bluuebunny/crossref_metadata_2025_split huggingface dataset:
miR-144-3p and miR-425-5p are Negatively Associated With Atrial Fibrosis and Promote Atrial Remodeling by Targeting CREB1 in Atrial Fibrillation
Authors: Wei Feiyu , Lv Li , Kuang Xiaohui , Ren Wenjun , Fan Jie 
Published: December 2020

Abstract
Background: Progression of atrial fibrosis is vital for atrial remodeling in atrial fibrillation (AF). The main objective of this study was to explore the association between miR-144-3p and miR-425-5p, and atrial fibrosis as well as the resultant impact on atrial remodeling in AF.Methods and Results: Through miRNAs sequencing and qRT-PCR, we demonstrated that miR-144-3p and miR-425-5p were downregulated in plasma and atrial tissue among the patients who suffered from AF. We confirmed that the plasma’s miRNAs level was negatively correlated with left atrial fibrosis, which was evaluated with the left low voltage area using left atrial voltage matrix mapping. Catheter ablation restored decreased plasma miR-144-3p and miR-425-5p. Besides, ROC curve analysis revealed that the miRNAs not only differentiated AF from healthy control of AUC 0.928 and 0.921, respectively, but also discriminated persistent AF from paroxysmal AF of AUC 0.906 and 0.888, respectively. Furthermore, the downregulated miR-144-3p and miR-425-5p promoted atrial fibroblast proliferation by CCK-8. CREB1 was realized to be a common direct target for miR-144-3p and miR-425-5p by western blot and luciferase assay.Conclusions: Our findings suggested that miR-144-3p and miR-425-5p could serve as novel atrial fibrosis biomarkers and hence contribute to atrial remodeling in AF.
Comparison of Associations Between Glucocorticoids Treatment and Mortality in COVID-19 Patients and SARS Patients: A Systematic Review and Meta-Analysis
Authors: Li Jianbo , Liao Xuelian , Zhou Yue , Wang Luping , Yang Hang , Zhang Wei , Zhang Zhongwei , Kang Yan 
Published: December 2020

Abstract

BackgroundThe response to glucocorticoids treatment may be different between Covid-19 and SARS. MethodsIn this systematic review and meta-analysis, we searched studies on Medline, Embase, EBSCO, ScienceDirect, Web of Science, Cochrane Library, ClinicalTrials.gov, ICTRP from 2002 to October 7, 2020. We used fixed-effects and random-effects models to compute the risk ratio of death in the group receiving glucocorticoids treatment and the control group for COVID-19 and SARS, respectively.ResultsTen trials and 71 observational studies, with a total of 45935 patients, were identified. Glucocorticoids treatment, was associated with decreased all-cause mortality both in COVID-19 (risk ratio, 0.88; 95% confidence interval, 0.82 to 0.94; I2=26%) and SARS (0.48; 0.29 to 0.79; 10%), based on high quality evidence, as well as decreased all-cause mortality-including composite outcome of COVID-19 (0.89; 0.82 to 0.98; 0%). In subgroup analyses, all-cause mortality was significantly lower among COVID-19 patients being accompanied by severe ARDS but not mild ARDS, taking low-dose or pulse glucocorticoids, being critically severe but not only severe, being of critical severity and old but not young, being of critical severity and men but not women, non-early taking glucocorticoids and taking dexamethasone or methylprednisolone; but for SARS, lower mortality were observed among those who were taking medium-high dose glucocorticoids, being severe or critically severe, early taking glucocorticoids, and taking dexamethasone or prednisolone. ConclusionsGlucocorticoids treatment reduced mortality in COVID-19 and SARS patients of critical severity; however, different curative effects existed between the two diseases among subpopulations, mainly regarding sex- and age-specific effects, optimal doses and use timing of glucocorticoids.
High flow nasal oxygen in patients with COVID-19 associated acute respiratory failure
Authors: Artigas Ricard Mellado , Ferreyro Bruno L. , Angriman Federico , Hernández-Sanz María , Arruti Egoitz , Torres Antoni , Villar Jesús , Brochard Laurent , Ferrando Carlos 
Published: December 2020

