bluuebunny/crossref_metadata_2025_split huggingface dataset:
Clinical Outcomes of S2 Alar-Iliac (S2AI) Screw Technique in The Treatment of Severe Spinal Sagittal Imbalance: A Retrospective 2-Year Follow-Up Study
Authors: Zhao Yao , Xu Beiyu , Qi Longtao , Li Chunde , Yu Zhengrong , Sun Haolin , Wang Shijun 
Published: November 2021

Abstract
Background

The treatment of adult spinal deformity (ASD) remains a significant challenge, especially in elderly patients. This study aimed to evaluate the outcomes of the S2AI screw technique in the treatment of severe spinal sagittal imbalance with a minimum 2-year follow-up.
Methods

From January 2015 to December 2018, 23 patients with severe degenerative thoracolumbar kyphosis who underwent placement of S2AI screws for long segment fusion were retrospectively reviewed. Patients were divided into group A (no mechanical complications, 13 cases) and group B (with mechanical complications, 10 cases) according to the occurrence of mechanical complications at the last follow-up. Radiographic parameters were compared between groups preoperatively, 1 month postoperatively, and at the last follow-up. Risk factors for mechanical complications were analyzed.
Results

The incidence of mechanical complications was 43.5%, and the revision rate was 17.4%. At 1 month postoperatively, sagittal correction was better in group A than in group B (P<0.05). The lumbar lordosis (LL), pelvic incidence minus lumbar lordosis (PI-LL), T1 pelvic angle (TPA), and sagittal vertical axis (SVA) of both groups at the last follow-up were significantly different from corresponding values at 1 month postoperatively (P<0.05), and the sagittal correction was partially lost. Pearson correlation analysis revealed that the occurrence of mechanical complications was associated with sacral slope (SS), LL, PI-LL, and global alignment and proportion (GAP) score at 1 month postoperatively.
Conclusion

A high incidence of mechanical complications was observed in long-segment corrective surgery with the S2AI screw technique for severe spinal sagittal imbalance. Inadequate sagittal correction is a risk factor for the development of mechanical complications.
Survival Status and Predictors of Mortality Among Preterm Neonates in Southwest Ethiopia: A Retrospective Cohort Study
Authors: Toma Temesgen Mohammed , Merga Hailu , Dube Lamessa 
Published: November 2021

Abstract
Background: Prematurity is a global public health priority linked with high neonatal morbidity and mortality. There is limited evidence regarding preterm neonatal mortality and its predictors to inform programs and policies in Ethiopia. The aim of this study was therefore to assess survival status and predictors of mortality among preterm neonates admitted to the neonatal intensive care unit of Jimma University Medical Center. Methods: Facility-based retrospective cohort study was conducted from March 11 to April 20, 2020, among 505 randomly selected preterm neonates admitted to the Neonatal Intensive Care Unit (NICU) of Jimma University Medical Center (JUMC). Data were collected from medical records and registers using a structured data collection checklist. Data were entered into Epi-Data 3.1, exported to, and analyzed with STATA version 15. Kaplan Meir method and log-rank test were used to estimate survival time, and compare survival experience. Cox-regression analysis was fitted to identify predictors of time to death. The assumption of the proportional hazard model was checked using Schoenfeld residual test. Adjusted hazard ratio (AHR) with its 95% confidence interval (CI) and corresponding p-value <0.05 was set to declare statistical significance. Result: In this study, 127 (25.1%) neonates died with neonatal morality rate of 28.9 deaths per 1,000 neonate-days [95%CI: 24.33, 34.46]. About 103 (81.1%) deaths occurred during early neonatal period. Antenatal steroid use [AHR=0.55, 95%CI: 0.34, 0.90], obstetric complications [AHR=1.84, 95%CI: 1.20, 2.82], gestational age increment by week [AHR= 0.81, 95%CI: 0.75, 0.87], respiratory distress syndrome [AHR=1.52, 95%CI: 1.01, 2.29], neonatal sepsis [AHR=1.71, 95%CI: 1.18, 2.49], perinatal asphyxia [AHR=2.44, 95%CI: 1.33, 4.49], and receiving kangaroo-mother care [AHR=0.48, 95%CI: 0.30, 0.77] were predictors of preterm neonatal mortality.Conclusion: Preterm neonatal mortality rate was high. Most neonatal deaths occurred in the early neonatal period. Predictors of preterm neonatal mortality were antenatal steroid use, obstetric complications, gestational age increment by week, respiratory distress syndrome, neonatal sepsis, perinatal asphyxia, and receiving kangaroo-mother care. Hence, early detection and management of obstetric and neonatal complications, use of antenatal steroids, kangaroo-mother care, and ensuring a continuum of care should be strengthened to increase preterm neonatal survival.
Forecasting the Reaction of DNA Modifying Enzymes on DNA Nanostructures by Coarse Grained Model for Stimuli-Responsive Drug Delivery
Authors: Deng Yingnan , Tan Yuanhang , Zhang Linghao , Zhang Chunyi , Su Xin 
Published: November 2021

