bluuebunny/crossref_metadata_2025_split huggingface dataset:
Network evaluation of an innovation platform in continuous quality improvement in Australian Indigenous primary health care
Authors: Cunningham Frances Clare , Potts Boyd Alexander , Ramanathan Shanthi Ann , Bailie Jodie , Bainbridge Roxanne Gwendalyn , Searles Andrew , Laycock Alison Frances , Bailie Ross Stewart 
Published: June 2022

Abstract
Background
From 2014–2019, the Centre of Research Excellence in Integrated Quality Improvement (CRE-IQI) was evaluated as an innovation platform focusing on continuous quality improvement in Indigenous Australian primary health care. Although social network analysis (SNA) is a recognised method for evaluating the functioning, collaboration and effectiveness of innovation platforms, applied research is limited. This study applies SNA to evaluate the CRE-IQI’s functioning as an innovation platform.
Methods
Two surveys (2017, 2019) were conducted using social survey and network methods. Survey items covered respondent characteristics, their perceptions of the CRE-IQI’s performance, its impact and sociometric relationships. Members’ relationship information was captured for the CRE-IQI at three timepoints: start (retrospectively), mid-term, and final year on three network types (knew, shared information, collaborated). SNA software was used to compute standard network metrics such as diameter, density and centrality, and develop visualisations. Survey and network results were workshopped by members to develop improvement strategies.
Results
The response rate was 80% in 2017 and 65% in 2019, n = 49 and 47 respectively. Between 2017 and 2019 respondents’ mean ratings of the CRE-IQI’s functioning and achievements in meeting its goals increased. They perceived the CRE-IQI as multi-disciplinary, having effective management and governance, and incorporating Indigenous research leadership, representation and ways of working. Respondents recognised high levels of trust amongst members, rated ‘good communication and coordination with participants’ highly, and ‘facilitating collaboration’ as the CRE’s most strongly recognised achievement. In collaboration and information sharing networks, average path length remained low in 2017 and 2019, indicating good small-world network properties for relaying information. On average, respondents shared information, and collaborated, with more CRE members in 2017 than 2019. However, in both 2017 and 2019 there were new collaborations and information sharing outside of direct collaborations. CRE-IQI outcomes included: evidence generation; knowledge transfer and skills development in quality improvement; research capacity building, career development; mentoring; grant support; development of new projects; health service support; and policy impact.
Conclusion
This study shows the utility of network analysis in evaluating the functioning, and collaboration, at individual, organisational and health system levels, of an innovation platform, and adds to our understanding of factors enabling successful innovation platforms.
14-3-3ζ mediates GABAAR activation by interacting with BIG1
Authors: Li Cuixian , Huang Shen , Peng Jin , Hong Tianguo , Zhou Chun , Tang Jie 
Published: June 2022

Abstract
Most fast synaptic inhibitions in the mammalian brain are mediated by GABAA receptors (GABAARs). An appropriate level of GABAAR expression at the cell surface is essential for neurodevelopment and the efficacy of GABAergic synaptic transmission. We previously reported that brefeldin A-inhibited GDP/GTP exchange factor 1 (BIG1), a binding partner of GABAARs, plays an important role in trafficking GABAARs to the cell surface. However, its regulatory mechanisms remain unknown. In the present study, we identified a new cellular protein, 14-3-3ζ, which can interact with the β subunit of GABAARs and BIG1 both in vitro and in vivo and colocalizes in the soma, dendrites and axons of hippocampal neurons. Overexpression of 14-3-3ζ-WT increased the surface expression of BIG1 in dendrites and axons, as well as the binding of BIG1 with GABAAR. Depleted 14-3-3ζ with efficacious siRNA attenuated the interaction between BIG1 and GABAARs and resulted in significant decreases in the surface expression levels of BIG1 and GABAAR. GABAAR agonist treatment increased the expression levels of BIG1 and 14-3-3ζ on the surface, indicating that 14-3-3ζ is involved in regulating BIG1-mediated GABAAR surface expression. Depletion of BIG1 or 14-3-3ζ significantly decreased GABAAR expression at the cell surface and suppressed the GABA-gated influx of chloride ions. These data indicate that the combination of 14-3-3ζ and BIG1 is required for GABAAR membrane expression. Our results provide a potential promising therapeutic target for neurological disorders involving GABAergic synaptic transmission.
Blocking P2RX7 attenuates ferroptosis in endothelium and reduces HG-induced hemorrhagic transformation after MCAO by inhibiting ERK1/2 and P53 signaling pathways
Authors: Liu Chengli , Tian Qi , Wang Jianfeng , He Peibang , Han Shoumeng , Guo Yujia , Yang Chen , Wang Guijun , Wei Heng , Li Mingchang 
Published: June 2022

