Search

Objective The aim of this work is to evaluate the safety and efficacy of repeated sessions of modified corneal collagen cross linking (M-CXL) combined with intrastromal injection of voriconazole for the treatment of severe fungal keratitis with ongoing corneal melting and compare the outcome with intrastromal injection of voriconazole alone. Patients and Methods This is a retrospective comparative clinical cohort study. Thirty eyes with clinically suspected and lab-confirmed severe fungal keratitis with ongoing corneal melting were included. These eyes were classified randomly into two groups each of 15 eyes. In group (A), all the eyes underwent repeated sessions of M-CXL with frequent instillations (FI) of topical voriconazole each week till the reversal of corneal melting, then intrastromal injections of voriconazole were added each week till complete corneal healing together with negative culture on lab examination. In group (B), all the eyes underwent repeated intrastromal injections of voriconazole each week together with frequent instillations (FI) of topical voriconazole till complete corneal healing with negative culture on lab examination. Identification of organisms was done by lab study before and after treatment. Corneal healing was evaluated by corneal examination and anterior segment OCT (AS-OCT). Results Successful treatment was reported in 13 eyes (86.7%) of group (A), while in group (B), successful treatment was reported in 9 eyes (60%), while failure of treatment with complications was reported in 2 eyes (13.3%) in group (A) , however, in group (B), failure of treatment with complications was reported in 6 eyes (40%), with statistically significant difference (p Conclusion M-CXL followed by intrastromal injection of voriconazole was found to be effective in treating severe fungal keratitis with ongoing corneal melting due to the known action of CXL for reversal of corneal melting and anti-infective properties. Our results revealed that combined M-CXL and intrastromal injection of voriconazole was safer than intrastromal injection of voriconazole alone for treating severe fungal keratitis with corneal melting with better visual outcomes.

Novel series of aminopyrimidines bearing a biologically active cyclohexenone 3a–f and oxo-selaneylidene moiety 4, besides selenadiazolopyrimidines (5a–e and 7), were synthesised using 5,6-diaminouracils as starting materials. Compound 3a exhibited strong anti-proliferative activity against three cell lines: HepG-2 (IC50 14.31 ± 0.83 µM), A-549 (IC50 30.74 ± 0.76 µM), and MCF-7 (IC50 27.14 ± 1.91 µM). Also, it was four times more selectively cytotoxic against WI-38 cell lines than doxorubicin. Furthermore, Topoisomerase II (IC50 4.48 ± 0.65 µM) and HSP90 (IC50 1.78 ± 0.11 µM) were both strongly inhibited in vitro by 3a. The cell cycle was halted at the G1-S phase, and total apoptotic cells were 65 times more than control Hep-G2 cells. Besides, it increased caspase-3 gene expression, triggering mitochondrial cell death. Molecular docking study indicated that it could bind to Topoisomerase II and HSP90 binding sites in an inhibitory mode. Its geometric properties were investigated using the density functional theory (DFT). Furthermore, compound 3a demonstrated in silico good oral bioavailability.

The current exponential advancements in the Internet of Things (IoT) technologies pave a vast intelligent computing platform by integrating smart objects with sensing, processing and communication capabilities. The core element of IoT is the complex big data generated from different interconnected sources at real-time, presenting divergent processing and analysis challenges. Best practices in software engineering have been continuously addressed in IoT technologies to handle such big data efficiently at different domains. Despite of the massive studies dedicated for IoT, no explicit processing architecture is proposed based on real investigation of software engineering concepts and big data analytics characteristics in IoT. This paper provides a systematic literature review for the current state-of-the-art of IoT systems in different domains. The study investigates the current techniques and technologies that serve IoT systems from the big data analytics and software engineering perspectives, revealing a matrix for the specific IoT data features and their encountered challenges and gaps for each domain. The review deduces a proposed domain-independent software architecture for big IoT data analytics, maintaining various IoT data processing challenges, including data scalability, timeliness, heterogeneity, inconsistency, confidentiality and correlations. Finally, the main research gaps are emphasized for future considerations.

The increasing incidence of erythromycin and erythromycin-induced resistance to clindamycin among Staphylococcus aureus (S. aureus) is a serious problem. Patients infected with inducible resistance phenotypes may fail to respond to clindamycin. This study aimed to identify the prevalence of erythromycin and erythromycin-induced resistance and assess for potential inhibitors. A total of 99 isolates were purified from various clinical sources. Phenotypic detection of macrolide-lincosamide-streptogramin B (MLSB)-resistance phenotypes was performed by D-test. MLSB-resistance genes were identified using PCR. Different compounds were tested for their effects on erythromycin and inducible clindamycin resistance by broth microdilution and checkerboard microdilution methods. The obtained data were evaluated using docking analysis. Ninety-one isolates were S. aureus. The prevalence of constitutive MLSB, inducible MLSB, and macrolide-streptogramin (MS) phenotypes was 39.6%, 14.3%, and 2.2%, respectively. Genes including ermC, ermA, ermB, msrA, msrB, lnuA, and mphC were found in 82.6%, 5.8%, 7.7%, 3.8%, 3.8%, 13.5%, and 3.8% of isolates, respectively. Erythromycin resistance was significantly reduced by doxorubicin, neomycin, and omeprazole. Quinine, ketoprofen, and fosfomycin combated and reversed erythromycin/clindamycin-induced resistance. This study highlighted the significance of managing antibiotic resistance and overcoming clindamycin treatment failure. Doxorubicin, neomycin, omeprazole, quinine, ketoprofen, and fosfomycin could be potential inhibitors of erythromycin and inducible clindamycin resistance.