Abstract
The reactivity of DNA modifying enzymes on their natural nucleic acid substrates has been fully understood. However, their reactivity on self-assembled nanostructures of nucleic acid is complicated and unpredictable. Here, we employed the molecular dynamic simulation to forecast the reactivity of tumor biomarker enzymes on DNA nanotubes by coarse grained model. It is found that the enzyme accessibility and the potential energy of the reaction products co-determine the structural change of DNA nanotubes. The reactivity can be regulated by the position of enzyme recognition site. According to the simulation results, stimuli-responsive drug nanocarrier with superior sensitivity and selectivity was developed. Drug payloads released in cancer cells is 3.7~5.5-fold higher than that in normal cells. The DNA nanocarrier equipped with cancer-specific aptamer AS1411 is used to deliver doxorubicin (DOX) to tumor-bearing mice not only effectively inhibiting tumor growth but also protecting major organs from drug-caused damage. This work provides new insights into the enzymatic reactivity of DNA nanostructures enriching the library of DNA-based reactions and heralding broad applications in nanomedicine.
Filler Dependent Microstructural and Optical Properties of PVDF/PEG Blends Versus PVDF/GO Nanocomposites
Authors: Panda Maheswar 
Published: November 2021

Abstract
The micro-structural features and the optical properties of polymer blends (PB) of polyvinylidene fluoride (PVDF) base with polyethylene glycol (PEG) and polymer nanocomposites (PNC) of PVDF base with graphene oxide (GO) as fillers are compared. Different weight fractions of PVDF/PEG and PVDF/GO samples were prepared through solution casting followed by spin coating. The microstructure of PVDF/PEG and PVDF/GO shows formation of immiscible/compatible/ homogenous microstructures over different extents of loading of the filler components. UV-Visible spectrometry revealed an increase in absorbance with the amount of filler and the absorbance was found to be lesser and highly dispersive for the PB as compared to PNC, attributed to the heterogeneity/electrical conductivity of the respective fillers of the two systems. Spectroscopic ellipsometry was used to study the various optical constants (e.g. refractive index, extinction co-efficient, etc.) & their dispersion behavior over the visible range of wavelengths. The parameters show large variation as a function of component fillers due to the different extent of interaction of matrix/filler. The optimized parameters were obtained for the 20% PB & the 5% PNC samples respectively, suggesting PNC with nano fillers are always to be a better choice for developing them as materials suitable for various optical applications.
A New Prediction Model Integrated Serum Lipid Profile for Patients with Multiple Myeloma
Authors: Wang Huizhong , Chen Biyun , Shao Ruonan , Liu Wenjian , Xiong Lang , Li Li , Lu Yue 
Published: November 2021

Abstract
This study aimed to explore a predictive risk-stratification model combing clinical characteristics and lipid profiles in multiple myeloma (MM) patients. The data of 275 patients in Sun Yat-Sen University Cancer Center were retrospectively analyzed and randomly divided into the training (n = 138) and validation (n = 137) cohorts. Triglyceride, low-density lipoprotein (LDL), high-density lipoprotein (HDL), lactate dehydrogenase (LDH), Apolipoprotein B (Apo B) and Apo B / Apolipoprotein A1 (Apo A1) ratio were the prognostic factors identified through univariate and multivariate Cox analysis. A 6-prognostic factor model was constructed based on Lasso regression. Patients were divided into low- and high-risk groups and the former group showed longer overall survival (OS) time (p<0.05). The area under the curve (AUC) of the risk score model for 5-and 10-year OS were 0.756 [95% CI: 0.661-0.850] and 0.940 [95% CI: 0.883-0.997], which exhibited better accuracy than International Staging System (ISS) and Durie and Salmon (DS) stage. The nomogram integrating ISS stage and risk score increased the prediction accuracy. The model can be used to help monitor the metabolic state and to establish primary prevention strategies to identify new therapeutic targets.
The dopamine transporter counter-transports potassium to increase the uptake of dopamine
Authors: Loland Claus , Schmidt Solveig , Malle Mette , Nielsen Anne , Bohr Søren , Pugh Ciara , Nielsen Jeppe , Poulsen Ida , Rand Kasper , Hatzakis Nikos 
Published: November 2021

