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Objectives: To evaluate the esthetic outcome, as well as clinical, radiographic, and volumetric tissue alterations 1 year after immediate implant placement (IIP) with connective tissue grafting (CTG) versus dual‐zone concept (DZ) at sites with thin labial bone in the esthetic zone. Materials and Methods: This randomized clinical trial included 30 patients treated with IIP simultaneous with either CTG or DZ (n = 15 each). Pink esthetic score (PES) was assessed 6 months after crown placement as the primary outcome. Amount of bone labial to the implant, labio‐palatal ridge reduction, and crestal bone changes were measured via CBCT after 1 year. Volumetric analysis of linear labial soft tissue contour, interdental, and mid‐facial soft tissue level changes, and total volume loss (mm3) were measured after 1 year. Results: Similar PES was observed in the CTG (12.53 ± 1.13) and DZ (12.13 ± 1.55) groups, with no significant difference (p = 0.42). Likewise, there were no statistically significant differences found between the two groups in labio‐palatal bone reduction (mm&%), interdental papillae, and mid‐facial gingival levels (p > 0.05). However, the mean vertical crestal bone changes in the CTG and DZ groups were −1.1 ± 0.6 mm and 0.2 ± 1.0 mm, respectively, with a statistically significant difference (p = 0.0002). Moreover, CTG revealed less linear and total volume (mm3) loss in the labial soft tissue which was statistically significant compared to DZ (p = 0.007). Conclusion: Both groups demonstrated the same PES, nevertheless, volumetric analysis revealed twice total labial volume loss in DZ compared to CTG. It might be concluded that the use of CTG with IIP caused less horizontal reduction in the supra‐implant complex compared to the DZ. [ABSTRACT FROM AUTHOR]

Background: The management options for cystic craniopharyngioma (CP) are controversial. Surgical resection (gross or partial), radiotherapy, chemotherapy, cyst aspiration, and Ommaya reservoir system (ORS) implantations are the options for management that are used individually or in combination, but the ideal treatment is unclear. Objective: We aimed to investigate the functional outcomes and the complications of Ommaya reservoir system (ORS) insertion as a treatment option for cystic CP. Methods: Between January 2018 and January 2020, 12 patients with enhanced cystic CP were treated by cyst fenestration and aspiration on the day of surgery, and an ORS was inserted. The period between the procedure time and the last outpatient visit was considered the follow‐up period. Results: The main presenting symptoms were increased intracranial pressure, visual defects, and hormonal changes. The clinical symptoms improved, and the tumour cysts appeared to stabilize after catheter placement without the need for repeated aspirations; of the 12 patients included, only one case needed re‐aspiration. The eight patients who had signs of increased intracranial pressure reported resolved symptoms (100%). Of the 12 patients, seven and six patients showed improved visual acuity and improved visual field defect. Conclusion: Cyst fenestration and insertion of ORS is a safe and effective treatment option for cystic CP. ORS can be used in de novo cases as a treatment option alone without further invasive treatment to alleviate symptoms and improve clinical status, especially the visual changes. [ABSTRACT FROM AUTHOR]

The Eastern Desert of Egypt suffered a protracted period of deformation triggered by cratonization of the new juvenile crust known as the Arabian Nubian Shield (ANS), which has been proposed for potential gold discoveries associated with the corresponding tectonic event. The Fatira area, on the border of Egypt's Northern and Central Eastern Deserts, is covered with metavolcanic rocks twisted by a dextral relocation of the Fatira Shear Zone (FSZ) relative to the Barud magmatic body. The recent study evaluated many deformed post‐orogenic granitic intrusions and felsite dikes associated with promising mineralization localities, notably orogenic gold deposits. The combination of various field observations and remote sensing data, followed by the analysis of aeromagnetic enhanced maps, allowed the differentiation of distinct lithologies, structural features, and hydrothermal alterations in the study area. Additionally, the integrated results obtained from the different interpretation techniques are utilized to identify and confirm the previously supposed mineralized localities in the Fatira and Abu Zawal areas and predict other matched localities. The final ASTER, Sentinel 2 hydrothermal alteration, and orientation entropy heat maps demonstrate the association between these mineralized regions and major structures related to the FSZ late stage of deformation rather than other structures studied throughout the area of interest. [ABSTRACT FROM AUTHOR]

