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Purpose: To present the long-term results of redo-hypospadias at our tertiary referral center following a failed prior repair., Methods: One hundred sixty-four individuals with a history of unsuccessful repairs qualified for our retrospective cohort study. Our inclusion criteria were as follows: pre-operative data that was accessible, redo-hypospadias that was successfully repaired, and at least three years of follow-up at the last hospital visit., Results: The mean patient age was 91.3 ± 21.1 months. The mean follow-up after successful repair was 41.3 ± 3.1 months. Ninety-two (group A) had one prior repair, and 72 (group B) had 2 or 3 repairs. Group A underwent six primary techniques: 32 underwent Onlay Island Flap (OIF), 10 underwent Mathieu, 12 underwent Tubularized Incised Plate Urethroplasty (TIPU), 8 underwent Urethral Mobilization (UM), and 34 underwent Buccal Mucosal Graft (BMG) { dorsal inlay Graft Urethroplasty (DIGU) in 4 and staged BMG in 30 patients}. In group B, four procedures were used: TIPU in 4, UM in 6, and BMG in 62 (staged BMG in 50 cases and DIGU in 12)., Conclusions: The selected type of repair will depend on many factors, like residual healthy local skin and expertise. Safe techniques for repair of redo hypospadias after its 1st failure include TIPU, Mathieu, UM, OIF, and DIGU for distal varieties. After 2nd or 3rd repair DIGU, UM, and TIPU can be performed in distal types, while staged BMG can be applied for proximal ones., (© 2024. The Author(s).)

Objective: To assess the efficacy of primary urethral realignment in the prevention of urethral stenosis and in simplifying delayed urethroplasty after complete pelvic fracture urethral injury in male children., Methods: This randomized comparative trial included 40 boys <18 years with complete pelvic fracture urethral injury. The initial management was a primary urethral realignment in 20 boys and suprapubic cystostomy alone in the remaining 20 boys. The boys who underwent primary urethral realignment were assessed regarding the development of urethral stenosis. Boys who needed to be delayed urethroplasty in the two groups were compared regarding urethral defect length, intraoperative details, postoperative outcomes, number of procedures, and time to achieve normal voiding., Results: Although 14 (70%) patients were able to void after primary urethral realignment, all of them developed urethral stenosis and needed delayed urethroplasty. No statistically significant difference between the two groups was found regarding urethral defect length, intraoperative details, and postoperative outcomes. Patients in the primary urethral realignment group underwent significantly more procedures (p < 0.001) and took a significantly longer time to achieve normal voiding (p = 0.002)., Conclusion: Primary urethral realignment is neither able to prevent urethral stenosis nor effective in simplifying later urethroplasty after complete pelvic fracture urethral injury in male children. It exposes the patients to more surgical procedures and a prolonged clinical course., (© 2023 The Japanese Urological Association.)

Objective: To compare the efficacy and morbidity of transurethral cystolithotripsy (TUCL) and percutaneous CL (PCCL) in the management of bladder stones in male children., Patients and Methods: A total of 100 boys, aged <14 years with a single bladder or urethral stone of <30 mm, were randomised into two equal groups. Initial diagnostic urethro-cystoscopy and push back of urethral stones were done for patients in both groups. Patients in Group A had TUCL, while those in Group B had PCCL through a 20-F sheath using a 12-F nephroscope. The two groups were compared regarding preoperative criteria, intraoperative details, and postoperative outcomes., Results: The patients in this study had a median (range) age of 36 (4-144) months and stone size of 10 (5-26) mm. There was no statistically significant difference between the two groups for preoperative criteria. The assigned procedure was successful in 48 (96%) patients in Group A and 49 (98%) in Group B (P = 1). Complications were encountered in 11 (22%) patients in Group A and five (10%) in Group B (P = 0.171). The median (range) operative time was 21.5 (4-90) min in Group A and 13 (5-70) min in Group B (P < 0.001). In all, 47 (94%) stones needed disintegration in Group A vs 22 (44%) in Group B (P < 0.001)., Conclusion: Both techniques have comparable success and complications rates. However, PCCL has a shorter operative time and less need for stone disintegration., (© 2022 The Authors BJU International © 2022 BJU International.)

