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Breast cancer is the most prevalent cancer affecting women. Identifying breast cancer risk factors are crucially to be established. The main objectives of the present study were to identify the predictors of breast cancer risk factors and their relative importance using neural network analysis. The present study depended on neural network analysis of data posted on [1]. The dataset is about predictors of breast cancer. There were 9 covariates included and one dependent variable, output (no disease (1), or disease (2)). The dataset was composed of 116 cases. The category of no disease comprised 79 (68.1%) cases, whereas the disease category included 37 cases (31.9%). Architecture model was built with some characteristics such as training part: gross entropy error was 23.7884, the percent of incorrect predictions was 10.1%. Stopping rule used was 1 consecutive step(s) with no decrease in error. For testing part, gross entropy error was 14,327, the percent of incorrect predictions was 13.5%. The relative importance of breast cancer was in the following order: glucose, resistin, BMI, age, leptin, adiponectin, MCP-1, insulin, and HOMA. Taken together, neural network analysis is an efficient tool to predict breast cancer risk factors.

The role of novel HBV markers in predicting Hepatitis B virus reactivation (HBV-R) in HBsAg-negative/anti-HBc-positive oncohaematological patients was examined. One hundred and seven HBsAg-negative/anti-HBc-positive oncohaematological patients, receiving anti-HBV prophylaxis for > 18 months were included. At baseline, all patients had undetectable HBV DNA, and 67.3% were anti-HBs positive. HBV-R occurred in 17 (15.9%) patients: 6 during and 11 after the prophylaxis period. At HBV-R, the median (IQR) HBV-DNA was 44 (27–40509) IU/ml, and the alanine aminotransferase upper limit of normal (ULN) was 44% (median [IQR]: 81[49–541] U/L). An anti-HBc>3 cut-off index (COI) plus anti-HBs persistently/declining to 2 time points, also predicts HBV-R (OR [95% CI ]: 13.8 [3.6–52.6]; 50% of positive vs. 7% of negative patients to these markers experienced HBV-R, P=0.001).HS-HBs and anti-HBc titration proved useful early markers of HBV-R.The use of these markers demonstrated that HBV-R frequently occurs in oncohaematological patients with signs of resolved HBV infection, raising issues of proper HBV-R monitoring.

HBeAg is a marker of HBV-activity, and HBeAg-loss predicts a favorable clinical outcome. Here, we characterize HBeAg-levels across different phases of HBV infection, their correlation with virological/biochemical markers and the virological response to anti-HBV therapy. Quantitative HBeAg (qHBeAg, DiaSorin) is assessed in 101 HBeAg+ patients: 20 with acute-infection, 20 with chronic infection, 32 with chronic hepatitis and 29 with immunosuppression-driven HBV-reactivation (HBV-R). A total of 15/29 patients with HBV-R are monitored for &gt, 12 months after starting TDF/ETV. qHBeAg is higher in immunosuppression-driven HBV-R (median[IQR]:930[206–1945]PEIU/mL) and declines in chronic hepatitis (481[28–1393]PEIU/mL, p = 0.03), suggesting HBeAg production, modulated by the extent of immunological pressure. This is reinforced by the negative correlation between qHBeAg and ALT in acute infection (Rho = −0.66, p = 0.006) and chronic hepatitis (Rho = −0.35, p = 0.05). Interestingly, qHBeAg strongly and positively correlates with qHBsAg across the study groups, suggesting cccDNA as a major source of both proteins in the setting of HBeAg positivity (with limited contribution of integrated HBV-DNA to HBsAg production). Focusing on 15 patients with HBV-R starting TDF/ETV, virological suppression and HBeAg-loss are achieved in 60% and 53.3%. Notably, the combination of qHBeAg &gt, 2000 PEIU/mL + qHBsAg &gt, 52,000 IU/mL at HBV-R is the only factor predicting no HBeAg loss (HBeAg loss: 0% with vs. 72.7% without qHBeAg &gt, 52,000 IU/mL, p = 0.03). In conclusion, qHBeAg varies over the natural course of HBV infection, according to the extent of immunological pressure. In the setting of HBV-R, qHBeAg could be useful in predicting the treatment response under immunosuppression.

