bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Systematic Review of the Psychosocial Factors Associated with Pain in Children with Juvenile Idiopathic Arthritis
Authors: Brandelli Yvonne N , Chambers Christine T , Mackinnon Sean P , Wildeboer Emily M , Parker Jennifer A , Huber Adam M , Stinson Jennifer N , Wilson Jennifer P , Piccolo Olivia 
Published: December 2022

Abstract
Background: Pain is one of the most frequently reported experiences amongst children with Juvenile Idiopathic Arthritis (JIA); however, the management of JIA pain continues to be a challenge. As pain is a multidimensional experience that is influenced by biological, psychological, and social factors, the key to effective pain management lies in understanding these complex relationships. The objective of this study is to systematically review the literature on psychosocial factors 1) associated with and 2) predictive of later JIA pain intensity, frequency, and sensitivity in children 0-17 years of age.
Methods: The Joanna Briggs Institute methodology for etiology and risk and Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) statement guided the conduct and reporting of this review. Terms related to pain and JIA were searched in English without date restrictions across various databases (PubMed, CINAHL, PsycINFO, Embase, Scopus, and the Cochrane Central Register of Controlled Trials) in September 2021. Two independent reviewers identified, extracted data from, and critically appraised the included studies. Conflicts were resolved via consensus.
Results: Of the 9,929 unique studies identified, 61 were included in this review and reported on 516 associations. Results were heterogeneous, likely due to methodological differences and moderate study quality. Results identified significant associations between pain and child and parent internalizing symptoms, child and parent cognitions (e.g., more pain beliefs, lower self-efficacy), worse social functioning, and lower well-being and health-related quality of life. Prognostically, studies had 1-to-60-month follow-up periods. Internalizing symptoms and lower well-being were predictive of later pain reports, however bidirectional relationships were also identified. Cognitions (i.e., fewer beliefs of harm, disability, and lack of control) were associated with reduced pain over time.
Conclusions: Despite the heterogeneous results, this review highlights important associations between psychosocial factors and JIA pain. Clinically, this information supports an interdisciplinary approach to pain management. More specifically, it informs the role of psychosocial supports and provides information to better optimize JIA pain assessments and interventions. It also identifies a need for high quality studies with larger sample sizes and more complex and longitudinal analyses to understand factors that impact the pain experience in children with JIA.
Registration: PROSPERO CRD42021266716
The impact of specific pulmonary arterial hypertension therapy on cardiac fluorodeoxyglucose distribution in PET/MRI hybrid imaging – follow up study
Authors: Kazimierczyk Remigiusz , Szumowski Piotr , Nekolla Stephan G. , Malek Lukasz A. , Blaszczak Piotr , Hladunski Marcin , Sobkowicz Bozena , Mysliwiec Janusz , Kaminski Karol A. 
Published: December 2022

