The use of calixarene as ionophores in potentiometric ion-selective electrodes of naftidrofuryl oxalate using microsized membrane sensors for kinetic study of naftidrofuryl (NFT) degradation

Novel miniaturized polyvinyl chloride membrane sensors in all-solid state graphite and platinum wire supports were developed, electrochemically evaluated and used for the assay of naftidrofuryl oxalate. The naftidrofuryl oxalate sensors were based on the formation of an ion-association complex between the drug cation and tetrakis-(4-chlorophenyl)borateanionic exchanger as electroactive material and sulfocalix-8-arene as ionophore dispersed in a polyvinyl chloride matrix. Linear responses of 10-2-10-6 M and 10-2-10-5 M with cationic slopes of 56.9 mV and 54.1 mV over the pH range 2-5 were obtained by using the naftidrofuryl oxalate-coated graphite (Sensor 1) and platinum wire (Sensor 2) membrane sensors, respectively. The utility of 4-sulfocalix-8-arene as ionophore had a significant influence on increasing the membrane sensitivity and selectivity. The methods were also used to determine the intact drug in the presence of it’s degradate in Praxilene® tablets, plasma and cerebrospinal fluid with good recovery. Sensor 1 was used to study the kinetics of naftidrofuryl oxalate alkaline degradation that was found to follow a pseudo first-order reaction. The activation energy could be estimated from the Arrhenius plot to be 12.572 Kcal/mol

Automated Vision-Based High Intraocular Pressure Detection Using Frontal Eye Images

Glaucoma, the silent thief of vision, is mostly caused by the gradual increase of pressure in the eye which is known as intraocular pressure (IOP). An effective way to prevent the rise in eye pressure is by early detection. Prior computer vision-based work regarding IOP relies on fundus images of the optic nerves. This paper provides a novel vision-based framework to help in the initial IOP screening using only frontal eye images. The framework first introduces the utilization of a fully convolutional neural (FCN) network on frontal eye images for sclera and iris segmentation. Using these extracted areas, six features that include mean redness level of the sclera, red area percentage, Pupil/Iris diameter ratio, and three sclera contour features (distance, area, and angle) are computed. A database of images from the Princess Basma Hospital is used in this work, containing 400 facial images; 200 cases with normal IOP; and 200 cases with high IOP. Once the features are extracted, two classifiers (support vector machine and decision tree) are applied to obtain the status of the patients in terms of IOP (normal or high). The overall accuracy of the proposed framework is over 97.75% using the decision tree. The novelties and contributions of this work include introducing a fully convolutional network architecture for eye sclera segmentation, in addition to scientifically correlating the frontal eye view (image) with IOP by introducing new sclera contour features that have not been previously introduced in the literature from frontal eye images for IOP status determination.https://doi.org/10.1109/JTEHM.2019.291553

New Bioactive Compounds from Verticillium alboatrum and Verticillium leptobactrum

Abstract: Three new fungal metabolites were isolated and purified from the broth culture of two entomopathogenic fungi Verticillium alboatrum and Verticillium leptobactrum. The obtained compounds were screened for their antibacterial, antifungal, antiviral and antitumor activity. To test antibacterial, anti-yeast and antifungal activity, the secondary metabolites obtained by culturing fungi on YES broth for 21 days was screened by using agar well diffusion method. The active ingredients were purified and the chemical structures of the new metabolites were elucidated using the spectroscopic methods FTIR, 1 HNMR and EI-MS analyses. The cytotoxic and antitumor effects of the compounds on Vero and HEp2 cells, resepectively, were assayed. The first compound was benzene diamine derivative produced from fraction 5 of Verticillium alboatrum and showed activity against bacteria, fungi and polio virus. Compound 2 was benzylamine derivative produced from fraction 5 of Verticillium alboatrum with broad spectrum antimicrobial as well as weak antitumor activity. Compound 3 was butylamideaniline derivative obtained from Verticillium leptobactrum with antibacterial activity. The compound 3 also showed antitumor activity with acceptable therapeutic indexes. In conclusion: The study illustrates the biological activities of new fungal metabolites from Verticillium alboatrum and Verticillium leptobactrum, and is therefore, a potential drug and a good candidate for further studies and development