Abstract
Purpose
Whether the use of high-flow nasal oxygen in adult patients with COVID-19 associated acute respiratory failure improves clinically relevant outcomes remains unclear. We thus sought to assess the effect of high flow nasal oxygen on ventilator free days, compared to early initiation of invasive mechanical ventilation, on adult patients with COVID-19.
Methods
We conducted a multicentre cohort study using a prospectively collected database of patients with COVID-19 associated acute respiratory failure admitted to 36 Spanish and Andorran intensive care units. Main exposure was the use of high flow nasal oxygen (conservative group), while early invasive mechanical (within the first day of critical care admission; early intubation group) served as the comparator. The primary outcome was ventilator-free days at 28 days. Intensive care unit length of stay and all-cause in hospital mortality served as secondary outcomes. We used propensity score matching to adjust for measured confounding.
Results
A total of 122 matched patients were included in the present analysis (61 for each group). When compared to early intubation, the use of high flow nasal oxygen was associated with an increase in ventilator-free days (mean difference: 8.0 days; 95% confidence interval (CI): 4.4 to 11.7 days), and a reduction in intensive care unit length of stay (mean difference: -8.2 days; 95% CI -12.7 to -3.6 days). No difference was observed in all-cause in-hospital mortality between groups (odds ratio: 0.64; 95% CI: 0.25 to 1.64).
Conclusions
The use of high flow nasal oxygen upon intensive care admission in adult patients with COVID-19 related acute hypoxemic respiratory failure may lead to an increase in ventilator-free days and a reduction in intensive care unit length of stay, when compared to early initiation of invasive mechanical ventilation. Future studies should confirm our findings.
Are associations between home environment and preschool children’s sedentary time influenced by parental educational level in a cross-sectional survey?
Authors: Lehto Elviira , Lehto Reetta , Ray Carola , Pajulahti Riikka , Sajaniemi Nina , Erkkola Maijaliisa , Roos Eva 
Published: December 2020

Abstract
Background: Childhood obesity is a major public health concern, especially in low socioeconomic groups. Sedentary time (SED) is an important predictor of obesity. To be able to diminish SED it is important to find modifiable predictors of sedentary behavior. The home environment associated with children’s SED may vary by parental socioeconomic status. This study aims to clarify the association between parental educational level (PEL) and the home environment of 3–6-year-old children, and to examine how home environment associates with children’s SED, and whether PEL modifies these associations. Methods: A cross-sectional Increased health and wellbeing in preschools (DAGIS) study was conducted in 2015–2016 in Finland. The parents (n=809) filled in questionnaires assessing PEL, and the home physical and social environment related to children’s SED. Children’s SED was measured with accelerometers, which the children (n=745) wore for one week. Results: High PEL was associated with a home environment restraining sedentary behaviour compared with low PEL. Stricter descriptive norms about screen time, considering it important to limit the child’s screen time, and satisfaction about the child’s screen time associated with children’s lower SED. The association with parental psychological control and SED was influenced by PEL. In the PEL stratified analyses, however, the associations between psychological control and SED did not reach statistical significance. Conclusions: Future interventions aiming to decrease SED should pay attention to relevant factors in children’s sedentary behaviour home environment. It is important to acknowledge the possible PEL differences in these factors.
Effect of mindfulness on physical activity in primary healthcare patients: a randomised controlled trial pilot study.
Authors: Nymberg Peter , Calling Susanna , Stenman Emelie , Palmér Karolina , Hansson Eva Ekvall , Sundquist Kristina , Sundquist Jan , Zöller Bengt 
Published: December 2020