Individuals and businesses are moving to cloud-based services, to benefit from their pay-as-you-go and elastic scalability features. The main concern to wide adoption of cloud-based services is the lack of protection of clients’ data and computation from the various outsider as well as insider attacks, which threaten to compromise client data confidentiality and integrity. Trusted computing provides a foundation for designing security services that are resilient to various threats and attacks in a distributed environment such as the cloud. Current trusted computing based solutions are ill-suited to the cloud as they inadvertently disclose too many details about the underlying infrastructure to clients and at the same time involve the complex task of attestation and verification on the client side. Additionally, direct verification of security properties of the cloud platform to each and every client introduces computational bottlenecks. In this work, we propose a scalable framework which enables verification of the properties of the cloud platform through a trusted third party without the direct involvement of the client. Our proposed framework is thin client (mobile device) friendly, as the client is alleviated of direct attestation and verification process. Performance analysis shows that the cost of our presented approach is lower in order of magnitude when compared with traditional trusted computing based solutions.

Ultrasound-Guided Regional Anesthesia (UGRA) becomes a standard procedure in surgical operations and contributes to pain management. It offers the advantages of the targeted nerve detection and provides the visualization of regions of interest such as anatomical structures. However, nerve detection is one of the most challenging tasks that anesthetists can encounter in the UGRA procedure. A computer-aided system that can detect automatically the nerve region would facilitate the anesthetist's daily routine and allow them to concentrate more on the anesthetic delivery. In this paper, we propose a new method based on merging deep learning models from different data to detect the median nerve. The merged architecture consists of two branches, one being one-dimensional (1D) convolutional neural networks (CNN) branch and another 2D CNN branch. The merged architecture aims to learn the high-level features from 1D handcrafted noise-robust features and 2D ultrasound images. The obtained results show the validity and high accuracy of the proposed approach and its robustness.

Droplet digital PCR is an innovative and promising approach for highly sensitive quantification of nucleic acids that is being increasingly used in the field of clinical virology, including the setting of hepatitis B virus (HBV). Here, we comprehensively report a robust and reproducible ddPCR assay for the highly sensitive quantification of serum HBV-DNA. The assay showed a limit of detection of 4 copies/ml ( Overall, the availability of assays for the highly sensitive quantification of serum HBV-DNA can provide an added value in optimizing the diagnosis of occult hepatitis B infection, improving the therapeutic management of chronically HBV infected patients, also in the light of innovative drugs (upcoming in clinical practise) aimed at achieving HBV functional cure.

Objectives To compare the efficacy of dolutegravir plus lamivudine dual therapy (DT) with that of dolutegravir plus two NRTIs triple therapy (TT) as switch strategies. Methods A multicentre cohort of HIV-positive, HBsAg-negative patients with viral suppression (HIV-RNA ≤50 copies/mL) switching to DT or TT was retrospectively selected from the ARCA database. The effect of DT versus TT on virological failure (VF; defined as two consecutive HIV-RNA values >50 copies/mL or one HIV-RNA value ≥200 copies/mL) was evaluated by multivariable Cox regression models, overall and after stratifying for the presence of NRTI resistance-associated mutations (RAMs). Results From December 2014 to June 2020, 628 patients were eligible: 118 (18.8%) started tenofovir/emtricitabine/dolutegravir, 306 (48.7%) abacavir/lamivudine/dolutegravir and 204 (32.5%) lamivudine/dolutegravir. The DT group had significantly higher nadir and baseline CD4 counts, a higher duration of viral suppression and a lower prevalence of RAMs at historical genotype. Overall, 41 VF occurred after a median of 1.7 years of follow-up, with a lower, but not statistically significant, rate for DT [versus TT, adjusted HR (aHR) = 0.58, 95% CI = 0.25–1.34]. However, DT was associated with less VF in the absence of RAMs when compared with tenofovir-based TT (aHR = 0.20, 95% CI = 0.06–0.67), but not with abacavir-based TT (aHR = 0.43, 95% CI = 0.17–1.11). Conversely, in the setting of pre-existing M184V/I, DT showed a trend to increased risk of VF (versus tenofovir-based TT, aHR = 137.50, 95% CI = 4.24–4464.06; versus abacavir-based TT, aHR = 33.88, 95% CI = 1.75–656.47). Conclusions Lamivudine/dolutegravir maintenance DT showed similar efficacy to dolutegravir-based TT; however, past M184V/I may favour VF.