Nanotechnology-based strategies can overcome the limitations of conventional cancer therapies. Hence, novel series of pyrimidine Schiff bases (4–9) were employed in the synthesis of selenium nanoparticle forms (4NPs–9NPs). All selenium nano-sized forms exerted greater inhibitions than normal-sized compounds, far exceeding 5-fluorouracil activity. Compound 4 showed effective anti-proliferative activity against MCF-7(IC50 3.14 ± 0.04 µM), HepG-2(IC50 1.07 ± 0.03 µM), and A549(IC50 1.53 ± 0.01 µM) cell lines, while its selenium nanoform 4NPs showed excellent inhibitory effects, with efficacy increased by 96.52%, 96.45%, and 93.86%, respectively. Additionally, 4NPs outperformed 4 in selectivity against the Vero cell line by 4.5-fold. Furthermore, 4NPs exhibited strong inhibition of CDK1(IC50 0.47 ± 0.3 µM) and tubulin polymerase(IC50 0.61 ± 0.04 µM), outperforming 4 and being comparable to roscovitine (IC50 0.27 ± 0.03 µM) and combretastatin-A4(IC50 0.25 ± 0.01 µM), respectively. Moreover, both 4 and 4NPs arrested the cell cycle at G0/G1 phase and significantly forced the cells towards apoptosis. Molecular docking demonstrated that 4 and 4NPs were able to inhibit CDK1 and tubulin polymerase binding sites.

The present study was carried out during the spring season of 2021 to evaluate some biological technological and physiological parameters of the silkworm, Bombyx mori L. fed on mulberry leaves Morus alba supplemented with royal jelly and palm pollen. Royal jelly at 2, 4 % and palm pollen at 50, 75 % increased significantly the larval weight, silk gland weight, cocoon weight, cocoon shell weight, pupal weight, the fecundity of female moth and improved the silk filament characters. Palm pollen at 75 and 50 % enhanced all physiological characters. Results established potential enhancement in most biological, cocoon and silk production by enriching mulberry leaves with both royal jelly and palm pollen.

To study the host range of Rose rosette virus (RRV), we employed crude sap inoculum extracted from RRV-infected roses and the RRV infectious clone. We inoculated plants from the families Solanaceae, Cucurbitaceae, Leguminosae, Malvaceae, Amaranthaceae, and Brassicaceae. Reverse transcription-polymerase chain reaction (RT-PCR) was used to detect RRV in the inoculated plants throughout their growth stages. Interestingly, RRV was detected in the newly developed leaves of tomato, pepper, tobacco, cucumber, squash, zucchini, pumpkin, pea, peanut, soybean, spinach, okra, and Chenopodium spp. The speed of upward advancement of RRV within infected plants was variable between plants as it took two to three weeks for some plant species and up to five weeks in other plant species to emerge in the newest leaves. No severe symptoms were detected on most of the inoculated plants. Chenopodium spp., spinach, cucumber and Nicotiana rustica exhibited either chlorotic or necrotic lesions with variable shapes and patterns on the systemically infected leaves. Double membrane-bound particles of 80–120 nm in diameter were detected by transmission electron microscopy in the infected tissues of cucumber, pepper, and N. benthamiana plants. This finding infers the validity of mechanical inoculation for RRV on a wide range of plants that would serve as potential natural reservoirs.

To study the host range of

Bioelectrical time signals are the signals that can be measured through the electrical potential difference across an organ over the time. Electroencephalography (EEG) signals and Electromyography (EMG) signals are among the best-known bioelectrical signals used for medical diagnosis and motion classification. In traditional machine learning methods, the task of extracting unique patterns and features from both bioelectrical signals is hard and requires a specific expert knowledge to study the non-linear, non-stationary and complex nature of these signals. With recent advancement in deep learning methods, features can be extracted from raw data without any handcrafted features. In this paper, a new deep learning approach that integrates EEG with EMG signals is proposed to investigate the efficiency of deep learning in hybrid systems with signal fusion and study the effect of hyper parameters tuning to enhance classification accuracy and boost the performance of hand and wrist motion control without manual feature engineering. Three deep learning models including Convolution Neural Networks CNN model, Long Short Term recurrent neural networks model LSTM, and a combined CNN–LSTM model, were proposed for hybrid system for signal classification. Experiments were tested on a dataset of multi-channel EEG signals merged with multi-channel sEMG signals, to decode hand and wrist motion. Experimental results signify that the proposed deep learning models achieve high classification accuracies that outperform other traditional machine learning state-of-the-art methods with up to 3.5% improvement ratio which indicates the promising application of the approach. Consequently, this work contributes to an automatic classification that facilitates and improves the real-time control of bio-robotics applications, mainly for limb movement classification.

