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Breast cancer (BC) is the most frequently diagnosed cancer among women in all the populations of the world. Although the BC mortality rate has declined, resistance to treatment is still a significant challenge for patient survival. Various cellular signaling pathways, such as Wnt and Rho/GTPase have been linked to the development, migration, and metastasis of BC, and also in treatment resistance mechanisms. Some studies have shown an association between two important cellular pathways, Wnt and Rho/GTPase, in cytoskeleton activation and cancer invasion. However, their involvement in BC has received little attention. This review summarizes the Wnt and Rho/GTPases signaling pathway functions, and also the crosstalk between these pathways in the progression, metastasis, and drug resistance mechanisms in BC. Considering the signaling pathways involved in BC tumorigenesis, future studies will need to investigate possible molecular interventions and new opportunities for the development of personalized strategies for BC treatment in order to improve overall outcomes. [Display omitted] • GTPase and Wnt pathways are involved pathways in various cell process that any disruption in those can lead to tumorigenesis. • Mutation in members of GTPase pathways caused uncontrolled cell division and cancer. • Both of wnt and GTPase pathways are involved in drug resistance in breast cancer. • There are a strong crosstalk between Wnt and GTPase pathways. • Understanding the relationship between the two signaling pathways suggests common therapeutic targets that can be more effective. [ABSTRACT FROM AUTHOR]

Solid oxide fuel cells (SOFCs) are promising clean energy technology to produce electricity with zero emission, fuel diversity, and high electrical efficiency. The dynamic behavior of SOFC systems is affected by exogenous inputs and complex internal multi-physics phenomena. This paper introduce a hybrid neural network to build a comprehensive predictive model for SOFC performance. The proposed algorithm includes residual blocks of temporal dilated convolution that are designed to hierarchically extract a high-level hidden abstraction of the temporal dependencies from a sequence of historic output, which contains paramount information about the SOFC dynamics. The influence of the exogenous inputs is extracted through a shallow neural network. The fusion of learned features is then fed to a fully connected network for performance prediction, representing a nonlinear autoregressive exogenous scheme. To train and evaluate the model's performance, experimental data from lab-scale SOFCs under various dynamic operations was collected. Having been validated over diverse operating conditions, the prediction performance of the proposed framework is benchmarked. The results demonstrate our model can more precisely capture the SOFC dynamical behavior and is computationally more efficient than the baseline models. The parallelism of the model holds the potential for real-time control, diagnostics, and optimization of SOFC systems. • A novel two-channel parallel neural network for predicting the performance of solid oxide fuel cells. • Effectively captures dynamics induced by both exogenous inputs and internal reactions. • Validated with diverse experimental data from real tubular fuel cells. • Compared with baseline dynamic neural networks for one-step-ahead prediction tasks. [ABSTRACT FROM AUTHOR]

Transfer learning (TL) is an effective method for minimizing modeling efforts and data requirements for diverse energy systems. This paper presents use of TL for different hydrogen-fueled solid oxide fuel cells (SOFCs) types (tubular vs planar) with different manufacturers (UAlberta vs Elcogen) and output power ranges. Three different single tubular cells were fabricated and tested under 18 operating conditions. In addition, a planar single cell was tested under 10 operating conditions. The data gathered from the first tubular cell (A1) was used to train a deep neural network (DNN) model with the cell voltage as the output. Then, the DNN model was transferred from this source domain to three different target domains. Two tubular cells with different cell properties such as electrolyte thickness, electrodes' thickness and porosity, and one planar cell with different microstructural properties and different physical layout were the target domains. Fine-tuning was used for TL, and the effect of different normalization strategies and different amounts of fine-tuning data were compared. The developed DNN was able to capture the nonlinear part in current density–voltage (J–V) curves from the rich dataset available for training. The DNN model trained on cell A1 achieved a high prediction accuracy (R2 = 0.99). Training the DNN in the source domain and fine-tuning the trained network using 10% of target data results in, on average, 85% less training time. This results in a DNN model developed for the target domain, which is now as accurate as the model developed for the source domain. The applied technique reduces the computational cost by 85% and the data requirement by 90% for developing predictive models for SOFCs. [Display omitted] • Collected datasets from three tubular cells and one planar cell. • 23,820 data samples as open-source for the research community. • Designed a DNN model to predict SOFC output voltage with less than 1% error. • Data requirement is reduced by 90% for SOFC data-driven model via transfer learning. • Reduction of training time by 85% compared to the case without a transferred model. [ABSTRACT FROM AUTHOR]