The anti-HBc-positive/HBsAg-negative status is frequent in HIV-infection and correlates with poor survival. Here, by highly-sensitive assays, we evaluate cryptic HBV replication and factors correlated with its detection in 81 anti-HBc-positive/HBsAg-negative HIV-infected patients. Patients were treated for &gt, 12 months with HBV-active modern combined antiretroviral-therapy (cART) and had serum HBV-DNA &lt, 20 IU/mL by commercial Real-Time PCR. Serum HBV-DNA was quantified by droplet digital PCR, serum HBV-RNA by an Abbott research assay, and anti-HBc titer (proposed to infer intrahepatic cccDNA) by Lumipulse/Fujirebio. Cryptic serum HBV-DNA was detected in 29.6% of patients (median (IQR): 4(1&ndash, 15) IU/mL) and serum HBV-RNA in 3.7% of patients despite HBsAg-negativity and HBV-active cART. Notably, cryptic serum HBV-DNA correlated with an advanced CDC-stage (p = 0.01) and a lower anti-HBs titer (p = 0.05), while serum HBV-RNA correlated with lower nadir CD4+ cell-count (p = 0.01). By analyzing serological HBV-markers, the combination of anti-HBs &lt, 50 mIU/mL (indicating lower immune response) plus anti-HBc &gt, 15COI (reflecting higher HBV replicative activity) was predictive of cryptic serum HBV-DNA (OR: 4.7(1.1&ndash, 21.7), p = 0.046, PPV = 62.5%, and NPV = 72%). In conclusion, cryptic HBV-replication (not detected by classical assays) characterizes a conspicuous set of anti-HBc-positive HIV-infected patients despite HBsAg-negativity and HBV-active combined antiretroviral therapy (cART). The integration of classical and novel markers may help identify patients with cryptic HBV-replication, thus optimizing the monitoring of anti-HBc-positive/HBsAg-negative HIV-infected patients.

Atorvastatin is commonly used among type 2 diabetic (DM2) patients at the University of Jordan Hospital to prevent cardiovascular complication. However, we noticed that there is a wide inter-individual variation in the efficacy and toxicity of atorvastatin. This study aimed to find out the effects of major genetic variants in 3-Hydroxy-3-Methylglutaryl-CoA Reductase (HMGCR), Apolipoprotein E (APOE), and Solute Carrier Organic Anion (SLCO1B1) genes on atorvastatin response among DM2 patients. A sample of 139 DM2 patients on 20 mg of atorvastatin was included in this study. The lipid and glycemic profile and the levels of hepatic enzymes alanine aminotransferase (ALT) and aspartate transaminase were recorded before and after 3 months of atorvastatin treatment. Additionally, the genetic variants HMGCR rs17244841,APOE rs7412 and rs429357, and SLCO1B1 rs2306283 and rs11045818 were genotyped using an Applied Biosystems DNA sequencing method (ABI3730&times, 1). We found that atorvastatin reduced total cholesterol and low-density lipoprotein (LDL) more significantly (p-value &lt, 0.05) in patients with wild genotype than variant alleles APOE rs7412C &gt, T and SLCO1B1 rs2306283A &gt, G. Furthermore, the ALT level was elevated significantly (p-value &lt, 0.05) by 27% in patients with heterozygous SLCO1B1 rs11045818 G/A genotype, while it was not elevated among wild genotype carriers. Additionally, atorvastatin reduced total cholesterol more significantly (p-value &lt, 0.05) in patients with SLCO1B1 rs2306283A and rs11045818G haplotypes and increased ALT levels by 27% (p-value &lt, 0.05) in patients with SLCO1B1 rs2306283G and rs11045818A haplotypes. In conclusion, it was found in this study that APOE rs7412, SLCO1B1 rs2306283, and rs11045818 genotypes can be considered as potential genetic biomarkers of atorvastatin response among DM2 patients of Jordanian Arabic origin. Further clinical studies with larger sample numbers are needed to confirm these findings.

Ultrasound-guided regional anesthesia (UGRA) becomes a standard procedure in surgical operations and contributes to pain management; it offers the advantages of the needle and targeted nerve detection and provides the visualization of regions of interest such as anatomical structures. In UGRA, the remote center of motion (RCM) constitutes an essential issue as the anesthetist has to manipulate a needle inside the human body. However, RCM imposes a very challenging task, where it is important to ensure that the needle should move within the constraints of the insertion point in order to prevent patient harm. To respond to this need, this paper proposes a control framework for robot-assisted UGRA for physical human-robot collaboration using 7 degrees of freedom robot manipulator (Franka Emika). This paper shows a geometric method computing the intended robot’s end-effect position with respect to the RCM constraints. This method helps the anesthetist to execute a more sophisticated motion within the patient’s body with high accuracy.

