bluuebunny/crossref_metadata_2025_split huggingface dataset:
Predictors of School Students’ Leisure-Time Physical Activity: An Extended Trans-Contextual Model Using Bayesian Path Analysis
Authors: Polet Juho J. , Schneider Jekaterina , Hassandra Mary , Lintunen Taru , Laukkanen Arto , Hankonen Nelli , Hirvensalo Mirja , Tammelin Tuija H. , Hamilton Kyra , Hagger Martin S. 
Published: July 2020

Abstract
Background: The trans-contextual model has been applied to identify the determinants of leisure-time physical activity participation in secondary school students. In the current study, the trans-contextual model was extended to include additional constructs that represent non-conscious, implicit processes that lead to leisure-time physical activity participation alongside the motivational and social cognition constructs from the model. The current study used baseline and follow-up data from an intervention study to test the extended model.Methods:The current study adopted a two-wave prospective design. Secondary-school students (N = 502) completed measures of perceived autonomy support from physical education (PE) teachers, autonomous motivation in PE and leisure-time contexts, social cognition constructs (attitudes, subjective norms, perceived behavioral control), intentions, trait self-control, habits, and past behavior all in a leisure-time physical activity context. Five weeks later, students (N = 298) completed a follow-up self-report measure of their leisure-time physical activity.Results: Bayesian path analyses supported two key premises of the model: perceived autonomous support predicted autonomous motivation in PE, and autonomous motivation in PE predicted autonomous motivation in leisure time. However, while both forms of autonomous motivation predicted social cognition constructs and intentions, they did not predict leisure-time physical activity participation. Trait self-control, attitudes, and past behavior were direct predictors of intentions and leisure-time physical activity participation. Specifying informative priors for key model relations using Bayesian analysis yielded greater precision for some of the estimates.Conclusions: Findings raise some questions on the efficacy of the original trans-contextual model, but highlight the value of extending the model to incorporate additional constructs representing non-conscious processes.
How should community health workers in fragile contexts be supported: Qualitative evidence from Sierra Leone, Liberia and Democratic Republic of Congo
Authors: Raven Joanna , Wurie Haja , Idriss Ayesha , Bah Abdulai Jawo , Baba Amuda , Nallo Gartee , Kollie Karsor K. , Dean Laura , Steege Rosie , Martineau Tim , Theobald Sally 
Published: July 2020

Abstract
Background: Community Health Workers (CHWs) are critical players in fragile settings, where staff shortages are particularly acute, health indicators are poor and progress towards Universal Health Coverage is slow. Like other health workers, CHWs need support to contribute effectively to health programmes and promote health equity. Yet the evidence base of what kind of support works best is weak. We present evidence from three fragile settings - Sierra Leone, Liberia and Democratic Republic of Congo on managing CHWs, and synthesise recommendations for best approaches to support this critical cadre.Methods: We used a qualitative study design to explore how CHWs are managed, the challenges they face and potential solutions. We conducted interviews with decision makers and managers (n=37), life history interviews with CHWs (n=15) and reviewed policy documents. Results: Fragility disrupts education of community members so that they may not have the literacy levels required for the CHW role. This has implications for the selection, role, training and performance of CHWs. Policy preferences about selection need discussion at the community level, so that they reflect community realities. CHWs scope of work is varied and may change over time, requiring ongoing training. The modular, local, and mix of practical and classroom training approach worked well, helping to address gender and literacy challenges and developing a supportive cohort of CHWs. A package of supervision, community support, regular provision of supplies, performance rewards and regular remuneration is vital to retention and performance of CHWs. But there are challenges with supervision, scarcity of supplies, inadequate community recognition and unfulfilled promises about allowances. Clear communication about incentives with facility staff and communities is required as is their timely delivery.Conclusions: This is the first study that has explored the management of CHWs in fragile settings. CHWs interface role between communities and health systems is critical because of their embedded positionality and the trusting relationships they (often) have. Their challenges are aligned to those generally faced by CHWs but chronic fragility exacerbates them and requires innovative problem solving to ensure that countries and communities are not left behind in reforming the way that CHWs are supported.
An Improved WENO-Z Scheme with Symmetry-Preserving Mapping
Authors: Hong Zheng , Ye Zhengyin , Ye Kun 
Published: July 2020