Abstract
Background
Hyperglycemia is a risk factor for poor prognosis after acute ischemic stroke and promote the occurrence of hemorrhagic transformation (HT). The activation of P2RX7 play an important role in endotheliocyte damage and BBB disruption. Ferroptosis is a novel pattern of programmed cell death caused by the accumulation of intracellular iron and lipid peroxidation, resulting in ROS production and cell death. This study is to explore the mechanism of P2RX7 could reduce HT pathogenesis after acute ischemic stroke through regulating endotheliocyte ferroptosis.
Methods
Male SD rats were performed to establish middle cerebral artery occlusion (MCAO) model injected with 50% high glucose (HG) and HUVECs were subjected to OGD/R treated with high glucose (30mM) for establishing HT model in vivo and in vitro. P2RX7 inhibitor (BBG) and P2RX7 small interfering RNAs (siRNA) were used to investigate the role of P2RX7 in BBB after MCAO in vivo and OGD/R in vitro, respectively. The neurological deficits, infarct volume, degree of intracranial hemorrhage, integrity of the BBB, immunoblotting and immunofluorescence were evaluated both 24 h after MCAO.
Results
Our study found that the level of P2RX7 was gradually increased after MCAO and/or treated with HG. Our results showed that treatment with HG after MCAO can aggravate neurological deficits, infarct volume, oxidative stress, iron accumulation and integrity of the BBB in HT model, and HG-induced HUVECs damage. The inhibition of P2RX7 reversed the damage effect of HG, significantly downregulated the expression level of P53, HO-1 and p-ERK1/2 and upregulated the level of SLC7A11 and GPX4, which implicated that P2RX7 inhibition attenuated oxidative stress and ferroptosis of endothelium in vivo and in vitro.
Conclusion
Our data provide evidence that the P2RX7 play an important role in HG-associated oxidative stress, endothelial damage and BBB disruption, which regulates HG-induced HT by ERK1/2 and P53 signaling pathways after MCAO.
The detection of four major clades and new species from China with elongated cheilocystidia: A contribution to the genus Mallocybe (Inocybaceae, Agaricales)
Authors: Hu Jian-hua , Yu Wen-jie , Deng Lun-Sha , Fan Yu-Guang , Bau Tolgor , Lin Wen-Fei 
Published: June 2022

Abstract
Mallocybe is a newly elevated genus of the ectomycorrhizal mushroom family, Inocybaceae, based on Inocybe subg. Mallocybe. Although new species in this genus have constantly been discovered worldwide during the past decades, species native to China is poorly documented. Moreover, as a characteristic feature, members of this genus often have phaseoliform basidiospores and short catenated cheilocystidia. Therefore, this study describes three new Chinese Mallocybe species with elongated cheilocystidia through morphological and molecular analyses. Of the identified species, while M. pallidipes was collected from Populus in northeastern China, M. aurantiodisca was discovered in subtropical evergreen broad-leaved forests dominated by Castanopsis in the Zhejiang Province, and M. longsicystis was discovered under fagaceous forests in tropical and subtropical China. From the multi-gene phylogenetic analysis, four major clades were also elucidated in the genus, of which three new species were grouped with three North American species in a full support clade. Finally, we discussed the systematic position of the new species and the genus's infrageneric phylogeny.
Abnormal aortic flow related to left ventricular mass and volume in coarctation: a four-dimensional flow CMR study
Authors: Warmerdam Evangeline Gerdine , Sotelo Julio , Driesen Bart W. , Uribe Sergio , Westenberg J. J.M , Leiner Tim , Krings Gregor J. , Molenschot Mirella C. , Zwaan Heleen B. van der , Voskuil Michiel , Grotenhuis Heynric B. 
Published: June 2022

