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The causal mechanistic relationships between Essure® and adverse effects are unclear, but corrosion in the in-vivo environment with release of metal ions may be suspected. Here we evaluated the concentrations of nickel (Ni), chromium (Cr) and tin (Sn) in the peritoneal fluid (PF) and in the fallopian tube (FT) during laparoscopic Essure® removal compared to a control group.

Ni, Cr and Sn concentrations were determined in the PF and FT from two groups(group A symptomatic patients with Essure®) vs group B (control group without Essure®) by Inductively Coupled Plasma Mass Spectrometry analysis. Obeticholic mw Correlation between metal elements concentrations and reported pre-operative symptoms was also investigated.

There were 131 patients in group A vs 92 control patients in group B. The concentrations of Cr and Ni in PF between both groups were significantly different (p<0.0001) while there was no statistical difference for Sn (p=0.58). There was also a significantly higher concentration in the FT for the 3 metal elements in group A than in group B (p<0.0001). There were differential dynamics of the levels of metal elements based on the length of time between the placement and removal of Essure®.

There was a chronic exposure to metal elements in symptomatic patients with Essure® raising the question of the relationship between adverse effects and these potential toxic metals.

There was a chronic exposure to metal elements in symptomatic patients with Essure® raising the question of the relationship between adverse effects and these potential toxic metals.DNA-protein interaction is a critical biological process that performs influential activities, including DNA transcription and recombination. DBPs (DNA-binding proteins) are closely associated with different kinds of human diseases (asthma, cancer, and AIDS), while some of the DBPs are used in the production of antibiotics, steroids, and anti-inflammatories. Several methods have been reported for the prediction of DBPs. However, a more intelligent method is still highly desirable for the accurate prediction of DBPs. This study presents an intelligent computational method, Target-DBPPred, to improve DBPs prediction. Important features from primary protein sequences are investigated via a novel feature descriptor, called EDF-PSSM-DWT (Evolutionary difference formula position-specific scoring matrix-discrete wavelet transform) and several other multi-evolutionary methods, including F-PSSM (Filtered position-specific scoring matrix), EDF-PSSM (Evolutionary difference formula position-specific scoring matrix), PSSM-DPC (Position-specific scoring matrix-dipeptide composition), and Lead-BiPSSM (Lead-bigram-position specific scoring matrix) to encapsulate diverse multivariate features. The best feature set from the features of each descriptor is selected using sequential forward selection (SFS). Further, four models are trained using Adaboost, XGB (eXtreme gradient boosting), ERT (extremely randomized trees), and LiXGB (Light eXtreme gradient boosting) classifiers. LiXGB, with the best feature set of EDF-PSSM-DWT, has attained 6.69% and 15.07% higher performance in terms of accuracies using training and testing datasets, respectively. The obtained results verify the improved performance of our proposed predictor over the existing predictors.A covered stent has been used to treat carotid artery stenosis to reduce the chance of embolization, as it offers improved performance over bare-metal stents. However, membrane infolding of covered stents can affect efficiency and functionality for treating occlusive disease of first-order aortic branches. In order to mitigate the degree of infolding of the stent once it was re-expanded, we proposed a new coating method performed on the pre-crimped stent. A systematic study was carried out to evaluate this new coating technique a) in vivo animal testing to determine the degree of membrane infolding; b) structural finite element modeling and simulation were used to evaluate the mechanical performance of the covered stent; and c) computational fluid dynamics (CFD) to evaluate hemodynamic behavior of the stents and risk of thrombosis after stent deployment. The degree of infolding was substantially reduced as demonstrated by the in vivo deployment of the pre-crimped stent compared to a conventional dip-coated stent. The structural analysis results demonstrated that the membrane of the covered stent manufactured by conventional dip-coating resulted in a large degree of infolding but this could be minimized by our new pre-crimped coating method. CFD studies showed that the new coating method reduced the risk of thrombosis compared to the conventional coating method. In conclusion, both simulation and in vivo testing demonstrate that our new pre-crimped coating method reduces membrane infolding compared with the conventional dip-coating method and may reduce risk of thrombosis.The range of effectiveness of the novel corona virus, known as COVID-19, has been continuously spread worldwide with the severity of associated disease and effective variation in the rate of contact. This paper investigates the COVID-19 virus dynamics among the human population with the prediction of the size of epidemic and spreading time. Corona virus disease was first diagnosed on January 30, 2020 in India. From January 30, 2020 to April 21, 2020, the number of patients was continuously increased. In this scientific work, our main objective is to estimate the effectiveness of various preventive tools adopted for COVID-19. The COVID-19 dynamics is formulated in which the parameters of interactions between people, contact tracing, and average latent time are included. Experimental data are collected from April 15, 2020 to April 21, 2020 in India to investigate this virus dynamics. The Genocchi collocation technique is applied to investigate the proposed fractional mathematical model numerically via Caputo-Fabrizio fractional derivative. The effect of presence of various COVID parameters e.g. quarantine time is also presented in the work. The accuracy