Abstract
Increased physical activity can have health benefits among inactive individuals. In Sweden, the healthcare system uses Physical Activity on Prescription (PAP) to motivate patients to increase their physical activity level. Mindfulness may further heighten the internal motivation to engage in physical activity. However, previous research has not demonstrated clear evidence of such associations.Aim: Examine the feasibility of the study design as a preparation for a full-scale study, and examine the differences, between three interventions, in change over time in physical activity levels and in related variables. Method: Comparison between three different interventions in an ordinary primary health care setting: PAP, mindfulness, and a combination of PAP and mindfulness. Physical activity was measured with self-report and ACTi Graph GT1X activity monitor. Statistical analysis was performed with a mixed effect model to account for repeated observations and estimate differences between groups at three- and six-months follow-up.Results: Between September 2016 and December 2018, a total of 88 participants were randomised into three groups. The total dropout rate was 20.4%, the attendance rate to the mindfulness courses (52% > 6 times) and the web-based mindfulness training (8% > 800 minutes) was low according to the stated feasibility criteria. Eleven participants were excluded from analysis due to low activity monitor wear time. Neither the activity monitor data nor self-reported physical activity showed any significant differences between the groups.Conclusion: The study design needs adjustment for the mindfulness intervention design before a fully scaled study can be conducted. A combination of PAP and mindfulness may increase physical activity and self-rated health more than PAP or mindfulness alone. Trial registration: ClinicalTrials.gov, registration number NCT02869854. Regional Ethical Review Board in Lund registration number 2016/404.
CNFE-SE: A novel approach combining complex network-based feature engineering and stacked ensemble to predict the success of Intrauterine Insemination and ranking the features
Authors: Ranjbari Sima , Khatibi Toktam , Dizaj Ahmad Vosough Taghi , Sajadi Hesamoddin , Totonchi Mehdi , Ghaffari Firouzeh 
Published: December 2020

Abstract
Background: Intrauterine Insemination (IUI) outcome prediction is a challenging issue which the assisted reproductive technology (ART) practitioners are dealing with. Predicting the success or failure of IUI based on the couples' features can assist the physicians to make the appropriate decision for suggesting IUI to the couples or not and/or continuing the treatment or not for them. Many previous studies have been focused on predicting the in vitro fertilization (IVF) and intracytoplasmic sperm injection (ICSI) outcome using machine learning algorithms. But, to the best of our knowledge, a few studies have been focused on predicting the outcome of IUI. The main aim of this study is to propose an automatic classification and feature scoring method to predict intrauterine insemination (IUI) outcome and ranking the most significant features. Methods: For this purpose, a novel approach combining complex network-based feature engineering and stacked ensemble (CNFE-SE) is proposed. Three complex networks are extracted considering the patients' data similarities. The feature engineering step is performed on the complex networks. The original feature set and/or the features engineered are fed to the proposed stacked ensemble to classify and predict IUI outcome for couples per IUI treatment cycle. Our study is a retrospective study of a 5-year couples' data undergoing IUI. Data is collected from Reproductive Biomedicine Research Center, Royan Institute describing 11255 IUI treatment cycles for 8,360 couples. Our dataset includes the couples' demographic characteristics, historical data about the patients' diseases, the clinical diagnosis, the treatment plans and the prescribed drugs during the cycles, semen quality, laboratory tests and the clinical pregnancy outcome Results: Experimental results show that the proposed method outperforms the compared methods with Area under receiver operating characteristics curve (AUC) of 0.84 ± 0.01, sensitivity of 0.79 ± 0.01, specificity of 0.91 ± 0.01, and accuracy of 0.85 ± 0.01 for the prediction of IUI outcome. Conclusions: The most important predictors for predicting IUI outcome are semen parameters (sperm motility and concentration) as well as female body mass index (BMI).
Association between serum markers of the humoral immune system and inflammation in the Swedish AMORIS study
Authors: santaolalla aida , Sollie Sam , Rislan Ali , Josephs Debra H. , Hammar Niklas , Walldius Goran , Jungner Ingmar , Garmo Hans , Karagiannis Sophia N. , Hemelrijck Mieke Van 
Published: December 2020