Abstract
Background
PET/MRI hybrid imaging in pulmonary arterial hypertension (PAH) provides important prognostic information identifying patients who might benefit from early therapy escalation, as right ventricle (RV) metabolic alterations are linked with hemodynamics and might precede clinical deterioration. Now, we hypothesize that adequate PAH therapy escalation may result in reversal of unfavourable increased glucose uptake of RV, which is associated with improved prognosis.
Methods
Out of twenty-six initially clinically stable PAH patients who had baseline PET/MRI scans, twenty (49.9±14.9 years) had second PET/MRI after 24 months. SUVRV/SUVLV ratio was used to estimate and compare cardiac glucose uptake. Occurrences of clinical endpoints (CEP), defined as death or clinical deterioration were assessed during 48-month follow-up from baseline.
Results
In first 24 months of observation sixteen patients had CEP and needed PAH therapy escalation. At follow-up visits we observed significant improvement of RV ejection fraction (45.1±9.6% to 52.4±12.9%, p=0.01), mean pulmonary artery pressure (50.5±18.3 to 42.8±18.6 mmHg, p=0.03), and SUVRV/SUVLV, which tended to decrease (mean change -0.20±0.74). Patients with baseline SUVRV/SUVLV value higher than 0.54 had worse prognosis in 48 months observation (log-rank test, p=0.0007); follow up SUVRV/SUVLV >1 predicted CEP in the following 24 months, regardless of previously escalated treatment.
Conclusions
PAH therapy escalation may influence RV glucose metabolism, what seems to be related with patients’ prognosis. PET/MRI assessment may predict clinical deterioration regardless of previous clinical course, however its clinical significance in PAH requires further studies. Importantly, even mild alterations of RV glucose metabolism predict clinical deterioration in long follow-up.
Clinical Trial Registration: ClinicalTrials.gov, NCT03688698, 05/01/2016, https://clinicaltrials.gov/ct2/show/study/NCT03688698?term=NCT03688698&draw=2&rank=1
BMP4 reprograms glucose metabolism in hepatocellular carcinoma (HCC) cells by upregulating glycogen synthesis through the SMAD/SLC2A1 (GLUT1) signaling axis
Authors: Zhong Jiamin , Tian Luyao , Gou Yannian , Zhao Piao , Dong Xiangyu , Guo Meichun , Zhao Guozhi , Li Aohua , Hao Ailing , He Tong-Chuan , Fan Jiaming 
Published: December 2022

Abstract
Background
Excessive hepatic glycogen accumulation benefits tumorigenesis and cancer cell survival. We previously reported that BMP4 was elevated in Hepatocellular carcinoma (HCC) and augmented HCC cell survival under hypoxia and hypoglycemia conditions by promoting the glycolysis pathway. However, the mechanism underlying BMP4’s effect on glucose metabolism remains elusive. In this study, we investigated the effect of BMP4 on hepatic glycose metabolism through glucose transporter SLC2A1 (GLUT1) in HCC cells.
Methods
The expression of BMP4 and SLC2A1 were acquired by analyzing the TCGA-LIHC dataset, as well as by immunohistochemical analysis of the 40 pairs of human HCC samples and para-tumor tissues. Gene expressions were detected by qPCR, immunoflurorescence staining, and Western blotting. Overexpression and silencing of BMP4 were accomplished through adenoviruses Ad-B4 and Ad-siB4 infection. Hepatic glycogen was detected by PAS staining. SLC2A1(GLUT1) function was blocked by the inhibitor BAY-876. ChIP assay was used to determine the binding of SMADs to the promoter region of SLC2A1 in HCC cells. Lastly, the in vivo effect of BMP4-regulated SLC2A1 on HCC tumor growth was assessed in a xenograft model of HCC.
Results
The elevated expression of BMP4 in HCC tumor tissues was highly correlated with hepatic glycogen accumulation in clinical samples. SLC2A1 was highly expressed in HCC tumor tissue and correlated with clinical stage and prognosis. Exogenous BMP4 augmented glycogen accumulation and up regulated the expression of glycogen synthesis-related genes in Huh7 and HepG2 cells, both of which were effectively blunted by SLC2A1inhibitor BAY-876. In mechanism, BMP4 activated SMAD5 to regulate the promoter of SLC2A1to enhance its expression. The in vivo xenograft experiments revealed that BMP4 promoted glycogen accumulation and tumor growth, which were effectively diminished by BAY-876.
Conclusion
These results demonstrate that BMP4 can reprogram hepatic glycogen metabolism and promote tumor growth of HCC cells through SMAD/SLC2A1 axis, which may be exploited as novel therapeutic targets for HCC treatment.
Development and validation of a prediction model for iron status: evidence from a large U.S. cohort of women
Authors: Von Holle Ann , O'Brien Katie M. , Janicek Robert , Weinberg Clarice R. 
Published: December 2022