Background: Hyperglycemia is frequent during steroid therapy and thus it is not uncommon during treatment of lymphoid malignancies. Steroid-induced diabetes (SID) can be complicated by an increased risk of infections, lower chemotherapy efficacy, and even increased mortality. Aim: To determine the prevalence of SID in patients with aggressive lymphoid malignancies during induction therapy and to analyze its impact on treatment outcomes. Methods: The study included 52 patients with lymphoid malignancies; 28 with acute lymphoblastic leukemia (ALL) and 24 with aggressive non-Hodgkin’s lymphomas (NHL). We studied the relation between the development of SID during induction therapy and the rates of complete remission (CR), complication and relapse and survival. Results: Steroid-induced diabetes occurred during induction therapy in 18/28 (64%) and 8/24 (33%) of patients with ALL and NHL, respectively. Older age, and elevated bilirubin level were significantly associated with the development of SID during induction therapy in ALL patients (p = 0.02 and 0.005, respectively), while only older age showed a significant association in NHL patients (p = 0.002). Compared with patients who did not develop SID, those with SID had significantly higher prevalence of febrile neutropenia in the ALL group (p = 0.001) and pneumonia in the NHL group (p = 0.009). Both ALL and NHL patients with SID were significantly less likely to achieve CR and had a significantly worse overall survival. Conclusion: The results of this study suggest that SID is frequent during induction therapy in patients with lymphoid malignancies and associated with more complications and worse treatment outcomes.

Different respiratory infections cause abnormal symptoms in lung parenchyma that show in chest computed tomography. Since December 2019, the SARS-COV-2 virus, which is the causative agent of COVID-19, has invaded the world causing high numbers of infections and deaths. The infection with SARS-COV-2 virus shows an abnormality in lung parenchyma that can be effectively detected using Computed Tomography (CT) imaging. In this paper, a novel computer aided framework (COV-CAF) is proposed for classifying the severity degree of the infection from 3D Chest Volumes. COV-CAF fuses traditional and deep learning approaches. The proposed COV-CAF consists of two phases: the preparatory phase and the feature analysis and classification phase. The preparatory phase handles 3D-CT volumes and presents an effective cut choice strategy for choosing informative CT slices. The feature analysis and classification phase incorporate fuzzy clustering for automatic Region of Interest (RoI) segmentation and feature fusion. In feature fusion, automatic features are extracted from a newly introduced Convolution Neural Network (Norm-VGG16) and are fused with spatial hand-crafted features extracted from segmented RoI. Experiments are conducted on MosMedData: Chest CT Scans with COVID-19 Related Findings with COVID-19 severity classes and SARS-COV-2 CT-Scan benchmark datasets. The proposed COV-CAF achieved remarkable results on both datasets. On MosMedData dataset, it achieved an overall accuracy of 97.76% and average sensitivity of 96.73%, while on SARS-COV-2 CT-Scan dataset it achieves an overall accuracy and sensitivity 97.59% and 98.41% respectively.

In this study, the effect of different diets on the biological characteristics of Coccinella undecimpunctata L. were studied under laboratory conditions of 26 ± 1°C and 65 ± 5% R.H. Three diets namely; AD1 (as basic artificial diet) , AD2 (as improvement artificial diet), aphid, Aphis craccivora Koch frozen as well as live aphid, (A. craccivora) as control were evaluated on the biological aspects of C. undecimpunctata . Results showed that the mean larval duration recorded 10.64 days, when larvae fed on control. It extended to 14.26, 14.56 and 21.27 days for frozen aphid, AD2 and AD1, respectively. Larval survival percentages were 93.33, 83.33, 73.33, and 60.00% when larvae reared on control, AD2, AD1 and frozen aphid, respectively. The maximum pupation percentage (92.86%) recorded in control, while minimum (66.67%) on frozen aphid. Adult emergence percentages on control was 92.31%, with sex ratio of 54.17%. Whereas, it 80.00 % with sex ratio of 62.50 % when larvae fed on AD2.Ovipositional period of mated female extended to 56.63 days and reproduced about 992.26 eggs throughout its lifespan, on control, whereas the lowest one (189.50 eggs) was noticed with AD1. Treatment control and AD2 gave the best result for egg fertility (95.62 and 75.90 %), egg hatching (95.01and 79.20 %) , respectively.Highest growth index was recorded with control (4.75) almost similar to artificial diet (AD2) (3.39). In general, AD2 played an important role in whole life cycle of C. undecimpunctata for mass production and proved better on most above parameters.

Marburg virus (MARV) is one of the most harmful zoonotic viruses with deadly effects on both humans and nonhuman primates. Because of its severe outbreaks with a high rate of fatality, the world health organization put it as a risk group 4 pathogen and focused on the urgent need for the development of effective solutions against that virus. However, up to date, there is no effective vaccine against MARV in the market. In the current study, the complete proteome of MARV (seven proteins) was analyzed for the antigenicity score and the virulence or physiological role of each protein where we nominated envelope glycoprotein (Gp), Transcriptional activator (VP30), and membrane-associated protein (VP24) as the candidates for epitope prediction. Following that, a vaccine construct was designed based on CTL, HTL, and BCL epitopes of the selected protein candidates and to finalize the vaccine construct, several amino acid linkers, β-defensin adjuvant, and PADRE peptides were incorporated. The generated potential vaccine was assessed computationally for several properties such as antigenicity, allergenicity, stability, and other structural features where the outcomes of these assessments nominated this potential vaccine to be validated for its binding affinity with two molecular targets TLR-8 and TLR-4. The binding score and the stability of the vaccine-receptor complex, which was deeply studied through molecular docking-coupled dynamics simulation, supported the selection of our designed vaccine as a putative solution for MARV that should be validated through future wet-lab experiments. Here, we describe the computational approach for designing and analysis of this potential vaccine.

