bluuebunny/crossref_metadata_2025_split huggingface dataset:
CLS, PLS and PCR Methods in Different Sets of Data for Simultaneous Determination of Paracetamol and Orphenadrine Citrate in Their Combined Pharmaceutical Dosage Forms
Authors: sebaiy mahmoud , Fawzy Michael , mattar amr , eladl sobhy 
Published: January 2022

Abstract
Three simple, specific, accurate and precise chemometric methods have been developed for the analysis of paracetamol and orphenadrine citrate in their mixture form without prior separation. CLS, PLS and PCR methods were used for simultaneous determination of paracetamol and orphenadrine citrate by using different sets of data in which better results were produced. The validity of these chemometrics has been carried out by using 8 synthetic mixtures for determination of the power of prediction for each method. Latent variable number is different from one model to another concurrent with changing the set of data. PRESS (predicted residual error sum of squares) and RMSEP (Root mean square error of prediction) were used for the comparison between different methods and for determination the predictive power of each set of data. Statistical comparison between the proposed chemometric methods was also performed.
The efficacy of deep learning models in the diagnosis of endometrial cancer using MRI: A comparison with radiologists
Authors: Urushibara Aiko , Saida Tsukasa , Mori Kensaku , Ishiguro Toshitaka , Inoue Kei , Satoh Toyomi , Masumoto Tomohiko , Nakajima Takahito 
Published: January 2022

Abstract
Purpose: To compare the diagnostic performance of deep learning models using convolutional neural networks (CNN) with that of radiologists in diagnosing endometrial cancer and to verify suitable imaging conditions. Methods: This retrospective study included patients with endometrial cancer or non-cancerous lesions who underwent MRI between 2015 and 2020. In Experiment 1, single and combined image sets of several sequences from 204 patients with cancer and 184 patients with non-cancerous lesions were used to train CNNs. Subsequently, testing was performed using 97 images from 51 patients with cancer and 46 patients with non-cancerous lesions. The test image sets were independently interpreted by three blinded radiologists. Experiment 2 investigated whether the addition of different types of images for training using single image sets improved the diagnostic performance of CNNs. Results: The AUC of the CNNs pertaining to single and combined image sets were 0.88–0.95 and 0.87-0.93, respectively, indicating better or equivalent diagnostic performance than the radiologists. The AUC of the CNNs trained with the addition of other types of images was 0.88–0.95. Conclusion: CNNs demonstrated high diagnostic performance for the diagnosis of endometrial cancer using MRI. Although there were no significant differences, adding other types of images improved the diagnostic performance for some image sets.
Restorative Biodegradable Two-layered Hybrid Microneedles for Melanoma Photothermal/Chemo Co-therapy and Wound Healing
Authors: Liao Jinfeng , Shan Yue , Tan Bowen , Zhang Min , Xie Xi 
Published: January 2022

Abstract
Tumor killing and wound healing are two complementary and influential processes during the treatment of melanoma. Herein, a two-layered microneedle platform was developed with bifunctional effect of chemo-photothermal synergistic melanoma therapy and skin regeneration. The platform composed of embeddable curcumin nanodrugs/new Indocyanine Green/hyaluronic acid (Cur NDs/IR820/HA) microneedles and sodium alginate/gelatin/hyaluronic acid (SA/Ge/HA) supporting back layer was prepared through a two-step casting process. With uniform incorporation of Cur NDs and IR820, the microneedles exhibited nice photothermal performance under external near-infrared (NIR) light stimulation and excellent tumor co-therapy ability. Once the embeddable microneedles insert into skin, they rapidly dissolve and activate drug release successfully for tumor treatment. Moreover, the SA/Ge/HA supporting back layer left behind to cover the wound and promote the proliferation of endothelial and fibroblasts cells for enhanced skin regeneration. The two-layered microneedles platform can simultaneously eliminate the tumor and accelerate wounding healing, which may be potentially employed as a competitive strategy for the treatment of melanoma.
Metoprolol Mitigates Ischemic Heart Remodeling and Fibrosis by Increasing the Expression of akap5 in Ischemic Heart
Authors: Zhu Feng , Wang Zhi , Zhang Benkai , Zhang Xue , Wang Hegui 
Published: January 2022