The last few years witnessed a rapid increase in the use of multimedia applications, which led to an explosion in the amount of data sent over communication networks. Therefore, it has become necessary to find an effective security solution that preserves the confidentiality of such enormous amount of data sent through unsecure network channels and, at the same time, meets the performance requirements for applications that process the data. This research introduces a high-speed and secure elliptic curve cryptosystem (ECC) appropriate for multimedia security. The proposed ECC improves the performance of data encryption process by accelerating the scaler multiplication operation, while strengthening the immunity of the cryptosystem against side channel attacks. The speed of the encryption process has been increased via the parallel implementation of ECC computations in both the upper scaler multiplication level and the lower point operations level. To accomplish this, modified version of the Right to Left binary algorithm as well as eight parallel multipliers (PM) were used to allow parallel implementation for point doubling and addition. Moreover, projective coordinates systems were used to remove the time-consuming inversion operation. The current 8-PM Montgomery ECC achieves higher performance level compared to previous ECC implementations, and can reduce the risk of side channel attacks. In addition, current research work provides performance and resources-consumption analysis for Weierstrass and Montgomery elliptic curve representations over prime field. However, the proposed ECC implementation consumes more resources. Presented ECCs were implemented using VHDL, and synthesized using the Xilinx tool with target FPGA.

Background and Objective In the last decade, Ultrasound-Guided Regional Anesthesia (UGRA) gained importance in surgical procedures and pain management, due to its ability to perform target delivery of local anesthetics under direct sonographic visualization. However, practicing UGRA can be challenging, since it requires high skilled and experienced operator. Among the difficult task that the operator can face, is the tracking of the nerve structure in ultrasound images. Tracking task in US images is very challenging due to the noise and other artifacts. Methods In this paper, we introduce a new and robust tracking technique by using Adaptive Median Binary Pattern(AMBP) as texture feature for tracking algorithms (particle filter, mean-shift and Kanade-Lucas-Tomasi(KLT)). Moreover, we propose to incorporate Kalman filter as prediction and correction steps for the tracking algorithms, in order to enhance the accuracy, computational cost and handle target disappearance. Results The proposed method have been applied on real data and evaluated in different situations. The obtained results show that tracking with AMBP features outperforms other descriptors and achieved best performance with 95% accuracy. Conclusions This paper presents the first fully automatic nerve tracking method in Ultrasound images. AMBP features outperforms other descriptors in all situations such as noisy and filtered images.

Background: Obstructive sleep apnea (OSA) is a known risk factor for atrial fibrillation (AF) that is principally driven by left atrial enlargement. The impact of hypoventilation caused by obesity-induced hypoventilation syndrome (OHS) on left atrial diameter has not been examined. We investigated the association between OHS and left atrial diameter in obese patients. Methods: We performed a retrospective review of 210 consecutive medical records of patients diagnosed as obese (body mass index [BMI] >30 kg/m2) and as having OHS and OSA for the period January 2010 through December 2016 at St. Vincent Charity Medical Center in Cleveland, OH. Logistic regression analysis was performed for left atrial diameter ≥4 cm in 2 groups of patients: those with OHS+OSA and those with OSA alone. Results: A total of 104 obese patients with OHS+OSA and 106 obese patients with OSA alone were identified. Statistically significant differences were found in 6 demographic and baseline characteristics: median BMI, median left atrial diameter, history of type 2 diabetes mellitus, history of stroke, history of coronary artery disease, and history of congestive heart failure. The median left atrial diameter for the OHS+OSA and OSA alone groups was 4.45 cm and 4.20 cm, respectively (P = 0.014). Left ventricular ejection fraction Conclusion: The results of this study indicate that OHS may be an independent risk factor for left atrial enlargement and may possibly contribute to AF development irrespective of left ventricular function.

Texture is an important characteristic for different computer vision tasks and applications. Local binary pattern (LBP) is considered one of the most efficient texture descriptors yet. However, LBP has some notable limitations, in particular its sensitivity to noise. In this paper, we address these criteria by introducing a novel texture descriptor, robust adaptive median binary pattern (RAMBP). RAMBP is based on a process involving classification of noisy pixels, adaptive analysis window, scale analysis, and a comparison of image medians. The proposed method handles images with highly noisy textures and increases the discriminative properties by capturing microstructure and macrostructure texture information. The method was evaluated on popular texture datasets for classification and retrieval tasks and under different high noise conditions. Without any training or prior knowledge of the noise type, RAMBP achieved the best classification compared to state-of-the-art techniques. It scored more than 90% under 50% impulse noise densities, more than 95% under Gaussian noised textures with a standard deviation $\sigma = 5$ , more than 99% under Gaussian blurred textures with a standard deviation $\sigma = 1.25$ , and more than 90% for mixed noise. The proposed method yielded competitive results and proved to be one of the best descriptors in noise-free texture classification. Furthermore, RAMBP showed high performance for the problem of noisy texture retrieval providing high scores of recall and precision measures for textures with high noise levels. Finally, compared with the state-of-the-art methods, RAMBP achieves a good running time with low feature dimensionality.