The stock solution 10% and half stock solution 5% of three oils namely; mineral oil (Diver oil 97 % EC), plant oils marjoram oil (Origanum majorana L.) and orange oil (Citrus aurantium L.), as well as two species of bacteria Bacillus megaterium and BTS1 (Bacillus poylmyxa)at two concentrations showed that nematicidal activity against egg-mass and second stage juveniles of Meloidogyne incognitaKofoid in vitro experiments. Egg hatching and juvenile mortality were significantly (P ≤ 0.05) influenced by tested materials, concentration and exposure time. B. megaterium, diver oil and marjoram oil gave a higher effect, while B. poylmyxa was the lowest effective one. In case of juvenile mortality, B. poylmyxa, diver oil and marjoram oil gave the highest percentage of juvenile mortality (75.14, 73.00 and 61.85 %) after exposure at stock solutions (10%) and (53.00 , 67.42 and 52.57 %) with exposure to half stock solution (5%) after seven days of treatment, respectively. Under greenhouse conditions, results indicated that, oxamyl, dry leaf powder of moringa and B. megaterium were the most effective in suppressing root galling of M. incognita infectingcucumber plants. The maximum percentage of increase in shoot fresh weight was recorded on cucumber 43.26 and 25.36% in pots treated with oxamyl and B. megaterium, respectively. A moderate effect on treated eggs with the tested eco-friendly materials was observed on invasion and reproduction of M.incognita under greenhouse conditions. All tested materials significantly (P ≤ 0.05) reduced nematode parameters and increased cucumber plant growth parameters as compared with control treatment. Strongly decrease in number of egg masses and galls diameter was recorded in pots treated with oxamyl, moringa and B. megaterium followed by oils. Minimum number and gall diameter particularly ≥ 4mm was recorded in cucumber plants treated by oxamyl, moringa and B. megaterium. Data showed that moringa and B. megaterium could be used to increase crop yield of cucumber plants and for controlling root – knot nematode, M. incognita.

Test cases prioritization has been excessively considered for continious regression and integration testing in Internet of Things based systems to apply multilevel testing activities. Various number of devices, sensors and acctuators are connected together through the internet using different technologies, which requires extensively testing the effeciency of these components and the transferred data between them. Due to the number of the connected components has dramatically increased, causing a direct proportional increase in the number of test cases.Studies that handle the augmentation of the number of test cases for traditional systems lack effeciency when applied for Internet of Things based systems. In this paper, we introduce an enhancement for test cases prioritization using Hill Climbing algorithm as a local search based technique, adapted to achieve tangible effeciency. It is integrated with the LSTM deep learning algorithm for test cases classification purposes. The results of the test cases prioritization using Hill Climbing for regression and integration testing are evaluated using precision, where it achieved 80% and 97% for regression testing, and 93% and 88% for integration testing using two Internet of Things-based system datasets.

Many studies have addressed e-learning, aiming to create a platform for the learning process that completes the traditional classroom work and maximizes the effectiveness of learning outcomes. Gamifying personalized adaptable educational systems have been recently considered to keep the learners motivated and positively progressing in a flow state. However, the current models remain inadequate, providing limited resources for comprehensive learning analytics. In this paper, a theoritical learning preferences adaptation model is proposed based on the Personalized Adaptive Gamified E-learning (PAGE) model. The PAGE model supports blended learning by enforcing the engagement of the traditional learning process’s parties, where effective learning analytics can be sustained to continuously improve the quality of the learning experience. The overall model has been evaluated for its validity through a survey from different perspectives. The overall mean value of the evaluation is 2.77 out of 3. Thus, the evaluation outcomes for the adaptation, gamification, and learning experience of the PAGE model ensure a promising vision for advancements in the learning processes and analytics.

Purpose To understand patient attitudes, access toward video calling to enhance efficiency of after-hours triage calls. Methods We surveyed patients aged 18 to 89 years. Questions included demographics, preferences, access to video calling devices, and perceived advantages and disadvantages of this technology. Answers were entered into Qualtrics database and analyzed using JMP 11 (SAS, Cary, NC). Results Two hundred ninety-eight patients agreed to participate. Mean age was 47.9 years; 71.6% were female; and 75.1% had access to video calling device. Device proficiency was inversely related to age and greatest in 18-to-32-years group (χ2 = 71.18, P Conclusions Patients seem to have access and interest in video communication for after-hours calls. Further studies are needed to evaluate whether addition of video component to after-hours triage calls will help reduce unnecessary emergency department visits.

Reducing data stored and transferred is a critical topic in the modern era, particularly after the evolution in multimedia applications and surveillance systems worldwide. Motivated by the massive amount of data generated by surveillance cameras and the enormous number of redundant pixels produced among them, this paper introduces a novel model entitled: “A Smart Multi-View Panoramic Imaging integrating stitching with geometric matrix relations among surveillance cameras (SMPI).” The introduced model aims to create a novel feedback real-time stitching system to reduce the storing and transferring of redundant data generated by neighboring surveillance cameras for an extra level of compression. Moreover, the panoramic view is mostly a better monitoring option rather than multiple monitors in complicated surveillance cameras’ control rooms. The proposed system, in this paper, merges feature extraction stitching techniques with geometric relational matrix calculations to reduce the time complexity limitations of traditional mosaicking. Additionally, the proposed work introduces a real-time algorithm to reconstruct images of each camera from the panoramic view, and a novel algorithm for ordering cameras’ frames before stitching is recommended for producing a panoramic view without any human interference. The experimental work tests numerous state of the art feature extraction algorithms for stitching, Scale Invariant Feature Transform (SIFT), Speed Up Robust Feature (SURF) and Oriented FAST and Rotated BRIEF (ORB) with different orders of stitching. The amount of compression per image after reconstruction is also analyzed. The suggested model was implemented and tested using a vast number of benchmark datasets. Evaluation measures have been used to indicate the efficiency of the recommended system. The proposed model’s algorithm has recorded a low time processing per frame while keeping high accurate results. It was found that the recommended Efficient Stitching Algorithm (ESA) produced an average of 46 panoramas per second, and the reconstruction phase could reach a rate of 90 frames per second, which is significantly higher than the 30 frames per second standard video format system. These results give our model an excellent advantage for the effective processing of more scalable systems with a higher number of frames per second. The proposed system created panoramas with an average of 99% similarities with the traditional mosaicking systems while being highly faster than these conventional methods. Compression ratios and data rate savings, reflecting the gain in data stored and transferred, were calculated, reporting an average of 2.66 and 0.62 per frame, respectively, when applied to standard datasets. The results illustrated that the proposed system gives a dramatic reduction in the volume of data stored/transferred and showed that the creating of mosaics and the reconstruction was made in proper processing time. Experimental outcomes also showed that, for the suggested methods, the produced frames after reconstruction have a high similarity percentage compared with original ones before stitching, which indicates that the proposed approach is efficient enough to preserve the essential features of cameras’ frames without significant information loss.