Abstract
Background The purpose of this study was to verify whether metoprolol regulates AKAP5 expression and test the role of AKAP5 post-injury in mitigating cardiac infarction-associated tissue remodeling and fibrosis.Methods and results Sprague-Dawley (SD) rats underwent coronary artery ligation (CAL), which was followed immediately with metoprolol daily. HW/BW ratio and cardiac expression of COL1 and COL3 were increased in rats following CAL compared with shams. Treatment with metoprolol post-injury was associated with a decrease in HW/BW ratio and COL1/COL3 expression compared to uncontrol rats. CAL resulted in decreased cardiac AKAP5 expression compared to the control group, while metoprolol treatment restored levels compared to baseline shams. Cardiac expression levels of NFATc3/p-NFATc3 and GATA4 were modest at baseline and increased with injury, whereas metoprolol suppressed gene expression to below injury-associated changes. Immunoprecipitation indicated that AKAP5 could bind and regulate PP2B. Conclusions The results indicate that metoprolol mitigates ischemic cardia remodeling and fibrosis, which mechanism of mitigating remodeling likely to improve cardiac AKAP5 expression and AKAP5-PP2B interaction.
Challenges in treating cancer in post-conflict Kosovo, a systematic review
Authors: Buleshkaj Shqipe Hyseni , Rugova Naser 
Published: January 2022

Abstract
Background: The direct and side effects of the impact of the conflict on the general health condition of the population and the health infrastructure are evidenced, measurable as well as fact-based as is the case of Kosovo. These findings are also confirmed by interviews with interviewed health professionals who address this issue. In these 22 years after the conflict and 13 years following the independence, the consequences of the conflict in the health system of Kosovo are obvious in all three levels of its organization. Cancer treatment and palliative care continue to be among the weakest links in Kosovo's health system. Case presentation: Since the end of the conflict there is a significant increase of cancer cases in Kosovo. This has proven to be a difficult extra burden for an already shattered health system. The main purpose of this study is to explore and analyze the available data on cancer treatment during this post-conflict period in Kosovo, including the causes, age and gender of those endangered by cancer, and measures taken by the Kosovo authorities to treat and contain these cases. Conclusions: The consequences of the conflict significantly complicate the enhancement and reforms in the health system of a country, especially in the areas of tertiary health care that require political will, sustainable and continuous funding, continuous education of health personnel, therapy based on modern protocols and advanced equipment, which the Kosovo health system and palliative care currently lack.
How are parental practices and attitudes towards corporal punishment related to child academic, developmental, or psychological-emotional dysfunctioning?
Authors: Grujicic Roberto , Toskovic Oliver , Lazarevic Ljiljana , Maravic Vanja Mandic , Voncina Marija Mitkovic , Radanovic Ana , Kircanski Jelena Radosavljev , Videnovic Marina , Pekmezovic Tatjana , Milovancevic Milica Pejovic 
Published: January 2022

Abstract
Corporal punishment (CP) is a widely spread disciplining technique among parents and caregivers globally. The aim of our paper was to explore the relationship between the parental attitudes towards CP, expected outcomes of CP, and parenting practices on one hand, with the reported dysfunctions of their children. In addition, we aimed to explore the relationship between the use of CP and the reported academic, developmental and psychological-emotional dysfunctions of their children. The present study involved a nationally representative sample of 1186 parents in Serbia, who had at least one child aged 0 to 18 years in the moment of interviewing. The parents filled out a series of interviews on their attitudes towards CP, expectations of CP outcomes and their parental practices. Our findings indicate that parents that report having a child with dysfunctions have positive attitudes towards CP and expect positive outcomes of CP. These parents also report using more CP as a disciplining method, as well as other harsh disciplining techniques. We also identified significant predictors of reported child dysfunctions. When these measured effects combine, this puts the children with health-related and school-related issues at a significant risk of further maltreatment and abuse.
HAPLN1, A Potential Promising Biomarker for Heart Failure
Authors: Xu Sichi , Chen Peipei , Xiao Ying , Wu Wei , Zhang Shuyang 
Published: January 2022