Screw nail medialization of arytenoid in unilateral adductor vocal cord paralysis: A new office procedure

AbstractObjectivesThe aim of this study is to describe and evaluate new technique for treatment of unilateral vocal cord paralysis giving the same results of the standard procedure but minimizing the surgical trauma.Patients and methodsA prospective study was carried out between November 2009 and November 2011 in the Otorhinolaryngology Department, Zagazig University Hospitals, Egypt. It included 12 patients with unilateral idiopathic vocal cord paralysis who were managed by this new technique. Preoperative and postoperative video-laryngoscopy was done, maximal phonation time was calculated and ratings grade of dysphonia was compared.ResultsPostoperative video-laryngoscopy relieved complete glottic gap closure in 83.3% of cases and partial glottic gap in the remaining 16.7% of cases. The mean preoperative maximal phonation time was 9.42s and the postoperative value was 21.5s with a mean difference of 11.6s. The rating of the grade of dysphonia showed great improvement in the patient’s voice quality from severe dysphonia preoperatively to normal or near-normal voice postoperatively. All patients were satisfied with the results of the procedure except one patient (8.3%).ConclusionThe screw nail medialization technique gives a good reliable and easy procedure under local anesthesia for managing unilateral vocal cord paralysis and avoids the disadvantages of infection laryngoplasty and medialization thyroplasty using implants

Innovative Method for enhancing Key generation and management in the AES-algorithm

With the extraordinary maturity of data exchange in network environments and increasing the attackers capabilities, information security has become the most important process for data storage and communication. In order to provide such information security the confidentiality, data integrity, and data origin authentication must be verified based on cryptographic encryption algorithms. This paper presents a development of the advanced encryption standard (AES) algorithm, which is considered as the most eminent symmetric encryption algorithm. The development focuses on the generation of the integration between the developed AES based S-Boxes, and the specific selected secret key generated from the quantum key distribution.Comment: 7 pages, 10 figures. arXiv admin note: text overlap with arXiv:1503.0479

Fixed soft point theorems for generalized contractive mapping on soft metric spaces

In this paper, we introduce new notions in a soft metric space. We study a fixed soft point under generalized contractive conditions without mappings continuity. Further, we prove some results related to our generalization. Moreover, we provide one example to present the application.Publisher's Versio

The Structure and Psychometric Properties of Wellness for A Sample of Teachers in Egypt

The present study attempts to examine the structure of wellness for a sample of teachers in Egypt and examine psychometric properties of wellness Inventory. The study questions addressed are: is the structure of wellness multidimensional or uni-dimensional?, Is wellness inventory has appropriate psychometric properties?. A total of 417 teachers from Egypt completed the inventory of wellness, including males (N= 260) and females (N= 157).The teachers ranged in age from 29 to 48 with an average age 36.7 and S.D. 1.7 .  The inventory that used in this study is referred to as the multidimensional organizational wellness inventory (MOWI) (Fasone, 2017), Confirmatory factor analysis (CFA) is used. The results indicates that The structure of wellness is multidimensional which includes physical, emotional, social, intellectual, spiritual, occupational , and environmental. And the inventory has appropriate psychometric properties. Keywords: Wellness, Reliability, Construct Validity DOI: 10.7176/RHSS/9-16-04 Publication date: August 31st 201

Skin prick test results and total ige levels of asthma patients in Zagazig University Hospital (2015-2019)