Digital breast tomosynthesis (DBT) is a highly promising 3D imaging modality for breast diagnosis. Tissue overlapping is a challenge with traditional 2D mammograms; however, since digital breast tomosynthesis can obtain three-dimensional images, tissue overlapping is reduced, making it easier for radiologists to detect abnormalities and resulting in improved and more accurate diagnosis. In this study, a new computer-aided multi-class diagnosis system is proposed that integrates DBT augmentation and colour feature map with a modified deep learning architecture (Mod_AlexNet). To the proposed modified deep learning architecture (Mod AlexNet), an optimization layer with multiple high performing optimizers is incorporated so that it can be evaluated and optimised using various optimization techniques. Two experimental scenarios are applied, the first scenario proposed a computer-aided diagnosis (CAD) model that integrated DBT augmentation, image enhancement techniques and colour feature mapping with six deep learning models for feature extraction, including ResNet-18, AlexNet, GoogleNet, MobileNetV2, VGG-16 and DenseNet-201, to efficiently classify DBT slices. The second scenario compared the performance of the newly proposed Mod_AlexNet architecture and traditional AlexNet, using several optimization techniques and different evaluation performance metrics were computed. The optimization techniques included adaptive moment estimation (Adam), root mean squared propagation (RMSProp), and stochastic gradient descent with momentum (SGDM), for different batch sizes, including 32, 64 and 512. Experiments have been conducted on a large benchmark dataset of breast tomography scans. The performance of the first scenario was compared in terms of accuracy, precision, sensitivity, specificity, runtime, and f1-score. While in the second scenario, performance was compared in terms of training accuracy, training loss, and test accuracy. In the first scenario, results demonstrated that AlexNet reported improvement rates of 1.69%, 5.13%, 6.13%, 4.79% and 1.6%, compared to ResNet-18, MobileNetV2, GoogleNet, DenseNet-201 and VGG16, respectively. Experimental analysis with different optimization techniques and batch sizes demonstrated that the proposed Mod_AlexNet architecture outperformed AlexNet in terms of test accuracy with improvement rates of 3.23%, 1.79% and 1.34% when compared using SGDM, Adam, and RMSProp optimizers, respectively.

Recently, many researchers have deployed different deep learning techniques to predict epileptic seizure, using electroencephalogram signals. However, most of this research requires very large amounts of memory and complicated feature extraction algorithms. In addition, they could not precisely examine EEG signal characteristics, which led to poor prediction performance. In this research, a non-patient-specific epileptic seizure prediction approach is proposed. The proposed model integrates Wavelet-based EEG signal processing with deep learning architectures for efficient prediction of pre-ictal and inter-ictal signals. The proposed system uses different models of one-dimensional convolutional neural networks to discriminate between inter-ictal signal and pre-ictal signals in order to enhance prediction performance. Experiments have been carried out on a benchmark dataset to validate the robustness of the proposed model. The experimental results showed that the proposed approach achieved 93.4% for 16 patients and 97.87% for 6 patients. Experiments showed that the proposed model can predict epileptic seizures effectively, which can have remarkable potential in clinical applications.

Through a two-step procedure, 3-amino-7-chloro-2-methylquinazolin-4(3H)-one was synthesized from 2-amino-4-chlorobenzoic acid as a starting material. The latter reacted with chloro acetylchloride, then nucleophilically substituted with various secondary amines to produce acetamide derivatives (5a-e), or underwent condensation reaction with various aldehydes to produce arylidine derivatives (6a-e). In-silico study of drug-likeness and ADME descriptors was conducted for all compounds. Compounds showed good oral bioavailability, as well as good gastrointestinal absorption potential and no symptoms of liver or CNS adverse effects. In-vitro cytotoxic activity of the compounds was moderate to good when compared to staurosporine in three cell lines: HCT, MCF-7, and HepG-2. Compound 5c showed the highest cytotoxic activity against the HCT cell line (IC50 = 8.00 ± 0.33 μM), Compound 5d showed the highest cytotoxic activity against the HepG-2 cell line (IC50 = 17.78 ± 0.58 μM). Acetamide derivatives revealed higher cytotoxic activity compared to arylidine derivatives. Compound 5d had the highest enzyme inhibition activity in the in-vitro PI3k-δ enzyme inhibition assay (IC50 = 1.24 ± 0.03 μM) followed by 5c (IC50 = 8.27 ± 0.19 μM). Both 5c and 5d were able to bind at the ATP binding site of the PI3k-δ enzyme in a mode similar to the native ligand where they formed H-bond interactions with the hinge region amino acid Val828 and hydrophobic interactions with other amino acids indicating an agreement between molecular docking simulation study and the biological screening.