Abstract

Serum iron levels can be important contributors to health outcomes, but it is not often feasible to rely on blood-based measures for a large epidemiologic study. Predictive models that use questionnaire-based factors such as diet, supplement use, recency of blood donation, and medical conditions could potentially provide a noninvasive alternative for studying health effects associated with iron status. We hypothesized that a model based on questionnaire data could predict blood-based measures of iron status biomarkers. Using iron (mg/dL), ferritin (mg/dL), and transferrin saturation (%) based on blood collected at study entry, in a subsample from the U.S.-wide Sister Study (n = 3,171), we developed and validated a prediction model for iron with multivariable linear regression models. Model performance was weak, with R2less than 0.10 for serum iron and transferrin saturation, but better for ferritin, with an R2of 0.13 in premenopausal women and 0.19 in postmenopausal women. When menopause was included in the predictive model for the sample, the R2was 0.31 for ferritin. Internal validation of the estimates indicated some optimism present in the observed prediction model, implying there would be worse performance when applied to new samples from the same population. Serum iron status is hard to assess based only on questionnaire data. Reducing measurement error in both the exposure and outcome may improve the prediction model performance, but environmental heterogeneity, temporal variation, and genetic heterogeneity in absorption and storage may contribute substantially to iron status.
Title: Dust and Antimicrobial Resistance (AMR) in Ghanaian Homes: developing home cleaning intervention through community engagement to reduce infections and AMR.
Authors: Tsekleves Emmanuel , De Souza Dziedzom , Pickup Roger , Ahorlu Collins , Darby Andy 
Published: December 2022

Abstract
Background
Globally Antimicrobial Resistance (AMR) constitutes a health crisis, particularly in developing countries, where infectious disease commonly leads to fatalities. Personal and environmental hygiene form the best ways of reducing home infections thus decreasing the need for antibiotics and consequently diminishing AMR. Despite this being an obvious step, our understanding of cleaning in the home and possible interventions on home cleaning are limited.
Methods
We combined design and microbiology methods in an innovative mixed-method approach. A traditional survey design (n = 240), a design ethnography (n = 12), a co-design workshop and a pre-intervention microbiological dust sample analysis was undertaken to provide insights for codesign workshops in which new cleaning practices might be developed to minimise any AMR bacteria present in the home environments located in the Greater Accra Region of Ghana.
Results
Microbiological analysis of household dust showed that 36.6% of bacterial isolates detected were found to carry at least one resistance to the panel of antibiotics tested. Four scenarios were generated from an economic segmentation of the survey data. 50 ethnographic insights were ‘presented’ and descriptions of 12 bacteria species that showed resistance to one or more antibiotics (representing 176 bacterial isolates that showed resistance to one or more antibiotics found in the dust samples) were presented to the participants in a codesign workshop. An intervention, a new regime of cleaning practices agreed through the co-design workshop and practiced for thirty days, was made in (n = 7) households.
Conclusion
The high prevalence of multidrug resistance observed in this study indicate the need for antibiotics surveillance program, not only in hospital settings but also in the home environment. There is, thus, an urgent need for targeting of interventions at the household level. Activating knowledge through community engagement in the research helps in increasing public perception and breaking down the scientist-public barrier.
A multi-site study of a new cement composite brick with partial cement substitutes and waste materials
Authors: Jackowski Mateusz , Malek Marcin 
Published: December 2022

Abstract

The building industry is a major contributor to climate change since it emits massive amounts of greenhouse gases. As a result of this research, a cement composite brick with partial cement substitutes and waste material was developed. This novel building material holds a lot of promise for partly solving this problem. The fresh formula mixture and hardened concrete were tested for physical, mechanical, and thermal properties to ensure that the assumptions were true. In this study, 20 recipes that led to the composition of mentioned brick were presented as well as its final recipe. The results of the fresh mixture were assessed in terms of initial and final setting time, pH, and air content. The compressive strength of the brick developed with the modified formula increased by 22% compared to the reference sample. Also, higher mechanical values were obtained for the splitting strength and flexural strength, which increased by 14% and 32%, respectively, compared to plain concrete. The results of the SEM images showed an even distribution of the fibers in the composite matrix and a crack-free structure. Moreover, the results of the image analysis confirmed the failure character consistent with traditional concretes in the case of compression and the lack of split into two independent samples in the case of bending. Therefore, the new composite brick has great potential to be used as a primary replacement for traditional concrete masonry in civil engineering projects.
Prevalence, modifiable and risk factors of depression, anxiety and stress (DASS) among elders in the northwest of Iran
Authors: Raeisvandi Abouzar , Amerzadeh Mohammad , Hajiabadi Fatemeh , Hosseinkhani Zahra 
Published: December 2022