Ultrasound-guided regional anesthesia (UGRA) becomes a standard procedure in surgical operations and pain management, offers the advantages of nerve localization, and provides region of interest anatomical structure visualization. Nerve tracking presents a crucial step for practicing UGRA and it is useful and important to develop a tool to facilitate this step. However, nerve tracking is a very challenging task that anesthetists can encounter due to the noise, artifacts, and nerve structure variability. Deep-learning has shown outstanding performances in computer vision task including tracking. Many deep-learning trackers have been proposed, where their performance depends on the application. While no deep-learning study exists for tracking the nerves in ultrasound images, this paper explores thirteen most recent deep-learning trackers for nerve tracking and presents a comparative study for the best deep-learning trackers on different types of nerves in ultrasound images. We evaluate the performance of the trackers in terms of accuracy, consistency, time complexity, and handling different nerve situations, such as disappearance and losing shape information. Through the experimentation, certain conclusions were noted on deep learning trackers performance. Overall, deep-learning trackers provide good performance and show a comparative performance for tracking different kinds of nerves in ultrasound images.

Hepatitis B virus (HBV) persistence is at the basis of HBV reactivation as a consequence of chemotherapy and immunosuppressive treatments. The identification of early viral replication indicators and markers of effective HBV immunological control would be useful in monitoring patients who are at risk of potential viral reactivation during the course of immunosuppressive treatment. Currently, international guidelines have shared some criteria to identify patients with a low, medium or high risk of HBV reactivation; however, permanently placing a patient in a definitive category is not always easy. More often, patients move from one category to another during the course of their immunosuppressive treatment; therefore, in many cases, there are no precise indicators or tools for monitoring possible reactivation and establishing the duration and suspension of antiviral prophylaxis. Historically, the sequence of HBV antigens and antibodies and HBV DNA levels has been used to evaluate the different stages of the acute and chronic phases of an HBV infection. In the last few years, new biomarkers, such as anti-HBs and anti-HBc titres, HBV core-related antigen (HBcrAg), ultra-sensitive HBsAg evaluation and HBV RNA, have been used in patients with an HBV infection to evaluate their diagnostic and prognostic potential. The aim of this review is to evaluate the published results on the use of new infection markers in the diagnosis and monitoring of HBV reactivation over the course of immunosuppressive treatments. Moreover, the importance of viral genotypic studies was emphasized, given the diagnostic and therapeutic implications of the mutational profiles of HBsAg during the HBV reactivation phase.

Background: Diabetes and hypertension are two of the commonest diseases in the world. As they unfavorably affect people of different age groups, they have become a cause of concern and must be predicted and diagnosed well in advance. Objective: This research aims to determine the effectiveness of artificial neural networks (ANNs) in predicting diabetes and blood pressure diseases and to point out the factors which have a high impact on these diseases. Sample: This work used two online datasets which consist of data collected from 768 individuals. We applied neural network algorithms to predict if the individuals have those two diseases based on some factors. Diabetes prediction is based on five factors: age, weight, fat-ratio, glucose, and insulin, while blood pressure prediction is based on six factors: age, weight, fat-ratio, blood pressure, alcohol, and smoking. Method: A model based on the Multi-Layer Perceptron Neural Network (MLP) was implemented. The inputs of the network were the factors for each disease, while the output was the prediction of the disease’s occurrence. The model performance was compared with other classifiers such as Support Vector Machine (SVM) and K-Nearest Neighbors (KNN). We used performance metrics measures to assess the accuracy and performance of MLP. Also, a tool was implemented to help diagnose the diseases and to understand the results. Result: The model predicted the two diseases with correct classification rate (CCR) of 77.6% for diabetes and 68.7% for hypertension. The results indicate that MLP correctly predicts the probability of being diseased or not, and the performance can be significantly increased compared with both SVM and KNN. This shows MLPs effectiveness in early disease prediction.