Substituted aldehydes, ethyl 2-(2-amino-thiazol-4-yl)acetate), and 2-mercaptoacetic acid, in a three-component one-pot green synthetic approach afforded 2-arylthiazolidin-4-one- thiazole hybrids(T1-T13). Compounds showed good anti-tubercular activity towards sensitive M. tuberculosis strain. Compound T8 was as potent as isoniazide (INH) with MIC = 0.12 μg/ml. Compounds T2 and T13 showed potent activity with MIC = 0.48 μg/ml. Other compounds showed moderate to good anti-tubercular activity towards MDR M. tuberculosis strain with MIC range 1.95-125 μg/ml. Compounds T2, T8, T9, and T13 showed anti-tubercular activity towards XDR M. tuberculosis strain with MIC range 7.81-125 μg/ml. Compounds T2 and T8 were capable of inhibiting M. tuberculosis InhA enzyme in-vitro with IC

Tremendous systems are rapidly evolving based on the trendy Internet of Things (IoT) in various domains. Different technologies are used for communication between the massive connected devices through all layers of the IoT system, causing many security and performance issues. Regression and integration testing are considered repeatedly, in which the vast costs and efforts associated with the frequent execution of these inflated test suites hinder the adequate testing of such systems. This necessitates the focus on exploring innovative scalable testing approaches for large test suites in IoT-based systems. In this paper, a scalable framework for continuous integration and regression testing in IoT-based systems (IoT-CIRTF) is proposed, based on IoT-related criteria for test case prioritization and selection. The framework utilizes search-based techniques to provide an optimized prioritized set of test cases to select from. The selection is based on a trained prediction model for IoT standard components using supervised deep learning algorithms to continuously ensure the overall reliability of IoT-based systems. The experiments are held on two GSM datasets. The experimental results achieved prioritization accuracy up to 90% and 92% for regression testing and integration testing respectively. This provides an enhanced and efficient framework for continuous testing of IoT-based systems, as per IoT-related criteria for the prioritization and selection purposes.

During the Coronavirus pandemic, e-learning systems have proven to be an essential pillar for education. This raises to surface what many studies have addressed earlier; creating a platform that completes the traditional classroom work and maximizes the effectiveness of learning outcomes. Striving to achieve such platform, studies have considered gamifying and personalizing the educational resources for the adaptation of educational systems as per the intended learners through intensive learning analytics. But was the learner really a part of the adaptation process taking place? Learning analytics are usually designed to the course’s adaptation and solely for the teachers. Thus, learning analytics in gamified adaptive educational systems involving the course, teachers and learners together are still under investigation. In this study, the Personalized Adaptive Gamified E-learning (PAGE) model is introduced to extend MOOCs by providing new satisfactory levels of learning analytics and visualization in the rich e-learning process that supports the learner’s intervention in the resultant learning analytics. The proposed Learning analytics have been developed to make the necessary adaptation to the course and learner’s learning flow, as well as visualizing the process and adaptation decisions to the learners. Results show a positive potential towards learning adaptation and visualization, and a necessity to provide an additional focus for the gamification concept.

Background: Vitiligo is an acquired, multifactorial disorder of the skin and mucous membranes. An elevated homocysteine level has been described in vitiligo. Methylenetetrahydrofolate reductase (MTHFR) and cystathionine B synthase (CBS) are major determinants of the homocysteine metabolism. Objectives: Determine serum homocysteine levels in vitiligo patients as well as the association between MTHFR (C677T, A1298C) and CBSgene polymorphisms and susceptibility to vitiligo in a sample of those populations. Methods: Homocysteine levels were estimated by radioimmunoassay while MTHFR (C677T, A1298C) and CBSgene polymorphisms were detected by the polymerase chain reaction-restriction fragment length polymorphism technique in 100 vitiligo patients and 80 healthy controls. Results: The homocysteine level was significantly higher in vitiligo patients than controls (p = 0.000). Significant differences in the genotype and allele distributions of single nucleotide polymorphisms of the MTHFR (C677T, A1298C) with the mutant genotypes are more common in the controls than patients (p = 0.001, 0.029, respectively). CBS gene mutant genotypes and alleles are more common in vitiligo patients than controls (p = 0.002). Conclusion: CBSand MTHFRgene polymorphisms may play a major role in the genetic susceptibility to vitiligo.