Abstract
Background
Depression, anxiety, and stress are among the most common mental health disorders of the elderly that affect the health of individuals and society. Considering the growing trend of the elderly population in Iran, this study aimed to determine the prevalence of these disorders and to identify the factors affecting them in the elderly.
Methods
We conducted this cross-sectional study on 301 elderly people referred to Qazvin health centers. Data was collected using the Depression, Anxiety, Stress Scale 21 (DASS-21) questionnaire and analyzed through univariate and multivariate linear regression tests with the interaction between variables in STATA Version 14 software. A P-value of less than 0.05 was significant.
Results
The prevalence of depression was 45.5%, anxiety 35.5%, and stress 40.2%. 22.9%, 7.9%, and 14.3% of people had severe and very severe levels of depression, anxiety, and stress. The variables of age, comorbidity, life status and job status had a significant relationship with the DASS score (p < 0.05). There was an interaction between the variables of comorbidity and income status influencing the DASS score (β = 0.68, 95% CI 0.15, 1.22).
Conclusion
The prevalence of depression, anxiety and stress in the elderly was high, indicating the inappropriate status of their mental health. Because of the interaction between comorbidity and the income status of the elderly, significant changes in lifestyle, and weakening the support structure of families for the elderly, it is necessary to plan and pay more attention to the needs and problems of these people.
Multivariate time series prediction of Covid-19 daily new cases in Indonesia based on Deep Learning: Unlocking the role of effective reproduction number (Rt)
Authors: Mauritsius Tuga , Jayadi Riyanto , Arifin Samsul 
Published: December 2022

Abstract
To date, COVID-19 and its variants have been among the greatest hindrances for humanity. This disease is spreading rapidly and almost all parts of the world are currently exposed to it. The ability to understand and simultaneously predict the dynamics of daily confirmed cases of this disease is essential to prevent and mitigate the impact of the pandemic. This study investigates the use of Deep Learning (DL), including Deep Feedforward Neural Networks (DFNN), Long Short-Term Memory (LSTM), a one-dimensional convolutional neural network (CONV1D), and Gated Recurrent Units (GRU), to predict daily confirmed cases of Covid-19 in Indonesia by taking into account as many as 25 variables (predictors) as inputs. Variable filtering was also performed to identify the predictors with the best weight. Extreme Gradient Boosting (XGBoost) regression is used for this purpose. Some statistical analyses were also carried out to increase our understanding of the data before modelling. The performance of the algorithm was assessed using several metrics, including Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Scaled Error (MASE). MASE is a measure of MAE relative to the baseline model. The results showed that DL using two predictors, the number of daily confirmed cases and the Rt (effective reproduction number) value, had the highest performance and was able to predict the number of daily confirmed cases 13 days ahead. Adding more variables deteriorates DL performance.
High-Performance Surface Plasmon Resonance Fiber Sensor Based on Cylindrical Vector Modes
Authors: Sharif Vahid , Pakarzadeh Hassan 
Published: December 2022

