Overall-survival (OS) and to construct a prognostic gene signature. External validation was performed using GSE65858. Moreover, gene set enrichment analyses (GSEA) analysis was used to analyze significantly rich pathways in high-risk and low-risk groups, and tumor immunoassays were used to clarify immune correlation of the prognostic gene. Finally, integrate multiple forecast indicators were used to build a nomogram using the TCGA-HNSCC dataset. Kaplan–Meier analysis, receiver operating characteristic (ROC), a calibration plot, Harrell’s concordance index (C-index), and decision curve analysis (DCA) were used to test the predictive capability of the seven genetic signals and the nomogram. Results: A novel seven-gene signature (including SLURP1, SCARA5, CLDN10, MYH11, CXCL13, HLF, and ITGA3) was established to predict overall survival in HNSCC patients. ROC curve performed well in the training and validation data sets. Kaplan–Meier analysis demonstrated that low-risk groups had a longer survival time. The nomogram containing seven genetic markers and clinical prognostic factors was a good predictor of HNSCC survival and showed a certain net clinical benefit through the DCA curve. Further research demonstrated that the infiltration degree of CD8 + T cells, B cells, neutrophils, and NK cells were significantly lower in the high-risk group.Conclusion: Our analysis established a seven-gene model and nomogram to accurately predict the prognosis status of HNSCC patients, immune relevance was also described, which may provide a new possibility for individual treatment and medical decision-making.
Air-Steam Gasification of Nigerian Lignite Coals for Hydrogen and Syngas Production
Authors: Oladokun Olagoke , Nyakuma Bemgba B. 
Published: September 2020

Abstract
Coal is the fuel that drives most emerging economies. The gasification along with carbon capture and sequestration could ameliorate the harmful effects of coal utilisation. In this study, two recently discovered lignite coals; Obomkpa (BMK) and Ihioma (IHM) were examined for hydrogen and synthesis gas production through air-steam gasification using a non-stoichiometric model simulated in ASPEN Plus. The results showed that H2 production from BMK and IHM was highly sensitive to temperature and air but moderately sensitive to steam during gasification. The optimal operating conditions for BMK and IHM are; temperature 1125 °C and 1350 °C; equivalence ratio (ER) 0.26 and 0.26; steam/carbon ratio (S/C) 2.25 and 2.19, respectively. The optimal gas composition for BMK was; H2 (0.66), CO (0.18) and CO2 (0.18) mole-fraction, whereas for IHM was; H2 (0.65), CO (0.17) and CO2 (0.17). The findings indicate that both lignite coals are suitable feedstock for H2 and syngas production.
An Investigation of Machine Learning Algorithms on COVID-19 Dataset&nbsp;
Authors: Theerthagiri Prasannavenkatesan , Jacob I.Jeena , Ruby A.Usha , Y.Vamsidhar  
Published: September 2020

Abstract
This paper studies the different machine learning classification algorithms to predict the COVID-19 recovered and deceased cases. The k-fold cross-validation resampling technique is used to validate the prediction model. The prediction scores of each algorithm are evaluated with performance metrics such as prediction accuracy, precision, recall, mean square error, confusion matrix, and kappa score. For the given dataset, the k-nearest neighbour (KNN) classification algorithm produces 80.4 % of predication accuracy and 1.5 to 3.3 % of improved accuracy over other algorithms. The KNN algorithm predicts 92 % (true positive rate) of the deceased cases correctly with 0.077 % of misclassification. Further, the KNN algorithm produces the lowest error rate as 0.19 on the prediction of accurate COVID-19 cases than the other algorithm. Also, it produces the receiver operator characteristic curve with the output value of 82 %.
Power Transformer Fault Diagnosis System Based on Internet of Things
Authors: Wang Guoshi , Liu Ying , Chen Xiaowen , Yan Qing , Sui Haibin , Ma Chao , Zhang Junfei 
Published: September 2020

Abstract
Transformer is the most important equipment in the power system. The research and development of fault diagnosis technology for Internet of things equipment can effectively detect the operation status of equipment and eliminate hidden faults in time, which is conducive to reducing the incidence of accidents and improving people's life safety index. Objective: To explore the utility of Internet of things in power transformer fault diagnosis system. Methods: A total of 30 groups of transformer fault samples were selected, and 10 groups were randomly selected for network training, and the rest samples were used for testing. The matter-element extension mathematical model of power transformer fault diagnosis was established, and the correlation function was improved according to the characteristics of three ratio method. Each group of power transformer was diagnosed for four months continuously, and the monitoring data and diagnosis were recorded and analyzed result. GPRS communication network is used to complete the communication between data acquisition terminal and monitoring terminal. According to the parameters of the database, the working state of the equipment is set, and various sensors are controlled by the instrument driver module to complete the diagnosis of transformer fault system. Results: The detection success rate of the power transformer fault diagnosis system model established in this paper is as high as 95.6%, the training error is less than 0.000 l, and it can correctly identify the fault types of the non-training samples. It can be seen that the technical support of the Internet of things is helpful to the upgrading and maintenance of the power transformer fault diagnosis system.
Development and validation of a DNA methylation signature to predict the progression-free survival in patients with advanced-stage ovarian cancer
Authors: Fan Suzhen , Gao Xiang , Qin Qiaohong , Li Hongyu , Yuan Zhongfu , Zhao Shujun 
Published: September 2020

Abstract
Background: Ovarian cancer (OC) is a disease characterized by late-stage presentation and poor prognosis. Our aim was to identify a DNA methylation signature for predicting progression-free survival (PFS) of patients with advanced-stage OC.Methods: A bioinformatics analysis was performed to identify methylation sites that are relevant to PFS and develop a DNA methylation signature. A total of 501 patients with advanced-stage OC who were identified from the Cancer Genome Atlas (TCGA) were enrolled as a training cohort, and 108 patients with advanced-stage OC from GSE65820 were used as a validation cohort.Results: A DNA methylation signature was constructed on the basis of five methylation sites. We found that patients with OC in the training and validation cohorts could be stratified, based on the DNA methylation signature, into high- and low-risk groups with distinct prognosis. Different cancer-related pathways were enriched between these two groups. Finally, a nomogram that integrated the methylation signature risk score and clinical stage was established, and the nomogram performed well.Conclusions: The DNA methylation signature provides a promising prognostic biomarker for patients with advanced-stage OC and may help to optimize clinical management.