Abstract
Since the classical weighted essentially non-oscillatory (WENO) scheme is proposed, various improved versions have been developed, and typical one is the WENO-Z scheme. Although better resolution is achieved, it is shown in this article that, the result of WENO-Z scheme suffers evident distortion in the long-time simulation of the linear advection equation. In order to fix the problem of WENO-Z, a symmetry-preserving mapping method is proposed in this article. In the original mapping method, the weight of each sub-stencil is used to map, which is demonstrated to cause asymmetric improvement about a discontinuity. This asymmetric improvement will lead to a distorted solution, more severe with longer output time. In the symmetry-preserving mapping method, a new variable related to the smoothness indicator is selected to map, which has the same ideal value for each sub-stencil. Using the new mapping method can not only fix the distortion problem of WENO-Z, but also improve the numerical resolution. Several benchmark problems are conducted to show the improved performance of the resultant scheme.
Development and internal validation of risk prediction model of metabolic syndrome in oil workers
Authors: Wang Jie , Li Chao , Li Jing , Qin Sheng , Liu Chunlei , Wang Jiaojiao , Chen Zhe , Wu Jianhui , Wang Guoli 
Published: July 2020

Abstract
Background.The prevalence of metabolic syndrome continues to rise sharply worldwide, seriously threatening people's health. The optimal model can be used to identify people at high risk of metabolic syndrome as early as possible, to predict their risk, and to persuade them to change their adverse lifestyle so as to slow down and reduce the incidence of metabolic syndrome.Objective.To develop and internally verify three risk prediction models for the metabolic syndrome of petroleum workers, compare the prediction performance of the three models, and find the optimal model.Methods. Design existing circumstances research. A total of 1,468 workers from an oil company who participated in occupational health physical examination from April 2017 to October 2018 were included in this study. We established the Logistic regression model, the random forest model and the convolutional neural network model, and compared the prediction performance of the models according to the F1 score, sensitivity, accuracy and other indicators of the three models.Results.The results showed that the accuracy of the three models in the training set was 83.45%, 94.21% and 86.34%, the sensitivity was 78.47%, 94.62% and 81.30%, the F1 score was 0.79, 0.93 and 0.83, the area under the ROC curve was 0.894, 0.987 and 0.935, and the Integrated Calibration Index was 0.074, 0.071 and 0.078, respectively. In the test set, the accuracy was 76.72%, 80.66% and 78.69%, the sensitivity was 70.00%, 77.50% and 68.33%, the F1 score was 0.70, 0.76 and 0.71, the area under the ROC curve was 0.797, 0.861 and 0.855, and the Integrated Calibration Index was 0.064, 0.051 and 0.096, respectively.Conclusions.The study showed that the prediction performance of random forest model is better than other models, and the model has higher application value, which can better predict the risk of metabolic syndrome in oil workers, and provide corresponding theoretical basis for the health management of oil workers.
Differentiating novel coronavirus pneumonia from general pneumonia based on machine learning
Authors: Liu Chenglong , Wang Xiaoyang , Liu Chenbin , Sun Qingfeng , Peng Wenxian 
Published: July 2020

Abstract
Background: Chest CT screening as supplementary means is crucial in diagnosing novel coronavirus pneumonia (COVID-19) with high sensitivity and popularity. Machine learning was adept in discovering intricate structures from CT images and achieved expert-level performance in medical image analysis. Methods: An integrated machine learning framework on chest CT images for differentiating COVID-19 from general pneumonia (GP) was developed and validated. Seventy-three confirmed COVID-19 cases were consecutively enrolled together with twenty-seven confirmed general pneumonia patients from Ruian People’s Hospital, from January 2020 to March 2020. To accurately classify COVID-19, region of interest (ROI) delineation was implemented base on ground glass opacities (GGOs) before feature extraction. Then, 34 statistical texture features of COVID-19 and GP ROI images were extracted, including 13 gray level co-occurrence matrix (GLCM) features, 15 gray level-gradient co-occurrence matrix (GLGCM) features and 6 histogram features. High dimensional features impact the classification performance. Thus, ReliefF algorithm was leveraged to select features. The relevance of each features was the average weights calculated by ReliefF in n times. Features with relevance lager than the empirically set threshold T were selected. After feature selection, the optimal feature set along with 4 other selected feature combinations for comparison were applied to the ensemble of bagged tree (EBT) and four other machine learning classifiers including support vector machine (SVM), logistic regression (LR), decision tree (DT), and K-nearest neighbor with Minkowski distance equal weight (KNN) using 10-fold cross-validation. Results and Conclusions: The classification accuracy (ACC), sensitivity (SEN), specificity (SPE) of our proposed method yield 94.16%, 88.62% and 100.00%, respectively. The area under the receiver operating characteristic curve (AUC) was 0.99. The experimental results indicate that the EBT algorithm with statistical textural features based on GGOs for differentiating COVID-19 from general pneumonia achieved high transferability, efficiency, specificity, sensitivity, and impressive accuracy, which is beneficial for inexperienced doctors to more accurately diagnose COVID-19 and essential for controlling the spread of the disease.
Maintaining proper health records improves machine learning predictions for novel 2019-nCoV
Authors: KHAN KOFFKA , Ramsahai Emilie 
Published: July 2020