Oxidative stress is involved in the pathophysiology of multiple health complications, and it has become a major focus in targeted research fields. As known, black seeds are rich sources of bio-active compounds and widely used to promote human health due to their excellent medicinal and pharmaceutical properties. The present study investigated the antioxidant potency of various black seeds from plants and their derived mycoendophytes, and determined the total phenolic and flavonoid contents in different extracts, followed by characterization of major constituents by HPLC analysis. Finally, in silico docking determined their binding affinities to target myeloperoxidase enzymes. Ten dominant mycoendophytes were isolated from different black seed plants. Three isolates were then selected based on high antiradical potency and further identified by ITS ribosomal gene sequencing. Those isolated were Aspergillus niger TU 62, Chaetomium madrasense AUMC14830, and Rhizopus oryzae AUMC14823. Nigella sativa seeds and their corresponding endophyte A. niger had the highest content of phenolics in their n- butanol extracts (28.50 and 24.43 mg/g), flavonoids (15.02 and 11.45 mg/g), and antioxidant activities (90.48 and 81.48%), respectively, followed by Dodonaea viscosa and Portulaca oleracea along with their mycoendophytic R. oryzae and C. madrasense . Significant positive correlations were found between total phenolics, flavonoids, and the antioxidant activities of different tested extracts. The n -butanol extracts of both black seeds and their derived mycoendophytes showed reasonable IC 50 values (0.81-1.44 mg/ml) compared to the control with significant correlations among their phytochemical contents. Overall, seventeen standard phenolics and flavonoids were used, and the compounds were detected in different degrees of existence and concentration in the examined extracts through HPLC analysis. Moreover, the investigation of the molecular simulation results of detected compounds against the myeloperoxidase enzyme revealed that, as a targeted antioxidant, rutin possessed a high affinity (-15.3184 kcal/mol) as an inhibitor. Taken together, the black seeds and their derived mycoendophytes are promising bio-prospects for the broad industrial sector of antioxidants with several valuable potential pharmaceutical and nutritional applications., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Hassane, Hussien, Abouelela, Taha, Awad, Mohamed, Hassan, Hassan, Abo-Dahab and El-Shanawany.)

Background: Nuclei classification, segmentation, and detection from pathological images are challenging tasks due to cellular heterogeneity in the Whole Slide Images (WSI)., Methods: In this work, we propose advanced DCNN models for nuclei classification, segmentation, and detection tasks. The Densely Connected Neural Network (DCNN) and Densely Connected Recurrent Convolutional Network (DCRN) models are applied for the nuclei classification tasks. The Recurrent Residual U-Net (R2U-Net) and the R2UNet-based regression model named the University of Dayton Net (UD-Net) are applied for nuclei segmentation and detection tasks respectively. The experiments are conducted on publicly available datasets, including Routine Colon Cancer (RCC) classification and detection and the Nuclei Segmentation Challenge 2018 datasets for segmentation tasks. The experimental results were evaluated with a five-fold cross-validation method, and the average testing results are compared against the existing approaches in terms of precision, recall, Dice Coefficient (DC), Mean Squared Error (MSE), F1-score, and overall testing accuracy by calculating pixels and cell-level analysis., Results: The results demonstrate around 2.6% and 1.7% higher performance in terms of F1-score for nuclei classification and detection tasks when compared to the recently published DCNN based method. Also, for nuclei segmentation, the R2U-Net shows around 91.90% average testing accuracy in terms of DC, which is around 1.54% higher than the U-Net model., Conclusion: The proposed methods demonstrate robustness with better quantitative and qualitative results in three different tasks for analyzing the WSI., (© 2022. The Author(s).)

The Deep Convolutional Neural Network (DCNN) is one of the most powerful and successful deep learning approaches. DCNNs have already provided superior performance in different modalities of medical imaging including breast cancer classification, segmentation, and detection. Breast cancer is one of the most common and dangerous cancers impacting women worldwide. In this paper, we have proposed a method for breast cancer classification with the Inception Recurrent Residual Convolutional Neural Network (IRRCNN) model. The IRRCNN is a powerful DCNN model that combines the strength of the Inception Network (Inception-v4), the Residual Network (ResNet), and the Recurrent Convolutional Neural Network (RCNN). The IRRCNN shows superior performance against equivalent Inception Networks, Residual Networks, and RCNNs for object recognition tasks. In this paper, the IRRCNN approach is applied for breast cancer classification on two publicly available datasets including BreakHis and Breast Cancer (BC) classification challenge 2015. The experimental results are compared against the existing machine learning and deep learning-based approaches with respect to image-based, patch-based, image-level, and patient-level classification. The IRRCNN model provides superior classification performance in terms of sensitivity, area under the curve (AUC), the ROC curve, and global accuracy compared to existing approaches for both datasets.

Deep learning (DL)-based semantic segmentation methods have been providing state-of-the-art performance in the past few years. More specifically, these techniques have been successfully applied in medical image classification, segmentation, and detection tasks. One DL technique, U-Net, has become one of the most popular for these applications. We propose a recurrent U-Net model and a recurrent residual U-Net model, which are named RU-Net and R2U-Net, respectively. The proposed models utilize the power of U-Net, residual networks, and recurrent convolutional neural networks. There are several advantages to using these proposed architectures for segmentation tasks. First, a residual unit helps when training deep architectures. Second, feature accumulation with recurrent residual convolutional layers ensures better feature representation for segmentation tasks. Third, it allows us to design better U-Net architectures with the same number of network parameters with better performance for medical image segmentation. The proposed models are tested on three benchmark datasets, such as blood vessel segmentation in retinal images, skin cancer segmentation, and lung lesion segmentation. The experimental results show superior performance on segmentation tasks compared to equivalent models, including a variant of a fully connected convolutional neural network called SegNet, U-Net, and residual U-Net.