Electrospinning technique became a very common and effective method to fabricate nonwoven films with a large surface area for different purposes especially in the biomedical field. Antimicrobial submicron fibrous films based on polyvinylpyrrolidone (PVP) doped with silver nanoparticles (Ag‐NPs), Zinc Oxide (ZnO), and (Ag‐NPS/ZnO), were successfully fabricated via the electrospinning technique. The morphology and the elemental configurations of the as‐prepared films were studied by using scanning electron microscope with EDX. While the phase formation and crystal structures were determined by using XRD analysis. The antibacterial effect was investigated against one of the most common Gram‐negative bacteria Klebsiella pneumoniae by using a modified Kirby–Bauer disc diffusion method. The results showed that the doping nanoparticles were uniformly loaded on the surface of the fabricated film fibers. By using Scherrer equation the calculated average crystallite sizes of Ag‐NPs, ZnO, and Ag‐NPs/ZnO on PVP fibers were 63, 30, and 44 nm, respectively. The antimicrobial activity against Klebsiella pneumoniae showed the growth inhibition zones in the bacteria plates of 23.3, 54, and 60.6 mm for the samples of Ag‐NPs/PVP, ZnO/PVP, and Ag‐NPs/ZnO/PVP, respectively. The antimicrobial efficiency increased by forming nanocomposites of both ZnO nanoparticles and Ag nanoparticles inside the film. Research Highlights: Novel antimicrobial submicron electrospun membranes based on polyvinylpyrrolidone doped with silver, zinc oxide nanoparticles were successfully fabricated.The fabricated samples showed bactericidal activity against K. pneumoniae [ABSTRACT FROM AUTHOR]

Three‐dimensional advection–diffusion equation with steady state was evaluated from continuous point source taking the vertical and crosswind eddy diffusivities as power law of vertical height and downwind distance with constant wind speed to get the concentration in three dimensions. Separation of variables technique and Hankel transform were used to calculate this equation. The proposed analytical solution was compared with diffusion experiment of radioactive Iodine‐135 (I135) in unstable condition at Inshas, Cairo, Egypt. A good agreement achieved between calculated and observed concentration support our proposed model. [ABSTRACT FROM AUTHOR]

Purpose: Task‐based fMRI (TfMRI) is a diagnostic imaging modality for observing the effects of a disease or other condition on the functional activity of the brain. Autism spectrum disorder (ASD) is a pervasive developmental disorder associated with impairments in social and linguistic abilities. Machine learning algorithms have been widely utilized for brain imaging aiming for objective ASD diagnostics. Recently, deep learning methods have been gaining more attention for fMRI classification. The goal of this paper is to develop a convolutional neural network (CNN)‐based framework to help in global diagnosis of ASD using TfMRI data that are collected from a response to speech experiment. Methods: To achieve this goal, the proposed framework adopts a novel imaging marker integrating both spatial and temporal information that are related to the functional activity of the brain. The developed pipeline consists of three main components. In the first step, the collected TfMRI data are preprocessed and parcellated using the Harvard–Oxford probabilistic atlas included with the fMRIB Software Library (FSL). Second, a group analysis using FSL is performed between ASD and typically developing (TD) children to identify significantly activated brain areas in response to the speech task. In order to reduce brain spatial dimensionality, a K‐means clustering technique is performed on such significant brain areas. Informative blood oxygen level‐dependent (BOLD) signals are extracted from each cluster. A compression step for each extracted BOLD signal using discrete wavelet transform (DWT) has been proposed. The adopted wavelets are similar to the expected hemodynamic response which enables DWT to compress the BOLD signal while highlighting its activation information. Finally, a deep learning 2D CNN network is used to classify the patients as ASD or TD based on extracted features from the previous step. Results: Preliminary results on 100 TfMRI dataset (50 ASD, 50 TD) obtain 80% correct global classification using tenfold cross validation (with sensitivity = 84%, specificity = 76%). Conclusion: The experimental results show the high accuracy of the proposed framework and hold promise for the presented framework as a helpful adjunct to currently used ASD diagnostic tools. [ABSTRACT FROM AUTHOR]