Abstract
Background After successful repair for coarctation of the aorta (CoA) abnormal blood flow patterns can be present, which may have a negative impact on left ventricular (LV) performance. The aim of this study is to investigate the relationship between aortic haemodynamics assessed by four-dimensional flow (4D flow) cardiac magnetic resonance imaging (CMR) and LV performance in CoA patients. Methods CoA patients with a clinical indication for CMR underwent additional 4D flow acquisition. Wall shear stress, oscillatory shear index, vorticity, viscous dissipation, energy loss, kinetic energy and velocity angle were assessed for several aortic segments at peak-systole. Global pulse wave velocity (PWV) was assessed for the entire aorta. Results In total, 17 CoA patients were included. Increased velocity angle in the ascending aorta was correlated with decreased LV mass (p=0.0445). Energy loss at the CoA site and increased degree of vorticity in the descending aorta were correlated with increased LV end-diastolic and end-systolic volume (p=0.0499, p=0.0461, respectively). Aortic PWV was correlated with LV mass and LV end-systolic volume (p=0.0385, p=0.0397, respectively). Conclusion Adverse aortic-ventricular interaction in CoA patients was demonstrated by abnormal aortic haemodynamics by 4D flow assessment which were associated impaired LV performance. 4D flow CMR may provide novel parameters to assess aortic-ventricular interaction.
Formulation and Development of a Curcumin-Piperine Loaded Nanoemulsion for the Treatment of Alzheimer’s Disease
Authors: Ahmad Shmmon , Hafeez Abdul 
Published: June 2022

Abstract
Background and Objective:
Curcumin (Cur) and piperine (Pip) are very well-known phytochemicals that claimed to have many health benefits and have been widely used in foods and traditional medicines. This study investigated the therapeutic efficacy of these compounds to treat Alzheimer’s disease (AD). However, poor oral bioavailability and permeability of curcumin are a major challenge for formulation scientists. In this research study, the researcher tried to enhance the bioavailability & permeability of curcumin by a nanotechnological approach.
Materials and Methods
In this research study, we developed a Cur-Pip-loaded SNEDDS in various oils. Optimized formulation NF3 was subjected to evaluate its therapeutic effectiveness on AD animal model in comparison with untreated AD model and treated group (by market formulation donepezil).
Results
On the basis of characterization results it is confirmed that NF3 formulation is the best formulation. The optimized formulation shows a significant dose-dependent manner therapeutic effect on AD-induced model
Conclusion
Novel formulation Cur-Pip Solid-SNEDDS was successfully developed and optimized. It’s expected developed S-SNEDDS can be a potential, safe and effective carrier for the oral delivery of curcumin to the brain. This research article first reported the bio-active S-SNEDDS of Cur-Pip effect on Alzheimer's.
Integrated Analysis of the Oncogenic Value of CPNE1 and CPNE3 in Non-Small Cell Lung Cancer
Authors: Du Wenwen , Pang Pei , Chen Cheng , Zhu Jianjie , Zeng Yuanyuan , Zhang Sheng , Liu Zeyi , Huang Jian-an 
Published: June 2022

Abstract
Background
Copines (CPNEs) are calcium-dependent, soluble and ubiquitous proteins found in eukaryotic organisms and protists. Recent evidence has also highlighted the roles of these protein play in regulating cancer immune responses and development. However, the specific expression, distribution and roles of CPNEs in NSCLC are poorly studied.
Methods
GSE and GEPIA2 database were selected to explore the expression levels of CPNEs in lung cancer tissues, which were verified by qRT-PCR analysis. The Kaplan-Meier plotter and GEPIA database were used to assess the effect of CPNE expression on patient survival. cBioportal database was used for genetic mutation analysis. FunRich database were used to predict the potential biological pathway. Finally, ssGSEA database was performed for immune infiltration analysis.
Results
Among all CPNEs, high expression of CPNE1 and CPNE3 were valuable for predicting both OS and DFS in lung cancer patients, which were associated with advanced TNM stages, lymph node metastasis and drug resistance to EGFR-TKIs. However, no statistical significance between CPNEs and the efficiency to immunotherapy was observed, which was further validated by weak correlation between CPNEs expression and immune cell infiltration levels. The processes of regulation of nucleobase and nucleotide, signal transduction, cell metabolism were involved in the effect of CPNE1 and CPNE3 on tumor pathogenesis. In terms of biological pathways, TGF-beta pathway, PI3K/AKT pathway, mTOR pathway and EGFR signaling pathway were most relevant to CPNE1 and CPNE3.
Conclusions
We highlighted the protumor role of CPNE1 and CPNE3 among all CPNEs, which may provide important therapeutic targets for lung cancer.
CNN-CardioAssistant: Deep Convolutional Neural Network and Recursive Feature Elimination Method for Heart Disease Detection
Authors: HASSAN FARMAN , Rahman Auliya Ur , Javed Ali , Alhazmi Ali , Alhazmi Majed 
Published: June 2022