and efficiency of the outputs of the present work are demonstrated through the pictorial presentation by comparing it to known statistical data. The real data for COVID-19 in India is compared with the numerical results obtained from the concerned COVID-19 model. From our results, to control the expansion of this virus, various prevention measures must be adapted such as self-quarantine, social distancing, and lockdown procedures.The growth of the fetus can be effectively monitored by measuring the fetal head circumference (HC) in ultrasound images. Moreover, it is the key to assessing the fetus's health. Ultrasound fetal head image boundary is blurred. The ultrasound sound shadow results in a partial absence of the skull in the image. The amniotic fluid and uterine wall form a structure similar to the head texture and grayscale. All these factors result in challenges to ultrasound fetal head edge detection. The new convolutional neural network (CNN) named GAC Net was proposed in this paper, which can effectively solve the above problems. GAC Net is an end-to-end network model constructed by the encoder and decoder. In order to suppress the interference of ultrasound image quality defects on the HC measurement, the graph convolutional network (GCN) module was added to the connection channel between the encoder and the decoder. The new attention mechanism enhanced the network's ability to perceive border areas. Experiments were performed on the HC18 fetal head ultrasound image data set. The following objective evaluation indicators were calculated, including the Hausdorff distance (HD), the absolute difference (AD), the difference (DF), and the Dice similarity coefficient (DSC) of head circumference. Experimental results showed that GAC-Net had an HD of 1.22 ± 0.71 mm, an AD of 1.75 ± 1.71 mm, a DF of 0.19 ± 2.32 mm, and a DSC of 98.21 ± 1.16%. The overall performance of the proposed algorithm exceeded the state-of-the-art methods, which fully proved the effectiveness of the GAC Net presented in this paper.Physics-based multi-scale in silico models offer an excellent opportunity to study the effects of heterogeneous tissue damage on airflow and pressure distributions in COVID-19-afflicted lungs. The main objective of this study is to develop a computational modeling workflow, coupling airflow and tissue mechanics as the first step towards a virtual hypothesis-testing platform for studying injury mechanics of COVID-19-afflicted lungs. We developed a CT-based modeling approach to simulate the regional changes in lung dynamics associated with heterogeneous subject-specific COVID-19-induced damage patterns in the parenchyma. Furthermore, we investigated the effect of various levels of inflammation in a meso-scale acinar mechanics model on global lung dynamics. Our simulation results showed that as the severity of damage in the patient's right lower, left lower, and to some extent in the right upper lobe increased, ventilation was redistributed to the least injured right middle and left upper lobes. Furthermore, our multi-scale model reasonably simulated a decrease in overall tidal volume as the level of tissue injury and surfactant loss in the meso-scale acinar mechanics model was increased. This study presents a major step towards multi-scale computational modeling workflows capable of simulating the effect of subject-specific heterogenous COVID-19-induced lung damage on ventilation dynamics.Breast cancer (BC) is one of the most malignant tumors and the leading cause of cancer-related death in women worldwide. So, an in-depth investigation on the molecular mechanisms of BC progression is required for diagnosis, prognosis and therapies. In this study, we identified 127 common differentially expressed genes (cDEGs) between BC and control samples by analyzing five gene expression profiles with NCBI accession numbers GSE139038, GSE62931, GSE45827, GSE42568 and GSE54002, based-on two statistical methods LIMMA and SAM. Then we constructed protein-protein interaction (PPI) network of cDEGs through the STRING database and selected top-ranked 7 cDEGs (BUB1, ASPM, TTK, CCNA2, CENPF, RFC4, and CCNB1) as a set of key genes (KGs) by cytoHubba plugin in Cytoscape. Several BC-causing crucial biological processes, molecular functions, cellular components, and pathways were significantly enriched by the estimated cDEGs including at-least one KGs. The multivariate survival analysis showed that the proposed KGs have a strong prognosis power of BC. Moreover, we detected some transcriptional and post-transcriptional regulators of KGs by their regulatory network analysis. Finally, we suggested KGs-guided three repurposable candidate-drugs (Trametinib, selumetinib, and RDEA119) for BC treatment by using the GSCALite online web tool and validated them through molecular docking analysis, and found their strong binding affinities. Therefore, the findings of this study might be useful resources for BC diagnosis, prognosis and therapies.

Lung adenocarcinoma (LUAD) is one the most prevalent cancer with high mortality and its risk stratification is limited due lack of reliable molecular biomarkers. Although several studies have been conducted to identify gene signature involved in LUAD progression, most currently used methods to select gene features did not fully consider the problem of the existence of strong pairwise gene correlations as it resulted inconsistency in gene election. Therefore, it is crucial to develop new strategy to identify reliable gene signatures that improve risk prediction.

In this study, novel feature selection strategy (1) univariate Cox regression model to select survival associated genes (2) integrating rigid Cox regression with Adaptive Lasso model to identify informative survival associated genes (3) stepwise Cox regression model to identify optimal gene signature and (4) prognostic risk predictive model for LUAD (PRPML) was constructed. The PRPML was developed-based on four machine learning (ML) methods including logistic regression (LR), K-nearest neighbors (KNN), support vector machine with the radial kernel (SVMR), and average neural network (Avnet).