In spite of advances in object recognition technology, handwritten Bangla character recognition (HBCR) remains largely unsolved due to the presence of many ambiguous handwritten characters and excessively cursive Bangla handwritings. Even many advanced existing methods do not lead to satisfactory performance in practice that related to HBCR. In this paper, a set of the state-of-the-art deep convolutional neural networks (DCNNs) is discussed and their performance on the application of HBCR is systematically evaluated. The main advantage of DCNN approaches is that they can extract discriminative features from raw data and represent them with a high degree of invariance to object distortions. The experimental results show the superior performance of DCNN models compared with the other popular object recognition approaches, which implies DCNN can be a good candidate for building an automatic HBCR system for practical applications.

Introduction: Psychic anuria is an old term, referring to a very rare psycho-urological event that has scarcely been studied so far., Materials and Methods: A retrospective study of the patients with psychic anuria presented to Assiut Urology and Nephrology Hospital during the period July 1991-June 2016 was done. Psychic anuria was defined, and the demographic and clinical characteristics including the methods of diagnosis and management were studied., Results: Of more than 3,800 cases of anuria, 9 female patients (0.24%) experienced psychic anuria in the age range of 17-43 years. Cardinal clinical findings included anuria for 36-72 h with absence of organic causes and normal renal function tests. Psychosocial risk factors were reported in the 9 cases. Anuria was documented by reliable history (56%) or observable urine collection (44%). Diagnosis was done by exclusion, where the investigations revealed no organic causes. Seven cases responded to the placebo intervention and 2 cases were self-limiting and resolved spontaneously., Conclusions: Psychic anuria is an extremely rare urological emergency that presents, mainly, in young adult females with unknown mechanisms. Renal vasoconstriction following psychosocial stressors is suggested. It is diagnosed by exclusion and resolves spontaneously or responds to placebo intervention as a mental distraction technique., (© 2018 S. Karger AG, Basel.)

In this paper, the fractional-order generalized Laguerre operational matrices (FGLOM) of fractional derivatives and fractional integration are derived. These operational matrices are used together with spectral tau method for solving linear fractional differential equations (FDEs) of order ν (0 < ν < 1) on the half line. An upper bound of the absolute errors is obtained for the approximate and exact solutions. Fractional-order generalized Laguerre pseudo-spectral approximation is investigated for solving nonlinear initial value problem of fractional order ν. The extension of the fractional-order generalized Laguerre pseudo-spectral method is given to solve systems of FDEs. We present the advantages of using the spectral schemes based on fractional-order generalized Laguerre functions and compare them with other methods. Several numerical examples are implemented for FDEs and systems of FDEs including linear and nonlinear terms. We demonstrate the high accuracy and the efficiency of the proposed techniques.

Background: Sinonasal lesions are a heterogeneous group of lesions that span from a tumor to tumor-like nature. Characterization of such cases preoperatively can improve the surgical control and the overall outcome of these patients., Objective: In this prospective study, we aimed at evaluation of the role of apparent diffusion coefficient (ADC) in the differentiation between benign and malignant sinonasal lesions., Subjects and Methods: All patients scheduled to have sinonasal surgical intervention at Ain Shams University Hospitals, Cairo, Egypt, were enrolled. Diffusion-weighted (DW) magnetic resonance imaging (MRI) with calculation of ADC were done for all cases. Radiologic findings were then compared with histologic findings, and the sensitivity, specificity, negative and positive predictive values (PPVs) of the conventional MRI, DW-MRI, and ADC value in differentiation of benign from malignant sinonasal lesions were then calculated., Results: There were 59 patients with median age of 43 years old. There were 20 cases of inflammatory lesions, 16 cases of benign tumors, and 23 cases of malignant lesions. The ADC values of all cases ranged from 0.4 × 10(-3) to 2 × 10(-3) (median = 1.5 × 10(-3)). The median ADC value for the malignant lesions was 0.6 × 10(-3), whereas that for the inflammatory conditions was 1.6 × 10(-3) and that for the benign tumors was 1.5 × 10(-3) with a highly significant difference (p < .001). Analysis of the conventional MRI and DW-MRI to differentiate between malignant and benign lesions showed that the sensitivity, specificity, PPV, and negative predictive value (NPV) were 100%, 97%, 96%, and 100% and 91%, 97%, 95%, and 95%, respectively., Conclusion: DW-MRI did not add significantly to the information gained from conventional MRI. It should be considered complimentary only to standard MRI in uncertain cases when malignancy is still a concern.