Abstract
Background: Although the onset of inflammatory cascades may profoundly influence the nature of antibody responses, the interplay between inflammatory and humoral (antibody) immune markers remains unclear. Thus, we explored the reciprocity between the humoral immune system and inflammation and assessed how external socio-demographic factors may influence these interactions.Methods: From the AMORIS cohort, 5,513 individuals were identified with baseline measurements of serum humoral immune (immunoglobulin G, A & M (IgG, IgA, IgM)) and inflammation (C-reactive protein (CRP), albumin, haptoglobin, white blood cells (WBC), iron and total iron-binding capacity) markers measured on the same day. Correlation analysis, principal component analysis and hierarchical clustering were used to evaluate biomarkers correlation, variation and associations. Multivariate analysis of variance was used to assess associations between biomarkers and educational level, socio-economic status, sex and age.Results: Frequently used serum markers for inflammation, CRP, haptoglobin and white blood cells, correlated together. Hierarchical clustering and principal component analysis confirmed the interaction between these main biological responses, showing an acute response component (CRP, Haptoglobin, WBC, IgM) and adaptive response component (Albumin, Iron, TIBC, IgA, IgG). A socioeconomic gradient associated with worse health outcomes was observed, specifically low educational level, older age and male sex were associated with serum levels that indicated infection and inflammation.Conclusions: These findings indicate that serum markers of the humoral immune system and inflammation closely interact in response to infection or inflammation. Clustering analysis presented two main immune response components: an acute and an adaptive response, comprising markers of both biological pathways. Future studies should shift from single internal marker assessment to multiple humoral and inflammation serum markers combined, when assessing risk of clinical outcomes such as cancer.
Impact of a multimedia website with patient experiences of multiple sclerosis (PExMS) on immunotherapy decision-making: study protocol for a pilot randomised controlled trial in a mixed-methods design
Authors: Barabasch Anna , Riemann-Lorenz Karin , Kofahl Christopher , Scheiderbauer Jutta , Eklund Desiree , Kleiter Ingo , Kasper Jürgen , Köpke Sascha , Lezius Susanne , Zapf Antonia , Rahn Anne Christin , Heesen Christoph 
Published: December 2020

Abstract
Background: A variety of management options (e.g. immunotherapies, lifestyle interventions, and rehabilitation) are available for people with relapsing-remitting multiple sclerosis (RRMS). Besides coping with the diagnosis, people with MS (pwMS) have to make complex decisions such as deciding about immunotherapies. In addition to factual information, reports of patient experiences (PEx) may support patients in decision-making. The added value of PEx in decision-making is not clear and controlled studies are rare. Therefore, systematic methods are necessary to develop and analyse PEx. As there are no evaluated PEx for MS in Germany, we are currently creating a website presenting PEx structured according to topics and illustrated by video, audio and text files. We aim to determine the feasibility of an intervention using PEx and evaluate whether PEx may help pwMS in their immunotherapy decision-making processes as a supplement to evidence-based information.Methods: This project will follow the Medical Research Council framework for development and evaluation of complex interventions. After the development of a website with PEx, a randomised controlled pilot trial (pilot RCT) will be conducted in 2-3 MS centres, clinics or rehabilitation centres including 55 pwMS and accompanied by a process evaluation. Patients with a RRMS diagnosis considering immunotherapy are eligible. The primary outcome is decision self-efficacy. Secondary outcomes include preparation for decision-making, decisional conflict, risk knowledge, confidence in active participation, affective forecasting, social support and self-reported impact of eHealth on its users. Participants will be randomly assigned either to i) an intervention group with a four weeks access to an evidence-based patient information resource and the PExMS-website as an adjunct or to ii) the control group with access to evidence-based information alone. A 6-members advisory panel involving representatives of pwMS, researchers, and neurologists, who accompany the whole project will mentor this pilot RCT.Discussion: The intervention was developed with systematic methods, created with active patient involvement and in critical appraisal by an expert advisory panel. The study is innovative as it contributes to the controversial evidence on the use of PEx in the context of evidence-based patient information.
Explanation and Prediction of Clinical Data with Imbalanced Class Distribution Based on Pattern Discovery and Disentanglement
Authors: Zhou Peiyuan , Wong Andrew K.C. 
Published: December 2020