Abstract
The dopamine transporter (DAT) facilitates dopamine reuptake from the extracellular space, and thereby terminates neurotransmission and refills cellular stores of dopamine. DAT belongs to the neurotransmitter:sodium symporter (NSS) family, which includes similar transporters for serotonin, norepinephrine, and GABA. A hallmark of NSS proteins is their ability to utilize the energy stored in the inward-directed Na+ gradient to drive the uphill transport of substrate. Decades ago, it was shown that the serotonin transporter also counter-transports K+, but investigations of K+-coupled transport in other NSSs have been inconclusive. Here, we show that the Drosophila dopamine transporter (dDAT) counter-transports K+. We found that ligand binding to both dDAT and human DAT is inhibited by K+ and that the conformational dynamics of dDAT in K+ is highly divergent from both the apo- and Na+-bound conformations. Furthermore, we found that K+ increased dopamine uptake by purified dDAT reconstituted in liposomes, and we visualized, in real-time, Na+ and K+ fluxes in single proteoliposomes using fluorescent ion indicators. Our results expand on the fundamentals of dopamine transport and prompt a reevaluation of the impact of K+ on other NSSs, including whether K+ counter-transport is a common mechanism for this pharmacologically important protein family.
Correlation of regional cerebral blood flow with brain structural changes in mild cognitive impairment and Alzheimer's disease
Authors: Nabizadeh Fardin , Rostami Mohammad Reza , Balabandian Mohammad , Mehrabi Soraya , Sedighi Mohsen 
Published: November 2021

Abstract
Objectives

Brain atrophy and structural changes due to aging contribute strongly in the pathophysiology of mild cognitive impairment (MCI) and Alzheimer’s Disease (AD). Regional cerebral blood flow (CBF) impairment is believed to be one of the initial changes in the AD continuum. In this study, we investigated the association between CBF and brain structural changes associated with aging and neurodegeneration.
Methods

Data from three groups of participants including 39 control normal (CN), 82 MCI, and 28 AD subjects were downloaded from the Alzheimer’s disease Neuroimaging Initiative (ADNI). Magnetic resonance images (MRI) of participants were automatically segmented by FreeSurfer V 7.0 software and arterial spin labeling (ASL) MRI was applied to measure CBF and investigate effect of aging and structural changes on CBF in experimental groups. One way ANOVA and Pearson correlation coefficient were used to compare data and find correlation between CBF and structural changes in the brain.
Results

AD patients had significantly lower Mini-Mental State Examination (MMSE) score (p=0.001) and more APOE-ε4 carriers (p=0.001) as compared to the MCI and CN groups. Our findings revealed a wide spread significant correlation between the CBF and structural changes, including cortical volume, subcortical volume, surface area, and thickness in all participants, particularly AD patients after adjusting for age, sex, and APOE genotyping status.
Conclusion

Several structural abnormalities correlated with declined CBF and could potentially be used as imaging biological correlates biomarkers to predict early onset of AD, regardless of Aβ or tau accumulation.
Effective of Fusion in the Treatment of Lumbar Spinal Stenosis：A Systematic Review Andmeta-Analysis
Authors: Yang Yang , Tang Shi-tian , Chen Qian , chen fang 
Published: November 2021