Abstract

In recent times, we have seen an exponential rise in different chronic diseases due to our unhealthy lifestyles. Cardio disease is the most common and life-threatening among all diseases, which contributes to a very high mortality rate. Accurate detection of cardio disease at an early stage is vital to save the lives of people. Most of the existing cardiovascular disease detection systems suffer from lower performance and efficiency due to redundant attributes, dimensionality curse, imbalance, and noisy datasets. In this work, we proposed a novel convolutional neural networks-based system (CNN-cardioAssistant) that predicts cardiovascular disease in patients. Recursive feature selection (RFE) is employed to select more prominent features from the clinical data of cardio patients. The selected features are then used to train the proposed CNN-CardioAssistant as well as 11 different classifiers i.e., support vector machine (SVM), Random Forest, Decision Tree, logistic regression, Naïve Bayes, K-Nearest Neighbor, XGboost, Multi-layer Perceptron, Gaussian process classifier, Adaboost, and Quadratic discriminant analysis separately for cardio disease prediction. We compared the results of all the methods on three subsets of features i.e., 6,8, and 15 for each dataset. The features selection method provides optimal subsets of features that can reliably be used to predict cardiovascular disease with the highest accuracy. Experimental results on three different cardio datasets i.e., Public Health, Framingham, and Z-Alizadeh sani clearly demonstrate that the proposed CNN-CardioAssistant system has superior performance against the existing state-of-the-art methods.
Epidemiological characteristics of notifiable respiratory infectious diseases in mainland China from 2010 to 2019
Authors: Deng Lele , Han Yajun , Wang Jinlong , Liu Haican , Li Guilian , He Guangxue , Wang Dayan 
Published: June 2022

Abstract
Background: Respiratory infectious diseases (RIDs) pose threatens to people’s health, some of them are serious public health problems. The aim of our study was to explore epidemic situations of all notifiable RIDs and the epidemiological characteristics of six most common RIDs in mainland China.Methods: We first collected the surveillance data of all 12 statutory notifiable RIDs for 31 provinces in mainland China that reported between January 1, 2010 and December 31, 2019, and then six most prevalent RIDs were chosen to analyze their spatial, temporal, seasonal and population distribution characteristics. Descriptive analysis was used to present the spatial and population distribution characteristics. Joinpoint regression model was used to explore the temporal trends and seasonal decomposition was used to explore seasonal characteristics.Results: From 2010 to 2019, there were 18 746 367 notifiable cases and 28 816 death cases of RIDs in mainland China. The incidence rate of RIDs increased from 109.85/100 000 in 2010 to 340.94/100 000 in 2019. The mortality of RIDs decreased from 0.24/100 000 in 2010 to 0.23/100 000 in 2019. The six most common RIDs were pulmonary tuberculosis (PTB), seasonal influenza, mumps, pertussis, measles and rubella. From 2010 to 2018, the incidence rates showed decreases in PTB and rubella, increases in pertussis and seasonal influenza, and irregular changes in measles and mumps. The mortality showed an increase in PTB from 2015 to 2018 and irregular change in seasonal influenza. PTB was mainly prevalent among people aged 15 years old and over, while the other five common RIDs mostly occurred among people younger than 15 years old. The incidence of the six common RIDs mostly occurred in winter and spring. The incidence rate of PTB was higher in the northwestern and southwestern China. The incidence rates of pertussis, measles and rubella were relatively lower in whole country.Conclusions: PTB, seasonal influenza and mumps remain the public health problems in China, suggesting that continuous government input, more precise and effective interventions, and a digital/intelligent surveillance and warning system are required to improve RIDs prevention and control efforts.
Schlieren Image Velocimetry of Swirl Sprays
Authors: Machado Danilo Almeida , Costa Fernando Souza , Andrade José Carlos , Dias Gabriel Silva , Fischer Gustavo Alexandre Achilles 
Published: June 2022