Abstract
Background: An ongoing outbreak of a novel coronavirus (2019-nCoV) pneumonia continues to affect the whole world including major cities such as China, USA, Italy, France and the United Kingdom. We present outcome ('recovered', 'isolated' or 'death') risk estimates of the 2019-nCoV over 'early' datasets. A major consideration is how likely are people to die from 2019-nCoV? Method: Accounting for the impact of the variations in the reporting rate of 2019-nCoV, we modelled machine learning techniques (AdaBoost, Bagging, Extra-Trees, Decision-Trees and k-Nearest Neighbours Classifiers) on two 2019-nCoV datasets obtained from Kaggle in March 30th 2020. We used 'country', 'age' and 'gender' as features to predict outcome for both datasets. Including the patient's 'disease' history (only present in the second dataset) to predict outcome for the second dataset. Results: The use of a patient's 'disease' history improves the prediction of 'death' by more than a 7-fold. Models ignoring a patent's 'disease' history performed poorly in test predictions. Conclusion: Our findings indicate the potential of using a patient's 'disease' history as part of the feature set in machine learning techniques to improve 2019-nCoV predictions. This can have a positive effect on predictive patient treatment and result in ease for current overburdened healthcare systems worldwide, especially with an increasing prevalence of second and third wave re-infections in some countries.
Impact of the COVID-19 Pandemic on Urology Practice in Indonesia: A Nationwide Survey
Authors: Rasyid Nur , Birowo Ponco , Parikesit Dyandra , Rahman Fakhri 
Published: July 2020

Abstract
Background.Coronavirus disease 2019 (COVID-19) has affected daily practices in health care services. This study aimed to investigate the impact of COVID-19 on urology practice in Indonesia.Methods.This was a cross-sectional study using web-based questionnaire (Survey Monkey), which was distributed and collected within a period of three weeks. All practicing urologists in Indonesia were sent an e-questionnaire link via E-mail, WhatsApp Messenger application, and/or short message service, and the chief of residents in each urology centre distributed the e-questionnaire to urology residents.ResultsThe response rate was 369/485 (76%) among urologists and 220/220 (100%) among urology residents. Less than 10 percent of the responses in each section were incomplete. There are 35/369 (9.5%) Nine percent of urologists and 59/220 (26.8%) of urology residents had been suspected as COVID-19 patients, of whom seven of them were confirmed to be COVID-19 positive. The majority of urologists (66%) preferred to continue face-to-face consultations with a limited number of patients, and more than 60% of urologists preferred to postpone the majority (66%) or all elective surgery. Most urologists also chose to postpone elective surgery in patient with COVID-19-related symptoms and patient who required post-operative ICU-care. Urologist and urology residents reported high rates of using personal protective equipment, except for medical gowns and N95 masks, which were in short supply. Several uro-oncology surgeries were considered to be the top priority for Indonesian urologist during COVID-19 epidemic period.Conclusion.The COVID-19 pandemic has had a major impact on urology practice in Indonesia.
The Value of 18F-FDG PET/CT in avoiding overtreatment of 131l Avidity Pulmonary Metastasis of Differentiated Thyroid Cancer
Authors: Xu Zhongyun , Li Chao , Fang Feng , Zhang Linlin , Wang Hui , Fu Hongliang , Wu Shuqi 
Published: July 2020