Abstract
Objective: Heart failure (HF) is the end pathophysiological process of many cardiovascular diseases. Although brain natriuretic peptide (BNP) and N-terminal pro B type natriuretic peptide, (NT-proBNP) have been widely used to diagnose HF, their expression levels are easily affected by age, weight, obesity, and renal function. Therefore, more accurate markers of HF are needed. This study aimed to explore common new biomarkers and mechanisms for HF based on microarray and bioinformatics analysis at the transcriptome level.Methods: Microarray datasets of HF were downloaded from the Gene Expression Omnibus (GEO) database and differentially expressed genes (DEGs) were selected by using R software. Tissue or organ-specific genes were predicted by BioGPS. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were used to conduct enrichment analysis. Hub genes were selected by a protein-protein interaction (PPI) network, and competitive endogenous RNA (ceRNA) networks were constructed by Cytoscape. The HF model was induced by aortic banding (AB) surgery. Furthermore, effective DEGs were further validated using reverse transcription-quantitative polymerase chain reaction (RT-PCR) analysis. Results: Three datasets were downloaded from the GEO. As many as 950 DEGs were recoded from GSE57338 and GSE141910, and 43 common DEGs were identified. DEGs were mostly enriched in extracellular matrix structural components, extracellular adhesion, oxygen binding and carrying, apoptosis, and phagosomes. Four hub genes were identified from the intersection of the PPI and BioGPS system. The diagnostic level of natriuretic peptide A (NPPA), haemoglobin subunit beta (HBB), and hyaluronan and proteoglycan link protein 1(HAPLN1) were validated by a GSE116250 dataset. SNHG16-miR-23-3p/miR-103-5p/HAPLN1 and XIST-miR-23-3p/ miR-103-5p/HAPLN1 might regulate the disease progression of HF at the transcriptional level. Moreover, the mRNA levels of HAPLN1 and CD163 were also validated in an animal model of HF. Conclusions: HAPLN1 might be a new potential biomarker of HF and CD163 may participate in the progression of HF. SNHG16-miR-23-3p/ miR-103-5p / HAPLN1 and XIST-miR-23-3p/miR-103-5p/HAPLN1 may be involved in the transcriptional regulation during HF.
Energy R&amp;D Trends And Sustainable Energy Strategies In IEA Countries: Efficiency, Dependency And Environmental Dynamics
Authors: Bicil İbrahim Murat , Erkul Abdullah , Türköz Kumru 
Published: January 2022

Abstract
Understanding the factors affecting R&D trends in the energy sector has a key role in overcoming environmental concerns such as combating climate change, as well as other economic and political problems related to energy. Based on such concerns, this study aims to analyze fundamental factors that determine the energy R&D trends of 29 International Energy Agency (IEA) countries. The data set, consisting of annual indicators for the period 1990-2015, is analyzed with the Driscoll-Kraay panel data estimator. Empirical findings for overall sample show that efficiency, import dependency, and the share of renewable energy use are positively related with R&D expenditure in energy sector. CO2 intensity is found to be statistically insignificant. When countries are grouped considering their energy composition structures, the dynamics of energy R&D expenditures differ between groups. In overall evaluation, our findings illustrate efficiency and dependency to have greater priority compared to environmental dynamics on energy R&D expenditures for IEA countries during the period.
Evaluation of Highway Collapse Hazard Based on Rough Set and Support Vector Machine
Authors: He Hujun , Quan Guorong , Zhu Haolei , Li Wei , Xing Rui , Zhao Yichen 
Published: January 2022