Enormous heterogeneous sensory data are generated in the Internet of Things (IoT) for various applications. These big data are characterized by additional features related to IoT, including trustworthiness, timing and spatial features. This reveals more perspectives to consider while processing, posing vast challenges to traditional data fusion methods at different fusion levels for collection and analysis. In this paper, an IoT-based spatiotemporal data fusion (STDF) approach for low-level data in–data out fusion is proposed for real-time spatial IoT source aggregation. It grants optimum performance through leveraging traditional data fusion methods based on big data analytics while exclusively maintaining the data expiry, trustworthiness and spatial and temporal IoT data perspectives, in addition to the volume and velocity. It applies cluster sampling for data reduction upon data acquisition from all IoT sources. For each source, it utilizes a combination of k-means clustering for spatial analysis and Tiny AGgregation (TAG) for temporal aggregation to maintain spatiotemporal data fusion at the processing server. STDF is validated via a public IoT data stream simulator. The experiments examine diverse IoT processing challenges in different datasets, reducing the data size by 95% and decreasing the processing time by 80%, with an accuracy level up to 90% for the largest used dataset.

the dangers generated from synthesized multimedia are increasing every day. The creation of the so-called Deepfakes multimedia is vastly evolving, making the detection task harder every day. Researchers and corporations are interested in exploring the technology limits and are coming up with new tools every year to create more robust fake media. In this paper, a new enhanced fake video detection model is introduced addressing many of the face-swapping threats and the low generalization problem. A preprocessing stage is proposed to minimize the noise in the data to enhance their quality. The proposed architecture uses a modified application of capsule neural networks (CapsNet) with an enhanced routing technique. It does not require a lot of training data and generates a small number of training parameters making it fast to build. The model was trained and tested using the DFDC-P dataset and the results have proven that it outperformed other detectors in terms of detection recall, weighted precision, and F1 score.

Background: The purpose of this study was to detect whether antibiotic therapy during IVF-ET cycle is as effective as aspiration of hydrosalpinx fluid under ultrasound guidance in preventing the adverse effect of hydrosalpinx on IVF-ET results.Methods: During the period between November 2012 and January 2020, patients with ultrasound visible hydrosalpinges undergoing IVF-ET who refused to undergo laparoscopic salpingectomy or not eligible for laparoscopy were advised to receive antibiotic therapy during IVF-ET cycle or to undergo aspiration of hydrosalpinx fluid under ultrasound guidance at the time of ovum pick up. A retrospective analysis was done for the results of IVF-ET cycles of patients who received antibiotic therapy ( n = 52 ) or underwent aspiration of hydrosalpinx fluid under ultrasound guidance ( n = 76). In the aspiration group, the tip of the suction needle was introduced into the hydrosalpinx to aspirate the hydrosalpinx fluid after completing the ovum pick up. In the antibiotic group, doxycycline (100 mg/12 h) was commenced one week before ovum pick up and was continued for another week after ovum pick up.Results: The implantation, clinical pregnancy, ongoing pregnancy and live birth rates were significantly higher in the aspiration group ( 16.67%% Vs. 8.33%, P value =0.029, 32.89% Vs. 15.38% P value = 0.026, 30.26% Vs. 13.46%,P value =0.028 and 28,95% Vs. 13.46% P value = 0.04 respectively). Conclusion: The aspiration of hydrosalpinx fluid under ultrasound guidance is more effective than antibiotic therapy in preventing the negative impact of hydrosalpinx on the results of IVF-ET. The results of the current study should be proved by subsequent randomized controlled studies.

Shereef Mohammed Abdelwahab, Abdelmonem M Hamed, Ahmed Sherin M Bayoumy, Maha Attaia Elfayoumi Ophthalmology Department, Benha College of Medicine, Benha University, Benha, EgyptCorrespondence: Abdelmonem M Hamed Tel +20 1221640288Email abdelmonem.abdelmonem@fmed.bu.edu.egPurpose: To assess the stability, safety, predictability, and efficacy of topography-guided myopic Femto-LASIK with two different treatment protocols.Setting: Ebsar Eye center, Benha, Qalyopia, Egypt.Design: Single-center, retrospective, COHORT control study.Methods: A total of 330 eyes enrolled in the study in group A and 322 eyes enrolled in group B underwent uncomplicated primary bilateral topography-guided Femto-LASIK. Group A was treated with the subjective clinical refraction; however, group B was treated with the modified refraction according to ALCON protocol.Results: The mean preoperative refractive spherical equivalent (MRSE) was − 4.85± 1.90D and − 5.0± 1.93D in group A and B, respectively (P = 0.86), and a cylinder of − 0.95± 0.80 D and − 0.92± 0.81D, respectively. At the 12 months’ postoperatively, the residual manifest SE within ± 0.5D was achieved by 82.86% of eyes in group A compared to 83.93% in group B. Of eyes, 92.06% had ≤ 0.5 astigmatism dioptre, while 100% of eyes had ≤ 1.0 astigmatism dioptre in group A (315 eyes); however, 91.80% of eyes had ≤ 0.5 astigmatism dioptre, while 100% of eyes had ≤ 1.0 astigmatism dioptre in group B.Conclusion: Topographic modification of the magnitude and axis of astigmatism treated using ALCON protocol when different from the clinical refraction may offer good refractive outcomes when we apply the Alcon precalculation considerations.Keywords: Femto-LASIK, topo-guided LASIK, kerato-refractive, LASER vision correction, modified refraction, measured astigmatism, Contoura calculator, Contoura LASIK

Failure of human body organs is believed to be one of the most important health problems all over the world. The application of stem cells in human regenerative medicine could be an alternative to organ transplantation, avoiding the problem of donor shortage and rejection. As few of the current treatment methods result in successful repair of damaged liver, the development of new procedures are warranted. The most recent type of liver regeneration approach is cell based therapy. The application of stem cell technology is gaining interest to be used for hepatic tissue engineering since stem cells have high proliferative capacity and multi-lineage differentiation potential. Stem cell-based therapy has received attention as a possible alternative to organ transplantation, owing to the ability of stem cells to repopulate and differentiate at the engrafted site. Advancements in the fields of stem cell biology and biomaterials science and engineering had been combined to produce strategies by which stem cell attachment; proliferation and differentiation in vitro were supported and enhanced. Before transplantation, cells were generally seeded on innovative modality of Nano scaffolds fabricated by electrospinning technique that recapitulate the extracellular matrix and provide cells with information that is important for tissue development. In this paper, we offer our view on the applying nanotechnology and present current and emergent approach in the field of hepatic tissue engineering for specific application. The application of nanotechnology to stem cell biology would be able to address the challenges of the failure of injected cells to engraft to target tissues.