Abstract
Background: We usually use 131 I whole body scan and serum thyroglobulin (Tg) values to determine whether differentiated thyroid cancer (DTC) patients need to receive 131 I treatment, but not all 131 I-avid (functioning) patients have good responses, which is more likely to cause the 131 I-avid patient to receive overtreatment. Our study aims to assess the date of 18 F-FDG PET/CT to avoid 131 I overtreatment and research the status of 131 I-avid pulmonary metastases (PMs) and the prognosis of the patients. Methods: The 131 I-avid PMs of DTC patients who underwent 18 F-FDG PET/CT scans were included. The SUVmax (maximum standardized uptake value), MTV (metabolic tumour volume) and TLG (total lesion glycolysis) were used to estimate 18 F-FDG uptake. The mean follow-up period was 34.14 ± 18.64months. Progression-free survival (PFS) was estimated by the Kaplan-Meier method.The study was based on per-patient and per-lesion analyses. Results: Among the 42 included patients, 34 (34/42, 81%) showed 18 F-FDG uptake, which was defined as abnormal foci (SUVmax >1.0) in the lungs. SUVmax, MTV and TLG and tumour size were the factors that influenced the outcome of 131 I treatment based on Tg levels (p=0.000,0.016,0.000,0.000). The only independent factor was the size of the lesion. There was a significant difference in response to 131 I therapy between PMs with F-I+ and F+/I+ according to both Tg levels and RECIST (version 1.1) (p= 0.044,0.001), according to the per-lesion analysis. When the changes in size or metabolism of some lesions are inconsistent with therapeutic efficacy of patients, it indicates that these patients have a poor prognosis (P=0.003). Conclusions: We concluded that higher 18 F-FDG uptake and larger tumour size predict poor therapeutic effects and a high risk of disease progression in 131 I-avid PMs of DTC. For evaluating the efficiency of 131 I treatment, per-lesion analyses and assessing the data of 18 F-FDG PET/CT would be more reliable than per-patient evaluation only. The increasing 18 F-FDG uptake or diameter of PMs may lead to a poor prognosis for the patient, and early focal treatment modalities may improve their life span.
Effects of microbiome rare taxa filtering on statistical analysis
Authors: Cao Quy Xuan , Sun Xinxin , Rajesh Karun , Chalasani Naga , Gelow Kayla , Katz Barry , Shah Vijay H. , Sanyal Arun J. , Smirnova Ekaterina 
Published: July 2020

Abstract
Background: Accuracy of microbial community detection in 16S rRNA marker-gene and metagenomic studies suffers from contamination and sequencing errors that lead to either falsely identifying microbial taxa that were not in the sample or misclassifying the taxa of DNA fragment reads. Filtering is defined as removing taxa that are present in a small number of samples and have small counts in the samples where they are observed. This approach reduces extreme sparsity of microbiome data and has been shown to correctly remove contaminant taxa in cultured "mock" datasets, where the true taxa compositions are known. Although filtering is frequently used, careful evaluation of its effect on the data analysis and scientific conclusions remains unreported. Here, we assess the effect of filtering on the alpha and beta diversity estimation, as well as its impact on identifying taxa that discriminate between disease states. Results: The effect of filtering on microbiome data analysis is illustrated on four datasets: two mock quality control datasets where same cultured samples with known microbial composition are processed at different labs and two disease study datasets. Results show that in microbiome quality control datasets, filtering reduces the magnitude of differences in alpha diversity and alleviates technical variability between labs, while preserving between samples similarity (beta diversity). In the disease study datasets, DESeq2 and linear discriminant analysis Effect Size (LEfSe) methods were used to identify taxa that are differentially expressed across groups of samples, and random forest models to rank features with largest contribution towards disease classiffcation. Results reveal that filtering retains significant taxa and preserves the model classification ability measured by the area under the receiver operating characteristic curve (AUC). The comparison between filtering and contaminant removal method shows that they have complementary effects and are advised to be used in conjunction. Conclusions: Filtering reduces the complexity of microbiome data, while preserving their integrity in downstream analysis. This leads to mitigation of the classification methods' sensitivity and reduction of technical variability, allowing researchers to generate more reproducible and comparable results in microbiome data analysis.
WITHDRAWN: Radiation-Induced Acute Injury of Intensity-Modulated Radiotherapy vs. Three-Dimensional Conformal Radiotherapy in Induction Chemotherapy Followed by Concurrent Chemoradiotherapy for Locoregionally Advanced Nasopharyngeal Carcinoma: A Prospective Cohort Study
Authors: Yao Zexin , Lei Xiaoxuan , Chen Zhiyan , Pang Mengru , Huang Jialin , Cheng Biao 
Published: July 2020