Background: Bronchial asthma is one of the relevant diseases of the respiratory tract, the asthma is one of the forms of respiratory allergy. The change in environment and aeroallergens are the main etiology of asthma. Allergy of asthma is thought to affect the bronchial region of the respiratory airway.Objective: The current study aimed to compare the total IgE elevation and skin prick test (SPT) positivity to evaluate the IgE and SPT ability to assess the asthmatic severity.Subjects and Methods: This retrospective case study was carried out on 3450 cases at the outpatient clinic and Chest Department at Zagazig University and did skin prick test and IgE in authorized centers were collected from 2015 -2019. The cases were already diagnosed asthmatic according to Global Initiative for Asthma (GINA) (9) guidelines and were divided according to severity into mild, moderate, and severe according to GINA guidelines.Results: IgE has been tested for different aeroallergens and has strong significant elevation with (P< 0.001) in Alternaria, cat hair, cotton, birch, and helminths aeroallergen. Most of the cases with elevated IgE had moderate asthma severity followed by mild severity cases then severe cases.Conclusion: Comparing the results of SPT and IgE, the SPT test is more accurate, reliable, and easy in detecting the aeroallergen sensitivity

Deep learning-based adaptive compression and anomaly detection for smart B5G use cases operation

The evolution towards next-generation Beyond 5G (B5G) networks will require not only innovation in transport technologies but also the adoption of smarter, more efficient operations of the use cases that are foreseen to be the high consumers of network resources in the next decades. Among different B5G use cases, the Digital Twin (DT) has been identified as a key high bandwidth-demanding use case. The creation and operation of a DT require the continuous collection of an enormous and widely distributed amount of sensor telemetry data which can overwhelm the transport layer. Therefore, the reduction in such transported telemetry data is an essential objective of smart use case operation. Moreover, deep telemetry data analysis, i.e., anomaly detection, can be executed in a hierarchical way to reduce the processing needed to perform such analysis in a centralized way. In this paper, we propose a smart management system consisting of a hierarchical architecture for telemetry sensor data analysis using deep autoencoders (AEs). The system contains AE-based methods for the adaptive compression of telemetry time series data using pools of AEs (called AAC), as well as for anomaly detection in single (called SS-AD) and multiple (called MS-AGD) sensor streams. Numerical results using experimental telemetry data show compression ratios of up to 64% with reconstruction errors of less than 1%, clearly improving upon the benchmark state-of-the-art methods. In addition, fast and accurate anomaly detection is demonstrated for both single and multiple-sensor scenarios. Finally, a great reduction in transport network capacity resources of 50% and more is obtained by smart use case operation for distributed DT scenarios.This research was funded by the European Commission Horizon Europe SNS JU DESIRE6G project (G.A. 101096466), by the AEI through the IBON project (PID2020-114135RB-I00), and by the ICREA institution.Peer ReviewedPostprint (published version

Deep learning-based adaptive compression and anomaly detection for smart B5G use cases operation

The evolution towards next-generation Beyond 5G (B5G) networks will require not only innovation in transport technologies but also the adoption of smarter, more efficient operations of the use cases that are foreseen to be the high consumers of network resources in the next decades. Among different B5G use cases, the Digital Twin (DT) has been identified as a key high bandwidth-demanding use case. The creation and operation of a DT require the continuous collection of an enormous and widely distributed amount of sensor telemetry data which can overwhelm the transport layer. Therefore, the reduction in such transported telemetry data is an essential objective of smart use case operation. Moreover, deep telemetry data analysis, i.e., anomaly detection, can be executed in a hierarchical way to reduce the processing needed to perform such analysis in a centralized way. In this paper, we propose a smart management system consisting of a hierarchical architecture for telemetry sensor data analysis using deep autoencoders (AEs). The system contains AE-based methods for the adaptive compression of telemetry time series data using pools of AEs (called AAC), as well as for anomaly detection in single (called SS-AD) and multiple (called MS-AGD) sensor streams. Numerical results using experimental telemetry data show compression ratios of up to 64% with reconstruction errors of less than 1%, clearly improving upon the benchmark state-of-the-art methods. In addition, fast and accurate anomaly detection is demonstrated for both single and multiple-sensor scenarios. Finally, a great reduction in transport network capacity resources of 50% and more is obtained by smart use case operation for distributed DT scenarios.This research was funded by the European Commission Horizon Europe SNS JU DESIRE6G project (G.A. 101096466), by the AEI through the IBON project (PID2020-114135RB-I00), and by the ICREA institution.Peer ReviewedPostprint (published version