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Abstract New computer-aided design and computer-aided manufacturing (CAD-CAM) materials have been reported to have suitable mechanical and physical properties for endocrown restorations. However, there is a lack of literature on evaluating the retention of endocrown using these materials. This in vitro study aimed to compare the retention of endocrowns fabricated from 3 different CAD-CAM materials. Sixty human mandibular third molars were divided into 3 groups (n = 20) according to the material used: (e.max CAD, Ivoclar AG, Schaan, Liechtenstein), (HC, Shofu, Koyoto, Japan), and (Brilliant Crios, Coltene, Altstätten, Switzerland) (n = 20). Specimens were stored in distilled water at 37 °C for 1 week and subjected to 5000 thermal cycles. A universal testing machine (5500R/1123, Instron, Norwood, USA) was used to measure the tensile force. All results were statistically analyzed using one-way ANOVA (α = 0.05), and different failure modes were demonstrated. The results revealed that e. max CAD showed the highest mean tensile force value (402.35 ± 15.812) N, and the lowest mean tensile force value was for Brilliant Coris (118.90 ± 12.430) N (P

Abstract Background Occlusal veneer had been evaluated for mechanical properties using lithium disillicate. However, studies evaluating the mechanical properties of occlusal veneer with different preparation designs and ceramic materials are lacking. So, this in vitro study aimed to evaluate the fracture resistance of occlusal veneers with two designs fabricated from two different ceramic materials. Material and methods Fourty mandibular third molars were distributed to 2 groups (n = 20) according to preparation design: group (O) anatomical occlusal reduction and group (OA) anatomical occlusal and 1 mm axial reduction. Each group was additionally subdivided into two subgroups (n = 10) according to ceramic materials; in subgroup X, lithium disilicate (e.max CAD, Ivoclar AG, Schaan, Liechtenstein) was used, and in subgroup S, zirconia-reinforced lithium silicate (ZLS) (Vita Suprinity, VitaZahnfabrik, Bad Säckingen, Germany) was used. All specimens were cemented with a light-cure resin cement (Choice 2, Bisco, Schaumburg, USA). 5000 thermocycles were applied to all specimens with both temperatures of 5 °C and 55 °C in two water baths; the dwell time was 30s at each bath, and the transfer time was 10s. Then all specimens were subjected to a fatigue simulation under dynamic loading of 200 N for 250,000 cycles. A universal testing machine (5500R/1123, Instron, Norwood, USA) was used to evaluate the fracture strength with a crosshead speed of 1 mm/min. All data were analyzed statistically by using a two-way ANOVA, and for some violations of assumptions, these results were compared with those obtained by the nonparametric test (Scheirer Ray Hare) (α = 0.05). Results A statistically significantly higher fracture resistance in the ‘OA’ (3389 N) compared to the ‘O’ (2787 N) group regardless of the ceramic material (P

Abstract Objectives To report our experience with three cases of anterior lumbosacral meningocele with our multidisciplinary team plan and perioperative management and surgical approach for each case. Background Anterior meningocele is a rare congenital form of spinal dysraphism that involves protrusion of the theca anteriorly into the retroperitoneal and presacral space through an anterior defect. Method Three cases with anterior meningocele at different spinal levels (lumbar and sacral) were operated at Neurosurgery Department at Assiut University hospital from June to November 2020 with multidisciplinary management plans that were tailored separately for each case. Conclusion Anterior meningocele is a rare form of spinal dysraphism and a challenging congenital anomaly that requires proper diagnosis and selection of surgical approach. Our case series outlines the importance of multidisciplinary team approach for tailoring perioperative management and surgical approach with intraoperative important steps.

Three frequent factors such as low classification accuracy, computational complexity, and resource consumption have an impact on malware evaluation methods. These challenges are exacerbated by elements such as unbalanced data environments and specific feature generation. To address these challenges, we aim to identify optimal texture features and classifiers for effective malware detection. The article outlines a method that consists of four stages: malware conversion to grayscale, feature extraction using (segmentation-based fractal texture analysis (SFTA), Local Binary Pattern (LBP), Haralick, Gabor, and Tamura), classification using (Gaussian Discriminant Analysis (GDA), k-Nearest Neighbor (KNN), Logistic, Support Vector Machines (SVM), Random Forest (RF), Extreme Learning Machine (Ensemble)), and finally the evaluation. Using the Malimg imbalanced and MaleVis balanced datasets, we assess classifier performance and feature effectiveness. Comparative analysis indicates that KNN outperforms other classifiers in terms of Accuracy, Error, F1, and Precision, while SVM and RF as runners-up. Gabor performs better in MaleVis, whereas the SFTA feature performs better under the Malimg dataset. The proposed SFTA-KNN and Gabor-KNN methods achieve 96.29% and 98.02% accuracy, respectively, surpassing current state-of-the-art approaches. Additionally, higher computing performance is achieved by using fewer dimensions when employing our feature extraction method.