Purpose: Early assessment of renal allograft function post‐transplantation is crucial to minimize and control allograft rejection. Biopsy — the gold standard — is used only as a last resort due to its invasiveness, high cost, adverse events (e.g., bleeding, infection, etc.), and the time for reporting. To overcome these limitations, a renal computer‐assisted diagnostic (Renal‐CAD) system was developed to assess kidney transplant function. Methods: The developed Renal‐CAD system integrates data collected from two image‐based sources and two clinical‐based sources to assess renal transplant function. The imaging sources were the apparent diffusion coefficients (ADCs) extracted from 47 diffusion‐weighted magnetic resonance imaging (DW‐MRI) scans at 11 different b‐values (b0, b50, b100,..., b1000 s/mm2), and the transverse relaxation rate (R2*) extracted from 30 blood oxygen level‐dependent MRI (BOLD‐MRI) scans at 5 different echo times (TEs = 2, 7, 12, 17, and 22 ms). Serum creatinine (SCr) and creatinine clearance (CrCl) were the clinical sources for kidney function evaluation. The Renal‐CAD system initially performed kidney segmentation using the level‐set method, followed by estimation of the ADCs from DW‐MRIs and the R2* from BOLD‐MRIs. ADCs and R2* estimates from 30 subjects that have both types of scans were integrated with their associated SCr and CrCl. The integrated biomarkers were then used as our discriminatory features to train and test a deep learning‐based classifier, namely stacked autoencoders (SAEs) to differentiate non‐rejection (NR) from acute rejection (AR) renal transplants. Results: Using a leave‐one‐subject‐out cross‐validation approach along with SAEs, the Renal‐CAD system demonstrated 93.3% accuracy, 90.0% sensitivity, and 95.0% specificity in differentiating AR from NR. Robustness of the Renal‐CAD system was also confirmed by the area under the curve value of 0.92. Using a stratified tenfold cross‐validation approach, the Renal‐CAD system demonstrated its reproducibility and robustness by a diagnostic accuracy of 86.7%, sensitivity of 80.0%, specificity of 90.0%, and AUC of 0.88. Conclusion: The obtained results demonstrate the feasibility and efficacy of accurate, noninvasive identification of AR at an early stage using the Renal‐CAD system. [ABSTRACT FROM AUTHOR]

Purpose Detection (diagnosis) of diabetic retinopathy ( DR) in optical coherence tomography ( OCT) images for patients with type 2 diabetes, but almost clinically normal retina appearances. Methods The proposed computer-aided diagnostic ( CAD) system detects the DR in three steps: (a) localizing and segmenting 12 distinct retinal layers on the OCT image; (b) deriving features of the segmented layers, and (c) learning most discriminative features and classifying each subject as normal or diabetic. To localise and segment the retinal layers, signals (intensities) of the OCT image are described with a joint Markov-Gibbs random field ( MGRF) model of intensities and shape descriptors. Each segmented layer is characterized with cumulative probability distribution functions ( CDF) of its locally extracted features, such as reflectivity, curvature, and thickness. A multistage deep fusion classification network ( DFCN) with a stack of non-negativity-constrained autoencoders ( NCAE) is trained to select the most discriminative retinal layers' features and use their CDFs for detecting the DR. A training atlas was built using the OCT scans for 12 normal subjects and their maps of layers hand-drawn by retina experts. Results Preliminary experiments on 52 clinical OCT scans (26 normal and 26 with early-stage DR, balanced between 40-79 yr old males and females; 40 training and 12 test subjects) gave the DR detection accuracy, sensitivity, and specificity of 92%; 83%, and 100%, respectively. The 100% accuracy, sensitivity, and specificity have been obtained in the leave-one-out cross-validation test for all the 52 subjects. Conclusion Both the quantitative and visual assessments confirmed the high accuracy of the proposed computer-assisted diagnostic system for early DR detection using the OCT retinal images. [ABSTRACT FROM AUTHOR]