Abstract
Schlieren image velocimetry is based on light deflection through flow heterogeneities and image cross-correlations. This is a low-cost and relatively low complexity technique that allows measurement of the droplet velocity field in a large region of a spray. A Z-type Toepler schlieren system with a high-speed camera was used to determine mean vertical and horizontal droplet velocities, as well as the cone angles of sprays produced by a pressure swirl injector with characteristic geometric constant K = 2. Different LEDs and digital filters were evaluated for edge detection and improvement of image contrast. Open software was adopted for digital image processing and velocimetry. Interrogation windows and overlaps of different sizes were tested to obtain an appropriate correlation for determination of the velocity field. The digital images were obtained with 5×103 fps and a resolution of 2.77 pixels/mm. Since the swirl sprays analysed presented instabilities, a number of 100 cross-correlations of images was required to reduce mean velocity fluctuations. Injection pressures varied from 0.05 to 7 bar and mass flow rates varied from 1.389 to 13.89 g/s, using water as test fluid. The wideband warm white LED with Laplacian or high-pass filters provided velocity data for a larger range of injection pressures. Mean axial velocities varied from 3.3 to 11.3 m/s, approximately, with mean horizontal velocities varying from around 0.17 to 3.3 m/s for pressures from 0.05 to 3.22 bar. The velocity data were compared to microscopic shadowgraphy results, showing a good agreement. Spray cone angles ranged from about 32.5o to 69.5o, for injection pressures from 0.05 to 7 bar, and results of triangulation with a blue LED were closer to semi-empirical data.
A heuristic-based package-aware function scheduling approach for creating a trade-off between cold-start time and cost in FaaS computing environments
Authors: Ebrahimpour Hossein , Ashtiani Mehrdad , Bakhtiariazad Ghazaleh 
Published: June 2022

Abstract
With the migration of the enterprise applications to micro-services and containers, cloud service providers, starting with Amazon in 2014, announced a new computational model called function-as-a-service. In these platforms, developers create a set of fine-grained functions with shorter execution times instead of developing coarse-grained software. In addition, the management of system resources and servers is entrusted to cloud service providers. This model has many benefits, such as reducing costs, but still faces many challenges such as balancing cost, balancing performance, programming models, using current dev tools, containers’ cold start problem, saving data in caches, security issues, privacy concerns, and scheduling challenges like execution time prediction. In this paper, we focus on scheduling and cold start problems. Compromise occurs when keeping warm operating environments which can reduce cold start times but increase costs. Here, we aim to create a trade-off by using four different types of decisions at runtime using a heuristic method and analyzing the function dependency graph, functions’ invocation frequency, and other environmental parameters. The proposed method shows a 32% improvement over the fixed-time method (i.e. the method used by Amazon). This comparison is made from the cumulative measurement viewpoint which is a combination of response time, turnaround time, cost, and utilization.
Nutrient sequestration and lipid production potential of Chlorella vulgaris under pharmaceutical wastewater treatment: Experimental, optimization, and prediction modeling studies
Authors: Kumari Sonika , Kumar Vinod , Kothari Richa , Kumar Pankaj 
Published: June 2022

Abstract
The efficient management and treatment of pharmaceutical industry wastewater (PIWW) have become a serious environmental issue due to its high toxicity.To overcome this problem, the present study deals with the phycoremediation of PIWW using Chlorella vulgaris microalga isolated from the Ganga River at Haridwar, India. For this, response surface methodology (RSM) and artificial neural network (ANN) tools were used to identify the best reduction of total phosphorus (TP) and total Kjeldahl’s nitrogen (TKN) based pollutants along with lipid production efficiency of C. vulgaris. Three different concentrations of pharmaceutical wastewater (0, 50, and 100%), operating temperatures (20, 25, and 30°C), and light intensity (2000, 3000, and 4000 lx)were used to design the phycoremediation experiments. Findings revealed thatC.vulgaris was good enough to remove maximum TP (90.35%), TKN (83.55%) along 20.88% of lipid yield at 25.62℃ temperature, 60.73% PIW concentration, and 4000 lx of light intensity, respectively.Based on the model performance and validation results, ANN showed more accuracy as compared to the RSM tool. Therefore, the findings of this study showed thatC.vulgarisis capable of treating PIWW efficiently along with significant production of lipid content which can further be used in various applications including biofuels production.