Abstract
Objective: The debate on efficacy of fusion added to decompression for lumbar spinal stenosis (LSS) is ongoing. The primary objective of this systematic review is to compare the outcome after decompression with and without fusion in patients with lumbar spinal stenosis .Methods: A literature search was performed in the Web of Science, EMBASE, Pubmed,and Cochrane Libraryfrom January 1990 to May 2021.The information of screened studies included clinical outcomes, and secondary measures, then data synthesis and meta-analysis were progressed.Data analysis was conducted using the Review Manager 5.0 software.Results: 17 studies were included in the analysis involving 2947 patients in total. In the majority of studies, including seven RCTs and ten observational studies. The pooled data revealed that fusion was associated with signifificantly higher rates of back pain scores when compared with decompression alone in RCT subgroup(SMD=-0.42, 95% CI (–0.60, -0.23), Z=4.31 P<0.0001).However, fusion signifificantly increased the intraoperative blood loss, operative time and hospital stay. Both techniques had similar leg Pain scores , EQ-5D, walking ability，ODI，major complication,clinical satisfactions and reoperation rate.Conclusions: Our studies showed that the additional fusion in the management of LSS yielded no clinical improvements over decompression alone within a 1-year follow-up period. We suggested that the least invasive and least costly procedure, being decompression alone, is preferred in patients with degenerative lumbar spinal stenosis. The appropriate surgical protocol for LSS should be discussed further.
An Efficient Hybrid Grey Wolf Optimization Based KELM Approach for Prediction of the Discharge Coefficient of Submerged Radial Gates
Authors: Roushangar Kiyoumars , Shahnazi Saman , Sadaghiani Arman Alirezazadeh 
Published: November 2021

Abstract
Radial gates are widely used hydraulic structures for flow control in irrigation canals. Accurately prediction of discharge coefficient through radial gates is considered as a challenging hydraulic subject, particularly under highly submerged flow conditions. Incurring the advantages of Kernel-depend Extreme Learning Machine (KELM), this study offers a Grey Wolf Optimization-based KELM (GWO-KELM) for effective prediction of discharge coefficient through submerged radial gates. Additionally, Support Vector Machine (SVM), and Gaussian Process Regression (GPR) methods are also presented for comparative purposes. To build prediction models using GWO-KELM, GPR, and SVM an extensive experimental database was established, consisting of 2125 data samples gathered by the US Bureau of Reclamation. From simulation results, it is observed that the proposed GWO-KELM approach with input parameters of the ratio of the downstream flow depth to the gate opening (y3/w) and submergence ratio (y1-y3/w) provides the best performance with the correlation coefficient (R) of 0.983, the Determination Coefficient (DC) of 0.966 and the Root Mean Squared Error (RMSE) of 0.027. Furthermore, the obtained results showed that the employed kernel-depend methods are capable of a statistically predicting the discharge coefficient under varied submergence conditions with satisfactory level of accuracy.
Performance Evaluation of Machine Learning Models with Ensemble Learning approach in Classification of Water Quality Indices Based on Different Subset of Features
Authors: GARABAGHI FARID HASSANBAKI , Benzer Semra , Benzer Recep 
Published: November 2021

Abstract
Since fresh water resources in form of groundwaters which are the most available water resources for human consumption are extremely limited and due to uncontrolled human activities are prone to contamination, it’s of a great importance to constantly monitor the quality of the ground fresh water resources to provide a sustainable drinking water for people as well as protecting the ecosystem. One tool for modeling the water quality of a basin is Water Quality Index (WQI). However, calculating WQI is complicated and time- consuming, therefore, today, scientists are being inclined to propose simpler ways for modeling the quality of the water resources such as machine learning algorithms. In this study the performance of four machine learning algorithms with ensemble learning approach were evaluated to propose a classification model (classifier) with highest performance. Moreover, to identify the most important water quality parameters in the classification process, three feature selection methods with machine learning approach were applied. As a result, among four classifiers, XGBoost showed outstanding performance, with the accuracy of 96.9696% when all the parameters of interest were involved in the classification process. However, in order to make the model cost-effective it is suggested to conduct the classification with optimum parameters which in this case, for the dataset which was used in this study XGBoost classifier is suggested as the best classifier with the maximum accuracy of 95.606% with 10-Fold Cross Validation when seven parameters which were identified by Backward Feature Elimination Feature selector were involved in the classification process.
Treatment outcome and its predictors among diabetic patients attending at selected hospitals of southern ethiopia.
Authors: Beyene Bereket , Hoyiso Dawit , Woldu Kinfe 
Published: November 2021