Today, the wide utilization of triazole fungicides due to environmental damage and its side effects has raised global concern. Thus, in this investigation, polyacrylonitrile/MnCo-layered double hydroxides nanofiber was synthesized and applied as an effective and novel adsorbent at thin-film solid-phase micro-extraction technique for the quick and concurrent extraction of five triazole fungicides in fruit and vegetable samples before quantitative analysis by high-performance liquid chromatography-ultraviolet. The incorporation of MnCo-layered double hydroxides with porous structure and abundant functional groups in a polymer medium improves the extraction efficiency of nanofibers owing to hydrogen bonding and π-π interactions formed between analytes and synthesized nano-adsorbent. Various important elements that affect the extraction efficiency of the intended analytes were optimized using a time-variable approach. Under the optimum conditions, the limit of detection and quantification range from 0.1 to 0.15 and 0.3–0.5 ng mL−1, respectively. Incorporation of the synthesized MnCo-LDH into the polymer network of PAN and the production of a novel, porous, rich in functional groups adsorbent, easy and fast synthesis by the electrospun device for the extraction of triazole fungicides in fruit and vegetable samples prior to identification by the HPLC-UV device. [Display omitted] • The MnCo-layered double hydroxides composite with a yolk-shell sphere structure. • Novel synthesis of polyacrylonitrile/MnCo-layered double hydroxides nanofibers. • Synthesized nanofibers have high absorption capacity with easy and fast synthesis. • Thin-film solid-phase micro-extraction/high-pressure liquid chromatography method. • Electrospun nanofibers with porous structure and abundant functional groups. [ABSTRACT FROM AUTHOR]

• CoFe-PBA@GO with a hollow-cube structure was synthesized. • CoFe-PBA@GO has functional groups and high absorption capacity and porosity. • Application of sorbent and method ecologically friendly in extraction of fungicides. • Triazole fungicide was identified and extracted using the PT-μSPE-HPLC-UV method. • Detection of trace amounts of triazole fungicides in fruit and vegetable samples. Today, the wide use of triazole fungicides due to environmental damage and its side effects has raised global concern. Hence, in this research, poly-vinyl alcohol/polyacrylic-acid/CoFe-PBA@GO electrospun nanofiber was synthesized and applied as effective, degradable, and novel adsorbent at pipette-tip microextraction (PT-μSPE) method for the rapid and concurrent extraction of five of triazole fungicides in fruit and vegetable samples prior to quantitative analysis by high-performance liquid chromatography-ultraviolet. The incorporation of CoFe-PBA@GO with superporous structure and abundant functional groups in a polymer medium improves the extraction efficiency of nanofibers due to hydrogen bonding and π-π interactions formed between analytes and synthesized nano-adsorbent. Various important elements that affect the extraction yield of the target analytes were optimized utilizing a time-variable approach. Under the optimum conditions, dynamic range was attained in the range of 0.3–900.0 ng/mL with correlation coefficients ≥ 0.999. The identification limit of the PT-μSPE-HPLC-UV method ranged from 0.1 to 0.3 ng/mL. [ABSTRACT FROM AUTHOR]

• Introducing a novel method to generate new flexible change point family of distributions. • Estimation via maximum likelihood, bootstrap and Bayesian. • Study some informational properties of proposed class from point of view of information theory. • Generating posterior samples using Gibbs sampling to obtain Bayes estimators and HPD intervals. • Simulation and modeling bladder cancer data. We define a new two-sided class F of distributions and a generalized class by compounding this class with a baseline distribution G. The new model is called the general two-sided F G class. Some of its properties are provided. The proposed model is specialized taking the Weibull and exponential for F and G , respectively. For a new specialized distribution, we obtain the survival, quantiles and hazard rate. We estimate the parameters by maximum likelihood, Bayesian and parametric and non-parametric bootstrap methods. We conduct a simulation study to check the accuracy of the estimators. The classic and Bayesian approaches are illustrated by means of a real data set. We show that the proposed model fits better than the generalized two-sided exponential and generalized transmuted two-sided exponential distributions. [ABSTRACT FROM AUTHOR]