Abstract Background Many monolithic machined materials have been introduced and provided a suitable mechanical and physical properties for inlay restorations. However, there is shortage in the studies evaluating the marginal adaptation using these materials. Purpose This study aimed to compare the effect of fabricating inlay restorations from 3 different CAD-CAM materials on marginal gaps before and after thermocycling. Materials and methods Sixty human premolars were randomly divided into 3 groups (n = 20) according to the material used: (e.max CAD, Ivoclar AG, Schaan, Liechtenstein), (HC, Shofu, Koyoto, Japan) and (Brilliant Crios, Coltene, Altstätten, Switzerland) (n = 20). A scanning electron microscope (SEM) (JSM- 6510 lv, JEOL, Tokyo, JAPAN) was used to for measuring the marginal gaps after cementation of inlay restorations. The magnification was adapted to 250x. Marginal gaps were revaluated with SEM after thermocycling. The temperatures of baths were 5 and 55 °C was applied for a total of 5000 cycles. All data were statistically analyzed by using ANCOVA to demonstrate if there were any statistically significant differences between the gap measures after thermocycling of the three independent (unrelated) groups. A Bonferroni adjustmen was used to perform post hoc analysis (α = 0.05). Results Post-intervention marginal gap was statistically significantly lower in group EX (110.8 μm) which was statistically significant compared with group SF (112.5 μm) (mean difference=-1.768, P = .007) and group BR (113 μm) (mean difference=-2.272, P = .001), however, in. comparing SF and BR groups, there was no significant difference (mean difference=-0.5, P = .770). Conclusions Thermocycling affected the marginal gaps of composite based restoration and resin-modified ceramics widely. However, it had a very small effect on glass ceramics marginal adaptation. Clinical implications The marginal gaps of CAD-CAM inlays varied according to material used (ceramic based, combination, or resin based). Thermocycling has a minor effect on the marginal adaptation of lithium disilicate glass-ceramic inlays, where it affected the margin of resin-modified ceramic and composite based inlays greatly. Using lithium disilicate glass-ceramic might improve the clinical longevity of inlay restored teeth.

The majority of applications use automatic image recognition technologies to carry out a range of tasks. Therefore, it is crucial to identify and classify image distortions to improve image quality. Despite efforts in this area, there are still many challenges in accurately and reliably classifying distorted images. In this paper, we offer a comprehensive analysis of models of both non-lightweight and lightweight deep convolutional neural networks (CNNs) for the classification of distorted images. Subsequently, an effective method is proposed to enhance the overall performance of distortion image classification. This method involves selecting features from the pretrained models’ capabilities and using a strong classifier. The experiments utilized the kadid10k dataset to assess the effectiveness of the results. The K-nearest neighbor (KNN) classifier showed better performance than the naïve classifier in terms of accuracy, precision, error rate, recall and F1 score. Additionally, SqueezeNet outperformed other deep CNN models, both lightweight and non-lightweight, across every evaluation metric. The experimental results demonstrate that combining SqueezeNet with KNN can effectively and accurately classify distorted images into the correct categories. The proposed SqueezeNet-KNN method achieved an accuracy rate of 89%. As detailed in the results section, the proposed method outperforms state-of-the-art methods in accuracy, precision, error, recall, and F1 score measures.

A significant quantity of malware is created on purpose every day. Users of smartphones and computer networks now mostly worry about malware. These days, malware detection is a major concern in the cybersecurity area. Several factors can impact malware detection performance, such as inappropriate features and classifiers, extensive domain knowledge, imbalanced data environments, computational complexity, and resource usage. A significant number of existing malware detection methods have been impacted by these factors. Therefore, in this paper, we will first identify and determine the best features and classifiers and then use them in order to propose the malware detection method. The comparative strategy and proposed malware detection procedure consist of four basic steps: malware transformation (converting images of malware from RGB to grayscale), feature extraction (using the ResNet-50, DenseNet-201, GoogLeNet, AlexNet, and SqueezeNet models), feature selection (using PCA method), classification (including GDA, KNN, logistic, SVM, RF, and ensemble learning), and evaluation (using accuracy and error evaluation metrics). Unbalanced Malimg datasets are used in experiments to validate the efficacy of the results that were obtained. According to the comparison findings, KNN is the best machine learning classifier. It outperformed the other classifiers in the Malimg datasets in terms of both accuracy and error. In addition, DenseNet201 is the best pretrained model in the Malimg dataset. Therefore, the proposed DenseNet201-KNN methods had an accuracy rate of 96% and a minimal error rate of 3.07%. The proposed methods surpass existing state-of-the-art approaches. The proposed feature extraction is computationally quicker than most other methods since it uses a lightweight design and fewer feature vector dimensions.