Securing electronic medical information has a significant impact on data access which directly affects patient’s privacy and quality care rights. Medical professionals need to have full access to all patients' medical history to make accurate decisions regarding diagnosis and treatment plans. This paper introduces a novel Secure framework that integrates a new hybrid encryption scheme of medical images using chaotic maps and 2D Discrete wavelet transform (DWT) Steganography to increase key size and achieve high security level. In addition, a web-based monitoring platform has been deployed for tracking of electronic medical records during transmission. To validate the efficiency of the proposed framework, an application case-study has been introduced for securely transmitting retinal fundus medical images for diagnostic decisions on diabetic patients. Compared to the state-of-the-art approaches, the proposed framework demonstrated the ability to mask the context of the confidential patient into a transmitted cover image with high imperceptibility and limited degradation in the stego image provided in acceptable cryptographic overhead. Experimental results demonstrated that the proposed framework outperforms other schemes in terms of accuracy, sensitivity and perceptibility.

Optical Coherence Tomography (OCT) imaging has major advantages in effectively identifying the presence of various ocular pathologies and detecting a wide range of macular diseases. OCT examinations can aid in the detection of many retina disorders in early stages that could not be detected in traditional retina images. In this paper, a new hybrid computer-aided OCT diagnostic system (HyCAD) is proposed for classification of Diabetic Macular Edema (DME), Choroidal Neovascularization (CNV) and drusen disorders, while separating them from Normal OCT images. The proposed HyCAD hybrid learning system integrates the segmentation of Region of Interest (RoI), based on central serious chorioretinopathy (CSC) in Spectral Domain Optical Coherence Tomography (SD-OCT) images, with deep learning architectures for effective diagnosis of retinal disorders. The proposed system assimilates a range of techniques including RoI localization and feature extraction, followed by classification and diagnosis. An efficient feature fusion phase has been introduced for combining the OCT image features, extracted by Deep Convolutional Neural Network (CNN), with the features extracted from the RoI segmentation phase. This fused feature set is used to predict multiclass OCT retina disorders. The proposed segmentation phase of retinal RoI regions adds substantial contribution as it draws attention to the most significant areas that are candidate for diagnosis. A new modified deep learning architecture (Norm-VGG16) is introduced integrating a kernel regularizer. Norm-VGG16 is trained from scratch on a large benchmark dataset and used in RoI localization and segmentation. Various experiments have been carried out to illustrate the performance of the proposed system. Large Dataset of Labeled Optical Coherence Tomography (OCT) v3 benchmark is used to validate the efficiency of the model compared with others in literature. The experimental results show that the proposed model achieves relatively high-performance in terms of accuracy, sensitivity and specificity. An average accuracy, sensitivity and specificity of 98.8%, 99.4% and 98.2% is achieved, respectively. The remarkable performance achieved reflects that the fusion phase can effectively improve the identification ratio of the urgent patients&rsquo, diagnostic images and clinical data. In addition, an outstanding performance is achieved compared to others in literature.

Biodiversity forms the foundation of the vast array of ecosystem that critically contribute to human well being. Biodiversity is important in human managed as well as natural ecosystems. The changing status of insects and other invertebrates is the key indicator of biodiversity and environment that shapes it. The order Odonata comes under the class insecta. Odonata are divided into two suborders; Zygoptera (damselflies) and Anisoptera (dragonflies). Natural calamities can alter the ecosystem and thereby biodiversity drastically. This study was conducted in Tirurangadi taluk of kerala, India which is a flood prone taluk due to the over flow of the Kadalundi river during monsoon season. We reported a total of 12 species belonging to 10 genera and 2 families during the entire study. Sub-order Anisoptera was represented by the families, Libellulidae and Aeshnidae, comprising 11 species of Libellulidae and one species of Aeshnidae. On a long term, we aim at studying the impacts on insect biodiversity caused by floods.

Data streams have become a widely-adopted data representation format in many real-world applications. This data streaming may be published for different scientific research, mining, or analysis purposes. However, such streams may contain personal-specific data that could be considered as sensitive about individuals. This makes the privacy preserving of data streams against privacy disclosure attacks, while maintaining their utilization, is a real challenge. Some studies have considered privacy-preserving publishing over data streams with only Single Sensitive Attribute, in which they do not protect the published streams from all possible privacy attacks. In this paper, we propose a novel Restricted Sensitive Attributes-based Sequential Anonymization (RSA-SA) approach for privacy-preserving data stream publishing. Besides, two new privacy restrictions are introduced to restrict the published Sensitive Attributes values: Semantic-diversity and Sensitivity-diversity. RSA-SA can protect the sensitive values of the published data streams against the related privacy attacks, including the attribute disclosure, skewness, similarity, and sensitivity attacks. In addition, RSA-SA handles data streams that have either single or multiple sensitive attributes with minimum information loss and delay time. Thus, the data utility of the published data streams is efficiently maintained to provide more accurate mining and analytical results, where robust invulnerability to privacy attacks is sustained.