Abstract
Cylindrical vector modes with azimuthal polarization and low transmission loss are proposed for the first time to be utilized in a novel design of a surface plasmon resonance (SPR) sensor based on a circular photonic crystal fiber (C-PCF). A C-PCF with a ring of air holes in the cladding is designed where a gold layer with thickness of 44nm is coated on the outer cladding surface. The optimal geometric parameters are determined using the finite-element method (FEM) for a high-quality TE01 mode and high sensitivity of the sensor. The proposed SPR sensor shows high sensitivity for analyte refractive index (RI) ranging from na=1.29 to 1.34 over the wavelength range of 1400–2000 nm. It is expected that the proposed sensor can sense low concentrations of hemoglobin, lymphocytes and monocytes of red and white blood cells which are effective in diagnosing the progress of cancer tumors. The maximum sensitivity of 13800 nm/RIU is obtained for the analyte RI of na=1.33. The sensor resolution is of the order of 10− 6 and the amplitude sensitivity reaches its maximum of 2380 RIU− 1 at na=1.30 which is the highest value ever reported. Our proposed sensor shows high sensitivity and simultaneously simple design with high performance.
A hybrid approach to predict COVID-19 cases using neural networks and inverse problem
Authors: Paul Subhendu , Lorin Emmanuel 
Published: December 2022

Abstract
We derive a novel hybrid approach, a combination of neural networks and inverse problem, in order to forecast COVID-19 cases, and more generally any infectious disease. For this purpose, we extract a second order nonlinear differential equation for the total confirmed cases from a SIR-like model. The latter is the cornerstone of the present study which allows to rigorously simplify the construction of the time-dependent epidemiological parameters without solving a system of differential equations. The neural network and inverse problems are used to compute the trial functions for total cases and the model parameters, respectively. The number of suspected and infected individuals can be found using the trial function of total confirmed cases. We divide the time domain into two parts, training interval (first 365/395 days) and test interval (first 366 to 395/ 396 to 450 days), and train the neural networks on the preassigned training zones. To examine the efficiency and effectiveness, we apply the proposed method to Canada, and use the Canadian publicly available database to estimate the parameters of the trial function involved with total cases. The trial functions of model parameters show that the basic reproduction number was closed to unity over a wide range, the first 100 to 365 days of the current pandemic in Canada. The proposed prediction models, based on the influence of previous COVID-19 cases and social economic policy, show excellent agreement with the data. The test results reveal that the single path prediction can forecast a period of 30 days, and the prediction using previous social and economical situation can forecast a range of 55 days.
Prognostic Value of LINC01600 and CASC15 as Competitive Endogenous RNAs in Lung Adenocarcinoma
Authors: Zhou Fangbin 
Published: December 2022

Abstract
Long non-coding RNAs (lncRNAs) can directly or indirectly regulate gene expression through interacting with microRNAs (miRNAs). Competitive Endogenous RNAs (ceRNAs) render the roles of lncRNAs more complicated in the process of tumor occurrence and progression. However, the prognostic value of lncRNAs as potential biomarkers and their functional roles as ceRNAs have not been clearly described for lung adenocarcinoma (LUAC). In the present study, the aberrant expression profiles of lncRNAs and miRNAs were analyzed at cBioPortal by interrogating LUAD dataset from The Cancer Genome Atlas (TCGA) database with 517 tissue samples. A total of 92 lncRNAs and 125 miRNAs with highly genetic alterations were identified. Further bioinformatics analysis was performed to construct a LUAD-related lncRNA-miRNA-mRNA ceRNA network, which included 24 highly altered lncRNAs, 21 miRNAs and 142 mRNAs. Some key lncRNAs in this network were subsequently identified as LUAD prognosis-related, and of those, LINC01600 and CASC15 both performed the potential prognostic characteristics with LUAD regarding OS and recurrence. Comprehensive analysis indicated that the expression of LINC01600 was significantly associated with KRAS mutation and lymph node metastasis, and CASC15 and LINC01600 were significantly tended towards co-occurrence, which may be due to the similarity of genes co-expressed by these two lncRNAs. Our findings provided novel insight into better understanding of ceRNA regulatory mechanisms in the pathogenesis of LUAD and facilitated the identification of potential biomarkers for prognosis.