Osteoporosis is a bone disease characterised by a reduction in bone mass, resulting in an increased risk of fractures. Doctors need the bone mineral density (BMD) measurements of vertebral bodies in order to diagnose and treat osteoporosis. The authors' objective is to segment the VBs as accurately as possible and hence to increase the accuracy of the BMD measurements and fracture analysis. Three pieces of information (intensity, spatial interaction and shape) are modelled to optimise a probabilistic energy functional. A universal shape prior, which is modelled using the cervical, thoracic and lumbar spinal regions, is proposed. Volumetric computed tomography data sets with various challenges are used in this study. The authors classify data sets based on some features related to the anatomy, imaging modality and level of the bone health. The proposed framework is one of only a few reported in the literature tested on the data obtained from different imaging devices. The experimental results reveal that the proposed method is robust under various noise levels, less variant to the initialisation and faster than existing vertebrae segmentation reports in the literature. [ABSTRACT FROM AUTHOR]

In this study, a novel probabilistic, geometric and dynamic shape-based level sets method is proposed. The shape prior is coupled with the intensity information to enhance the segmentation results. The two-dimensional principal component analysis method is applied on the training shapes to represent the shape variation with enough number of shape projections in the training step. The shape model is constructed using the implicit representation of the projected shapes. A new energy functional is proposed (i) to embed the shape model into the image domain and (ii) to estimate the shape coefficients. The proposed method is validated on synthetic and clinical images with various challenges such as the noise, occlusion and missing information. The authors compare their method with some of related works. Experiments show that the proposed segmentation method is more accurate and robust than other alternatives under different challenges. [ABSTRACT FROM AUTHOR]

Background & Aims Hepatitis C virus ( HCV) genotype 4 (G4) infection is common in the Middle East. Post-treatment long-term outcomes have not been reported in these patients. This study evaluates these outcomes in patients after interferon-based therapy. Patients and methods A total of 157 patients were followed from June 2001 to February 2012. Descriptive and analytical statistics, cumulative outcomes and the independent predictors of disease progression were calculated. Results The overall age was 48.0 ± 11.8 years, 75 (47.8%) were males and 53 (70.7%) of 75 who were genotyped had G4. The follow-up period was 63.8 ± 32.8 months. Sustained virological response ( SVR) was achieved in 62 (39.5%) and 24 (45.3%) patients in the whole group and the G4 subgroup respectively. Among the whole cohort and the G4 subgroup, disease progressed in 59 (37.6%) and 21 (39.6%), respectively, with less progression in the SVR groups; 15/62 (24.2%) and 3/24 (12.5%) compared with non-responders; 44 (46.3%) and 18 (62.1%) with P = 0.01 and 0.001 respectively. Multivariate logistic regression analysis showed that having diabetes mellitus ( P = 0.03), higher baseline APRI score ( P = 0.00) and non- SVR ( P = 0.00) were independent predictors of disease progression. G4 patients showed similar results, but 'non- SVR' ( P = 0.00) was the only independent predictor of progression. Eight patients died and four developed HCC all among the non- SVR group only. Conclusions This study describes, for the first time, the natural history and demonstrates the beneficial long-term effects of interferon-based therapy in HCV G4 patients. [ABSTRACT FROM AUTHOR]