ان منطقة الدراسة تمتد من خط طول 44°59'22.68"Eشرقا الى خط طول 45°16'15.52"E شرقا ومن دائرة عرض34°11'21.3"N شمالا الى دائرة عرض 34°28'49.79"N شمالا وتتبلغ مساحتها 467كم2 في الجزء الشمالي من محافظة ديالى. من الناحية الجيولوجية تغطي منطقة الدراسة رسوبيات العصر الثلاثي والعصر الرباعي وشملت تكوينات العصر الثلاثي على تكوين باي حسن الذي نلاحظه في الأجزاء الشمالية من المدينة وبعض المواقع الغربية والوسطى وتكوين المقدادية والذي يشغل نسبة 44.52%. والذي يتركز في الأجزاء الشرقية والوسطى من منطقة الدراسة ويشمل العصر الرباعي على السهول الفيضي وترسبات متعددة الأصل والتي تشغل 55.48%. وتعد من اهم الخزانات الجوفية في منطقة الدراسة. ان طبيعة المناخ التي لعبت دور كبير في الموازنة المناخية حيث ان المناخ شبه الجاف الذي أدى الى عجز مائي في جميع اشهر السنة في محطتين خانقين وطوزخرماتو. واظهرت الدراسة ان التربة في منطقة الدراسة متكونة بصورة رئيسية من نوعين من الترب الصحراوية الكلسية والتي مساحتها تصل الى 94.55كم2وتصل نسبتها الى21.51%. والنوع الثاني التربة الكلسية الجافة وهي تربة رديئة النسجة ومساحتها 344.95كم2وتصل نسبتها الى 78.49% من المساحة الكلية للمنطقة. اما النبات الطبيعي يكون انتشاره بالقرب من المساحات المزروعة وبالقرب من مشاريع الري والبزل مما اتضح لنا وجود أنواع متعددة من اشكال الغطاء النباتي في منطقة الدراسة.

The majority of applications use automatic image recognition technologies to carry out a range of tasks. Therefore, it is crucial to identify and classify image distortions to improve image quality. Despite efforts in this area, there are still many challenges in accurately and reliably classifying distorted images. In this paper, we offer a comprehensive analysis of models of both non-lightweight and lightweight deep convolutional neural networks (CNNs) for the classification of distorted images. Subsequently, an effective method is proposed to enhance the overall performance of distortion image classification. This method involves selecting features from the pretrained models' capabilities and using a strong classifier. The experiments utilized the kadid10k dataset to assess the effectiveness of the results. The K-nearest neighbor (KNN) classifier showed better performance than the naïve classifier in terms of accuracy, precision, error rate, recall and F1 score. Additionally, SqueezeNet outperformed other deep CNN models, both lightweight and non-lightweight, across every evaluation metric. The experimental results demonstrate that combining SqueezeNet with KNN can effectively and accurately classify distorted images into the correct categories. The proposed SqueezeNet-KNN method achieved an accuracy rate of 89%. As detailed in the results section, the proposed method outperforms state-of-the-art methods in accuracy, precision, error, recall, and F1 score measures. [ABSTRACT FROM AUTHOR]

The security of a computer system can be harmed by specific applications, such as malware. Malware comprises unwanted, dangerous enemies that aim to compromise the security and generate significant loss. Consequently, Malware Detection (MD) and Malware Classification (MC) has emerged as a key issue for the cybersecurity society. MD only involves locating malware without determining what kind of malware it is, but MC comprises assigning a class of malware to a particular sample. Recently, a few techniques for analyzing malware quickly have been put out. However, there remain numerous difficulties, such as the low classification accuracy of samples from related malware families, the computational complexity, and consumption of resources. These difficulties make detecting and classifying malware very challenging. Therefore, in this paper, we proposed an efficient malware detection and classification technique that combines Segmentation-based Fractal Texture Analysis (SFTA) and Gaussian Discriminant Analysis (GDA). The outcomes of the experiment demonstrate that the SFTA-GDA produces a high classification rate. There are three main steps involved in our malware analysis, namely: (i) malware conversion; (ii) feature extraction; and (iii) classification. We initially convert the RGB malware images into grayscale malware images for effective malware analysis. The SFTA and Gabor features are then extracted from gray-scale images in the feature extraction step. Finally, the classification is carried out by GDA and Naive Bayes (NB). The proposed method is evaluated on a common MaleVis dataset. The proposed SFTA-GDA is the effective choice since it produces the highest accuracy rate across all families of the MaleVis Database. Experimental findings indicate that the accuracy rate was 98%, which is higher than the overall accuracy from the existing state-of-the-art methods.