There is currently a strong push in the research community to develop biological scale implementations of neuron based vision models. Systems at this scale are computationally demanding and generally utilize more accurate neuron models, such as the Izhikevich and the Hodgkin-Huxley models, in favor of the more popular integrate and fire model. We examine the feasibility of using graphics processing units (GPUs) to accelerate a spiking neural network based character recognition network to enable such large scale systems. Two versions of the network utilizing the Izhikevich and Hodgkin-Huxley models are implemented. Three NVIDIA general-purpose (GP) GPU platforms are examined, including the GeForce 9800 GX2, the Tesla C1060, and the Tesla S1070. Our results show that the GPGPUs can provide significant speedup over conventional processors. In particular, the fastest GPGPU utilized, the Tesla S1070, provided a speedup of 5.6 and 84.4 over highly optimized implementations on the fastest central processing unit (CPU) tested, a quadcore 2.67 GHz Xeon processor, for the Izhikevich and the Hodgkin-Huxley models, respectively. The CPU implementation utilized all four cores and the vector data parallelism offered by the processor. The results indicate that GPUs are well suited for this application domain.

Accurate automated alignment of laser beams in the National Ignition Facility (NIF) is essential for achieving extreme temperature and pressure required for inertial confinement fusion. The alignment achieved by the integrated control systems relies on algorithms processing video images to determine the position of the laser beam images in real time. Alignment images that exhibit wide variations in beam quality require a matched-filter algorithm for position detection. One challenge in designing a matched-filter-based algorithm is to construct a filter template that is resilient to variations in imaging conditions while guaranteeing accurate position determination. A second challenge is to process images for thousands of templates in under a second, as may be required in future high-energy laser systems. This paper describes the development of a new analytical template that captures key recurring features present in the beam image to accurately estimate the beam position under good image quality conditions. Depending on the features present in a particular beam, the analytical template allows us to create a highly tailored template containing only those selected features. The second objective is achieved by exploiting the parallelism inherent in the algorithm to accelerate processing using parallel hardware that provides significant performance improvement over conventional processors. In particular, a Xilinx Virtex II Pro field programmable gate array (FPGA) hardware implementation processing 32 templates provided a speed increase of about 253 times over an optimized software implementation running on a 2.2 GHz AMD Opteron core.

When tetrathionate-grown Acidithiobacillus ferrooxidans ATCC 23270 cells were incubated with ferric ions and tetrathionate at pH 3.0, ferrous ions were produced enzymatically. Fe(3+)-reductase, which catalyzes Fe(3+) reduction with tetrathionate, was purified to homogeneity not only from tetrathionate-grown, but also from sulfur- and iron-grown A. ferrooxidans ATCC 23270 cells. The results for apparent molecular weight measured by SDS-PAGE (52.3 kD) and the N-terminal amino acid sequences of the purified enzymes from iron-, sulfur, and tetrathionate-grown cells (AVAVPMDSTG) indicate that Fe(3+)-reductase corresponds to tetrathionate hydrolase. The evidence that tetrathionate-grown A. ferrooxidans ATCC 23270 cells have high iron-oxidizing activity at the early log phase, comparable to that of iron-grown ATCC 23270 cells, is supported by our finding that tetrathionate hydrolase produces Fe(2+) from tetrathionate during growth on tetrathionate. This is the first report on ferric reductase activity associated with tetrathionate hydrolase.

The iron oxidation system from sulfur-grown Acidithiobacillus ferrooxidans ATCC 23270 cells was reconstituted in vitro. Purified rusticyanin, cytochrome c, and aa(3)-type cytochrome oxidase were essential for reconstitution. The iron-oxidizing activity of the reconstituted system was 3.3-fold higher than that of the cell extract from which these components were purified.

When Acidithiobacillus ferrooxidans ATCC23270 cells, grown for many generations on sulfur were grown in sulfur medium with and without Fe(3+), the bacterium markedly increased not only in iron oxidase activity but also in Fe(2+)-producing sulfide:ferric ion oxidoreductase (SFORase) activity during the early log phase, and retained part of these activities during the late log phase. The activity of SFORase, which catalyzes the production of Fe(2+) from Fe(3+) and sulfur, of sulfur-grown cells was approximately 10-20 fold higher than that of iron-grown cells. aa(3) type cytochrome c oxidase, an important component of iron oxidase in A. ferrooxidans, was partially purified from sulfur-grown cells. A. ferrooxidans ATCC23270 cells grown for many generations on sulfur had the ability to grow on iron as rapidly as that did iron-grown cells. These results suggest that both iron oxidase and Fe(2+)-producing SFORase have a role in the energy generation of A. ferrooxidans ATCC23270 from sulfur.