Abstract
The prediction of possibility and risk classification of collapse is an important issue in the process of highway construction in mountain area. Based on the principle of rough set and support vector machine, a landslide hazard prediction model was established. First of all, according to field investigation, an evaluation index system and a sample set of evaluation index data were established, the rough set decision table was constructed by preprocessing the original data based on the function classification of standard evaluation index, and then, the influence indexes of the collapse activity were reduced by rough set theory, and the main 9 indexes affecting the collapse activity as the key discriminant factors of support vector machine model, namely slope shape of slope, aspect of slope, slope of slope, height of slope, exposed structural face, stratum lithology, relationship between weakness face and free face, vegetation cover rate and weathering degree of rock were extracted. Then, taking the data of 13 post earthquake collapses provided by the author etc in Yingxiu-Wolong highway of Hanchuan County as training samples, the optimal model parameters were analyzed and calculated. When the penalty parameter C is 8 and the kernel parameter g is 0.5, the correct rate of cross-validation is 100%, and the model is optimal. At last, 4 other landslide data were tested, the discriminant results of the test sample data were compared with the results obtained by uncertainty measure and distance discriminant analysis. The results show that the discriminant results of the test sample data by RS-SVM were consistent with the results obtained by uncertainty measure and distance discriminant analysis, the accurate rate is 100%. The collapse hazard analysis model based on rough set and support vector machine can reduce the computation while ensuring the accuracy of evaluation, and better solve the small sample and nonlinear problems, can provide certain a good idea for collapse hazard evaluation in the future.
The Linear Behavior of the Joint Initial-Boundary-Value Predictability of the Climate System
Authors: Yong Jianlin , Zhang Shaoqing , Liu Zhengyu , Gao Yang , Wu Lixin , Li Jianping , Lu Lv , Jiang Yingjing , Yu Xiaolin , Li Mingkui , Zhao Haoran , Lin Xiaopei 
Published: January 2022

Abstract
The primary sources of predictability of the Earth climate system come from initial conditions and external radiative forcings, which is a joint initial-boundary-value problem. It is crucial to clearly understand the contributions of initial conditions and external forcings (i.e. initial-value and boundary-value) to the total predictability, for which the traditional predictability study examines the growth of system uncertainties is however difficult to quantitatively identify. This study uses the variations of prediction skills in the space of prediction lead times to systematically examines the linear behaviors of initial-value, boundary-value and joint initial-boundary-value predictability, which are measured by anomaly correlation coefficients between system states in a series of perturbed experiments on initial and boundary conditions. The lead time scale at which the prediction skill induced from external forcing exceeds the skill from the initial condition is a key measurement of system uncertainty, after which the boundary-value predictability efficiently extends the total predictability. These results provide a guideline to understand how the accurate setting of external forcing caused by human activities promotes the prediction skill when a comprehensive Earth climate system model is initialized to make climate predictions.
Characteristics of the Turkana Low-Level Jet Stream and the Associated Rainfall in Cmip6 Models
Authors: lino oscar , Nzau Mutemi J. , Ellen Dyer , Franklin Opijah , Rachel James , Richard Washington , Webb Tom 
Published: January 2022

Abstract
The Turkana low-level jet stream (TJ) is important to climatic conditions over northern Kenya and East Africa. The representation of the TJ in climate models varies due to the TJ interaction with Turkana channel that is influenced by model resolution and influences the model representation of the regional climate. This study compares features of the TJ in CMIP6 AMIP model simulations with ERA5. Models reveal wind speeds that match those of the reanalysis from the ERA5 at the jet entrance (13 m/s) but lower magnitudes of wind speed and vertical shears compared to ERA5 within the Turkana channel. The models with slowest wind speeds, have a flattened Turkana channel and fail to exhibit the terrain constriction at 37°E which otherwise aids in accelerating winds to form a jet core. Furthermore, they fail to represent the narrowing of the channel as in ERA5, thereby forming blocking walls in the channel, forcing vertical ascent and mixing, and weakening shear. Stronger ascent is conducive for rainfall formation, enhancing wet anomalies by up to 8% above the observed climatology at the exit of the TJ when the jet stream is weaker. By applying a new narrowing index, we demonstrate the need to improve topography details in the CMIP6 models, particularly those with resolution coarser than 1.5°, in order to properly simulate the TJ and the observed rainfall over the northwestern areas of eastern Africa.
Which Muscle Mass Measurement Should we Prefer in the Global Leadership Initiative on Malnutrition Criteria?
Authors: Sahin Gulsah Gunes , Akin Sibel , Ergul Serap Sahin , Ozer Nurhayat Tugra 
Published: January 2022