Abstract
Purpose: Induction chemotherapy (IC) followed by concurrent chemoradiotherapy (CCRT) was the new standard treatment for locoregionally advanced nasopharyngeal carcinoma (LANPC). We aimed to address whether the addition of intensity-modulated radiotherapy (IMRT) compared to three-dimensional conformal radiotherapy (3D-CRT) aggravate radiation-induced acute injury of these patients.Methods: We conducted a prospective study of consecutive patients in the stage III to IVb with biopsy-proven nonmetastatic LANPC who newly underwent radiotherapy and sequentially received IC, followed by CCRT at our institution. Among 182 patients included, 87 (47.80%) were treated with IMRT and 95 (52.20%) with 3D-CRT. Occurring time of radiation-induced toxicities were estimated and compared using the Kaplan-Meier method and Log-rank test. Results: The most severe acute toxicities included oral mucositis in 97.25% and dermatitis in 90.11%. Subset analysis revealed that Grade 3-4 acute dermatitis were significantly higher in the IMRT than 3D-CRT (P=0.0482). Oral mucositis and dermatitis were the earliest occurrence of acute injuries (2 years: 60.44% and 17.58%). Patients in IMRT group achieved significantly lower risk of bone marrow toxicity (Hazard ratio [HR]= 0.426, P=0.0009), but higher risk of leukopenia (HR=1.444, P=0.0344) and gastrointestinal injury (HR=2.383, P=0.0103). Multivariate analyses also demonstrated that IMRT (HR=0.429, P=0.0211), female gender (HR=2.070, P=0.0531) and hepatitis (HR=2.152, P=0.0552) were the independent prognostic factors for bone marrow toxicity. The use of IMRT was a predictive factor of significantly better short-term efficacy, whereas anemia was a significant independent predictor.Conclusions: In a combined regimen of IC followed by CCRT for the treatment of LANPC, IMRT seems to be an aggressive technique with a trend towards increased gastrointestinal and hematological toxicities, but decreased bone marrow toxicity than those treated with 3D-CRT.
Combination of SB431542, Chir9901, and Bpv as a Novel Supplement in the Culture of Umbilical Cord Blood Hematopoietic Stem Cells
Authors: Afzal Elaheh , Zarrabi Morteza , Asghari Mohammad Hassan , Ebrahimi Marzieh 
Published: July 2020

Abstract
Background: Small molecule compounds have been well recognized for their promising power in generation, expansion and maintenance of embryonic or adult stem cells. The aim of this study was to identify a novel combination of small molecules in order to optimize the ex vivo expansion of umbilical cord blood derived-CD34+ cells. Methods: Considering the most important signaling pathways involved in the self-renewal of hematopoietic stem cells, CB-CD34+ cells were expanded with cytokines in the presence of seven small molecules including SB, PD, Chir, Bpv, Pur, Pμ and NAM. Eliminativism approach was used to find the best combination of selected small molecules for effective ex vivo expansion of CD34+ cell. In each step, proliferation, self-renewal, and clonogenic potential of the expanded cells as well as expression of some hematopoietic stem cell related genes were studied. Finally, the engraftment potential of expanded cells was also examined by the mouse intra-uterine transplantation model. Results: Our data shows that simultaneous use of SB431542 (TGF-β inhibitor), Chir9901 (GSK3 inhibitor) and Bpv (PTEN inhibitor), resulted in a 50-fold increase in the number of CD34+CD38- cells. This was further reflected in approximately 3 times increase in clonogenic potential of the small molecule cocktail-expanded cells. These cells, also, showed a 1.5-fold higher engraftment potential in the peripheral blood of NMRI model of in utero transplantation. These results are in total conformity with up-regulation of HOXB4, GATA2 and CD34 marker gene as well as CXCR4 homing gene. Conclusion: Taken together, our findings introduce a novel combination of small molecules to improve the yield of existing protocols used in the expansion of hematopoietic stem cells.