The purpose of the present study was to determine the short and medium effects of dry needling (DN) on myofascial trigger points (MTrPs) in individuals with symptomatic hallux valgus (SHV). A total of 30 female volunteers, aged 25–60, with hallux valgus (HV) were randomly divided into two groups: DN group (n = 15) and control group (n = 15) who received sham dry needling. The outcome measures were pain intensity (Visual Analogue Scale, VAS), Foot Function Index (FFI), hallux valgus angle (HVA), and first metatarsophalangeal joint radiography. At the end of the intervention, the HVA showed a significant decrease in the case group (P < 0.001); however, statistically no significant difference was found in the pain intensity and foot function between the two groups (P > 0.05). These findings were maintained for a week and a month during follow-ups. According to the findings, dry needling can be recommended for improving first metatarsophalangeal joint alignment in the mild to moderate SHV individuals. [ABSTRACT FROM AUTHOR]

Abstract Background and purpose This systematic review and meta-analysis aimed to assess the effects of vitamin D supplements on indices of glycemic control [homeostatic model assessment-insulin resistance (HOMA-IR), hemoglobin A1C (HbA1C), fasting blood glucose (FBG), and quantitative insulin-sensitivity check index (QUICKI) and lipid profile in diabetic patients. Methods Eight databases were searched, for randomized controlled trials (RCTs) or cross-sectional and cohort studies that have been published up to December 2017. We used the comprehensive meta-analysis (CMA) software for all statistical analysis and used the I2 index for assessing heterogeneity. A p value of <0.05 was considered as statistically significant. Results We found 621 articles, and after the exclusion of ineligible publications, 82 studies remained to be assessed of which 37 were used for meta-analysis. Vitamin D supplementation was associated with a significant improvement in FBG (p = 0.001 and 95% CI: −0.526 to −0.136) and HbA1C (p = 0.003 and 95% CI: 1.719 to −0.361) in individuals with type 2 diabetes mellitus (T2DM); while in women with gestational diabetes mellitus (GDM) the reduction in FBG (p = 0.071 and 95% CI: −0.873 to −0.035) and HbA1C (p = 0.199 and 95% CI: 3.270 to 0.681) failed to reach statistical significance. Treatment with vitamin D supplements was associated with an improvement in HOMA-IR in pregnant diabetic women (p = 0.028 and 95% CI: 0.924 to −0.053) and for individuals with diabetes mellitus (p = 0.005 and 95% CI: 1.772 to −0.319). The pooled result of the cross-sectional meta-analysis indicated that serum vitamin D concentrations were significantly lower in diabetic patients than in healthy controls (p = 0.018 and 95% CI: 0.587 to −0.054). Conclusion This meta-analysis suggests that vitamin D supplementation improves indices of glycemic control (FBG, HOMA-IR, and HbA1C) in patients with diabetes mellitus. Hence, vitamin D supplements may be of potential therapeutic value in diabetic patients, as an adjuvant therapy along with other treatments. [ABSTRACT FROM AUTHOR]

Abstract Objectives Obesity and overweight are among the main causes of cardiovascular disease (CVD) mortality. Dyslipidemia, fatty liver index, is strongly related to CVD. Vitamin E as an antioxidant protects the hepatic cells against oxidative stress and prevents fatty liver disease. The aim of the current study is to evaluate the relationship between anthropometric parameters and fasted lipid profile with serum vitamin E levels. Study design A randomized trial was designed based on data from the Mashhad stroke and heart atherosclerotic disorders (MASHAD: 2010–2020). Methods 363 CVD subjects (173 males and 190 females) was selected at random, among 9704 subjects in three regions of Mashhad, northeast of Iran to investigate the specific correlations among their serum vitamin E, lipid profile (TG, HDL-C, LDL-C and TC), and anthropometric features (height, weight, BMI, hip and waist circumferences. Result The results indicated the significant relationships between vitamin E, and fasting serum lipid profile in subjects. Serum vitamin E was negatively correlated to TC, TG, and LDL-C and positively related to HDL-C. Also, statistically negative correlations were found between vitamin E and anthropometric parameters (weight, waist and hip circumference, middle Arm, and Systolic Blood Pressure). Moreover, vitamin E ratios such as vitamin E/(TC + TG) and vitamin E/TC values as standardized vitamin E, had significant negative correlation with BMI, the whole of anthropometric parameters, and dyslipidemia risk factors including TC, TG and LDL-C. Conclusion We found that vitamin E profile was significantly lower in the dyslipidemia subjects. It is generally suggested that vitamin E monitoring might be used as a useful prognostic and therapeutic agent in dyslipidemia disorder. [ABSTRACT FROM AUTHOR]