Abstract
Objectives: The first aim of our study was to evaluate the malnutrition rates of patients separately with four different phenotypic methods (Calf Circumference (CC), Middle Arm Circumference (MAMC), Fat Free Mass Index (FFMI), and Handgrip Strength (HGS)) that define reduced muscle mass with the Global Leadership Initiative on Malnutrition (GLIM).The study's first aim was to determine the frequency of MN according to (Mini Nutritional Assessment) MNA and the GLIM criteria. The second aim was to evaluate the relationship between the energy and content of the foods consumed daily by older adults and four different phenotypic methods.
Methods: The study included 238 outpatients. Anthropometric measurements, body composition and HGS of the patients were evaluated. The dietician recorded the 24-hour food consumption records of the patients.
Results: The median age of outpatients included in the study was 68(60-85). The median Basal metabolic Index (BMI) value of the outpatients was 30.7(16.9-61.2). GLIM-MAMC was 26.9%, GLIM-CC was 28.1%, GLIM-FFMI was 31.5%, GLIM-HGS was 31.9%, and GLIM-2F+2E, 21.4% of outpatients.
Conclusion: One of the most practical and straightforward methods to determine malnutrition in geriatric outpatients is the MAMC, which is more meaningful with food consumption recording. However, the method that determines the highest rate of MN according to HGS is the GLIM. By using these methods, the malnutrition of patients will not be neglected.
Minimising Multi-Centre Radiomics Variability Through Image Normalisation: A Pilot Study
Authors: Campello Víctor M. , Martín-Isla Carlos , Izquierdo Cristian , Guala Andrea , Rodríguez-Palomares José F. , Viladés David , Descalzo Martín L. , Karakas Mahir , Çavus Ersin , Raisi-Estabragh Zahra , Petersen Steffen E. , Escalera Sergio , Seguí Santi , Lekadir Karim 
Published: January 2022

Abstract
Radiomics is an emerging technique for the quantification of imaging data that has recently shown great promise for deeper phenotyping of cardiovascular disease. Thus far, the technique has been mostly applied in single-centre studies. However, one of the main difficulties in multi-centre imaging studies is the inherent variability of image characteristics due to centre differences. In this paper, a comprehensive analysis of radiomics variability under several image-and feature-based normalisation techniques was conducted using a multi-centre cardiovascular magnetic resonance dataset. 218 subjects divided into healthy (n=112) and hypertrophic cardiomyopathy (n=106, HCM) groups from five different centres were considered. First and second order texture radiomic features were extracted from three regions of interest, namely the left and right ventricular cavities and the left ventricular myocardium. Two methods were used to assess features’ variability. First, feature distributions were compared across centres to obtain a distribution similarity index. Second, two classification tasks were proposed to assess: 1) the amount of centre-related information encoded in normalised features (centre identification) and 2) the generalisation ability for a classification model when trained on these features (healthy versus HCM classification). The results showed that the feature-based harmonisation technique ComBat is able to remove the variability introduced by centre information from radiomic features, at the expense of degrading classification performance. Piecewise linear histogram matching normalisation gave features with greater generalisation ability for classification (0.75 ± 0.09). Models trained with features from images without normalisation showed the worst performance overall (0.47 ± 0.13). In conclusion, centre-related information removal did not imply good generalisation ability for classification.
Effects of Training Data on the Learning Performance of LSTM Network for Runoff Simulation
Authors: PENG Anbang , TIAN Yuanyang , XU Wei , ZHANG Xiaoli 
Published: January 2022

Abstract
With the rapid development of Artificial Intelligence (AI) technology, an increasing number of intelligent algorithms have been used for simulating and forecasting hydrological process, among which the Long Short-Term Memory (LSTM) network is widely studied. The training of artificial intelligence networks often entails a large amount of training data, which contradicts the limitation of hydrological data. In this study, the effect of training data amount on the performance of LSTM network for runoff simulation are evaluated. First, the runoff series of 130 years are randomly generated by K-Nearest Neighbour (KNN) algorithm and SWAT model. The K-Nearest Neighbour (KNN) algorithm is employed for generating the meteorological data series based on the observed data, and the SWAT model is used to obtain the runoff series with the generated meteorological data series. Then, the LSTM models are developed and evaluated, with the 5-year, 10-year, 20-year, 40-year and 80-year data series of rainfall and runoff as training data respectively, and the 50-year data serves as validating data. The results obtained in Yalong River, Minjiang River and Jialing River show that (1) increasing the training data amount can effectively reduce the over-fittings of LSTM network; (2) increasing the training data amount can also improve the prediction accuracy and stability of LSTM network.