Abstract
Background: Statistical data analysis, especially the advanced machine learning (ML) methods, have attracted considerable interest in clinical practices. We are looking for interpretability of the diagnostic/prognostic results that will bring confidence to doctors, patients and their relatives in therapeutics and clinical practice. When datasets are imbalanced in diagnostic categories, we notice that the ordinary ML methods might produce results overwhelmed by the majority classes diminishing prediction accuracy. Hence, it needs methods that could produce explicit transparent and interpretable results in decision-making, without sacrificing accuracy, even for data with imbalanced groups. Methods: In order to interpret the clinical patterns and conduct diagnostic prediction of patients with high accuracy, we develop a novel method, Pattern Discovery and Disentanglement for Clinical Data Analysis (cPDD), which is able to discover patterns (correlated traits/indicants) and use them to classify clinical data even if the class distribution is imbalanced. In the most general setting, a relational dataset is a large table such that each column represents an attribute (trait/indicant), and each row contains a set of attribute values (AVs) of an entity (patient). Compared to the existing pattern discovery approaches, cPDD can discover a small succinct set of statistically significant high-order patterns from clinical data for interpreting and predicting the disease class of the patients even with groups small and rare.Results: Experiments on synthetic and thoracic clinical dataset showed that cPDD can 1) discover a smaller set of succinct significant patterns compared to other existing pattern discovery methods; 2) allow the users to interpret succinct sets of patterns coming from uncorrelated sources, even the groups are rare/small; and 3) obtain better performance in prediction compared to other interpretable classification approaches. Conclusions: In conclusion, cPDD discovers fewer patterns with greater comprehensive coverage to improve the interpretability of patterns discovered. Experimental results on synthetic data validated that cPDD discovers all patterns implanted in the data, displays them precisely and succinctly with statistical support for interpretation and prediction, a capability which the traditional ML methods lack. The success of cPDD as a novel interpretable method in solving the imbalanced class problem shows its great potential to clinical data analysis for years to come.
Leadership, governance and management for improving district capacity and performance: the case of USAID Transform: Primary Health Care
Authors: Desta Binyam Fekadu , Abitew Azeb , Beshir Ismael Ali , Argaw Mesele Damite , Abdlkader Sualiha 
Published: December 2020

Abstract
Background: Primary health care (PHC) in Ethiopia serves as the main entry point for preventive, promotive and curative health services. The district health office is responsible for the planning, implementation and evaluation of all district health activities. In addition, district health offices manage service delivery facilities working on provision of PHC – primary hospitals, health centers and health posts. As the leader of the health care system tier, district health management must ensure direction, alignment and commitment within teams and organizations and make sure that achievements are consistent with the vision, values and strategy of the organization. USAID Transform: Primary Health Care provides diverse support to improve district health manager competencies including in-service trainings followed by planning and implementation of performance improvement projects and on-the-job mentoring and support. Methods: This study was conducted to compare district level capacity and performances between leadership, management and governance (LMG) and non-LMG districts. Project outcome monitoring data that shows the performance of districts was collected from 284 districts from January to December 2019. The study was carried out using a comparative-cross sectional study design, which assessed and compared district health office level indicators. Districts were classified into two categories: LMG and non-LMG districts. The study compared data from 94 LMG and 190 non-LMG districts. Propensity score matching was used to control the effect of differences between LMG and non-LMG districts. Results: Results of the independent samples t-test revealed that LMG districts scored better average performances of 61.8±121.45 standard deviation (SD) compared to non-LMG districts 56.89±110.39 SD, with t (282243) = -3.407317 and p < 0.001, two-tailed. The difference of 4.9 percentage unit in the average performance indicated a statistically significant difference between the LMG and non-LMG districts. Conclusion: District level leadership development program contributes to improving district capacity, structure and management practices, and quality of care.