Abstract
Background: Diabetes is one of the largest health emergencies of the 21st century. The World Health Organization (WHO) estimates that globally, hyperglycaemia is the third highest risk factor for premature mortality, after high blood pressure and tobacco use. It is an important cause of blindness, kidney failure, lower limb amputation and other long-term consequences that impact significantly on quality of life. It result peoples in a disease, death and increase a health care expenditure.Objectives: To determine treatment outcome of Diabetes mellitus and its predictors among patients attending diabetes treatments at selected hospitals of southern Ethiopia, 2021.Methods: All diabetic patients who attended selected hospitals of southern Ethiopia were a source population. Institution based cross-sectional study design was conducted at selected hospitals of southern Ethiopia. Patient’s data was collected using pretested questionnaire. After cleaning and checking for errors, the data was entered in to Epi-data to minimize error then exported to SPSS for analysis. Descriptive findings were presented by tables and charts. The outcome variable was analyzed by using logistic regression model to identify predictors after assumptions of regression analysis had checked. All the independent variables with p<0.25 was used as a candidate for multivariate analysis. Then the level of significance will be set at p<0.05 and AOR with 95% CL was done as the final model.Result: From the total sample; 277 (65.6%) were found to be good treatment outcome. The number of medication taken AOR 95%CI; 0.924 (0.869, 0.983), presence of complication AOR 95%CI; 0.425 (0.217, .832), increased fasting blood glucose AOR 95%CI; 0.080 (0.034, 0.188) were found to be independent predictors. Since the magnitude of treatment outcome found to be low, provision of health information about the adhering to prescribed medication and monitoring fasting blood glucose level will bring good clinical outcome.
Prognostic Nomogram in Children and Adolescents With Central Nervous System Germ Cell Tumors
Authors: Jia Zetian , Yan Yaqi , Wang Jiuxin , Yang He , Liu Shuo , Han Chengxi , Hu Yuhua 
Published: November 2021

Abstract
Background: The prognostic risk factors for primary central nervous system (CNS) germ cell tumors (GCTs) in children and adolescents remain controversial. This study aimed to develop and validate a nomogram that predicts 5-and 10-year survival rates for CNS GCTs in children and adolescents.Methods: Pediatric with intracranial GCTs in the Surveillance, Epidemiology, and End Results (SEER) database from 2000 to 2018 were analyzed. All GCTs children were randomly divided into the development group (70%) and the validation group (30%). The least absolute shrinkage and selection operator (LASSO) was used to screen the characteristic variables, and the 5- and 10-year survival probability nomogram was constructed. The accuracy of the nomogram was evaluated by consistency index (C-index), calibration plot, receiver operating characteristic curve (ROC), decision curve analysis (DCA) and survival curve.Results: We identified 819 cases of CNS GCTs in children. Three variables (histology, site and size) were selected by lasso regression to construct the nomogram. The C-index of the development group and verification group is 0.744 and 0.759. According to the time points of 5 and 10 years, the area under the curve of the development group is 0.753 and 0.696, and the verification set is 0.787 and 0.718. The results show that the prognostic nomogram developed by us has good accuracy.Conclusions: This is the first prognostic nomogram of CNS GCTs in children and adolescents. It can effectively predict the 5- and 10-year survival probability of children. It provides a useful prediction tool for clinicians.
Association Rule Mining Algorithms for Big Data using RDD-ECLAT Algorithms
Authors: Martha  , Vankdothu Ramdas , Abdul Hameed Mohd , Gangula Rekha 
Published: November 2021

Abstract
The revolution in technology for storing and processing big data leads to data intensive computing as a new paradigm. To find the valuable and precise big data knowledge, efficient and scalable data mining techniques are required. In data mining, different techniques are applied depending on the kind of knowledge to be mined. Association rules are generated from the frequent itemsets computed by frequent itemset mining (FIM) algorithms. The problem of designing scalable and efficient frequent itemset mining algorithms on the Spark RDD framework. The research done in this thesis aims to improve the performance (in terms of execution time) of the existing Spark-based frequent itemset mining algorithms and efficiently re-design other frequent itemset mining algorithms on Spark. The particular problem of interest is re-designing the Eclat algorithm in the distributed computing environment of the Spark. The paper proposes and implements a parallel Eclat algorithm using the Spark RDD architecture, dubbed RDD-Eclat. EclatV1 is the earliest version, followed by EclatV2, EclatV3, EclatV4, and EclatV5. Each version is the consequence of a different technique and heuristic being applied to the preceding variant. Following EclatV1, the filtered transaction technique is used, followed by heuristics for equivalence class partitioning in EclatV4 and EclatV5. EclatV2 and EclatV3 are slightly different algorithmically, as are EclatV4 and EclatV5. Experiments on synthetic and real-world datasets.