The new thiopyrano[2,3-b]pyridines 4–9 could be synthesized from the nicotinonitrile derivative 1. The cytotoxicity activity of the selected compounds 5, 6 and 8 was tested against MCF-7 and HCT-116 cell lines. The compound 5 (TP5) exhibited significant inhibitory activity and displayed the most potent activity, more than 6 and 8. The compound 5 with potent inhibitory activity in tumor growth inhibition would be a potential anticancer agent. In the light of this result, the labeled 125I-compound 5 (125I-TP5) was prepared and its cytotoxicity against ascites tumor in mice has been evaluated. The results show that compound 5 (TP5) may be potentially used as a radiopharmaceutical for tumor diagnosis when labeled with 125I.

Malware development has significantly increased recently, posing a serious security risk to both consumers and businesses. Malware developers continually find new ways to circumvent security research’s ongoing efforts to guard against malware attacks. Malware Classification (MC) entails labeling a class of malware to a specific sample, while malware detection merely entails finding malware without identifying which kind of malware it is. There are two main reasons why the most popular MC techniques have a low classification rate. First, Finding and developing accurate features requires highly specialized domain expertise. Second, a data imbalance that makes it challenging to classify and correctly identify malware. Furthermore, the proposed malware classification (MC) method consists of the following five steps: (i) Dataset preparation: 2D malware images are created from the malware binary files; (ii) Visualized Malware Pre-processing: the visual malware images need to be scaled to fit the CNN model’s input size; (iii) Feature extraction: both hand-engineering (Tamura) and deep learning (GoogLeNet) techniques are used to extract the features in this step; (iv) Classification: to perform malware classification, we employed k-Nearest Neighbor (KNN), Support Vector Machines (SVM), and Extreme Learning Machine (ELM). The proposed method is tested on a standard Malimg unbalanced dataset. The accuracy rate of the proposed method was extremely high, making it the most efficient option available. The proposed method’s accuracy rate was outperformed both the Hand-crafted feature and Deep Feature techniques, at 95.42 and 96.84 percent.

Steganography is the technique for secretly hiding messages in media such as text, audio, image, and video without being discovered. Image is one of the most essential media for concealing data, making it hard to identify hidden data not visible to the human eye. In general, the cover image and the encrypted image are symmetrical in terms of dimension size, resolution, and qualities. This makes the difference difficult to perceive with the human eye. As a result, distinguishing between the two symmetric images required the development of methods. Steganalysis is a technique for identifying hidden messages embedded in digital material without having to know the embedding algorithm or the “non-stego” image. Due to their enormous feature vector dimension, which requires more time to calculate, the performance of most existing image steganalysis classification (ISC) techniques is still restricted. Therefore, in this research, we present a steganalysis classification method based on one of the texture features chosen, such as segmentation-based fractal texture analysis (SFTA), local binary pattern (LBP), and gray-level co-occurrence matrix (GLCM). The classifiers employed include Gaussian discriminant analysis (GDA) and naïve Bayes (NB). We used a public database in our proposed method and applied it to IStego100K datasets to be able to assess its performance. The experimental results reveal that in all classifiers, the SFTA feature surpassed all of the texture features, making it a great texture feature for image steganalysis classification. In terms of feature dimension and classification accuracy (CA), a comparison was made between the suggested SFTA-based GDA approach and various current ISC methods. The outcomes of the comparison are obvious show that the proposed method surpasses current methods.

Image watermarking is one of many methods for preventing unauthorized alterations to digital images. The major goal of the research is to find and identify photos that include a watermark, regardless of the method used to add the watermark or the shape of the watermark. As a result, this study advocated using the best Gabor features and classifiers to improve the accuracy of image watermarking identification. As classifiers, discriminant analysis (DA) and random forests are used. The DA and random forest use mean squared energy feature, mean amplitude feature, and combined feature vector as inputs for classification. The performance of the classifiers is evaluated using a variety of feature sets, and the best results are achieved. In order to assess the performance of the proposed method, we use a public database. VOC2008 is a public database that we use. The findings reveal that our proposed method’s DA classifier with integrated features had the greatest TPR of 93.71 and the lowest FNR of 6.29. This shows that the performance outcomes of the proposed approach are consistent. The proposed method has the advantages of being able to find images with the watermark in any database and not requiring a specific type or algorithm for embedding the watermark.

Autophagy is a key metabolic process where cells can recycle its proteins and organelles to regenerate its own cellular building blocks. Chemotherapy is indispensable for cancer treatment but associated with various side-effects, including organ damage. Stem cell-based therapy is a promising approach for reducing chemotherapeutic side effects, however, one of its main culprits is the poor survival of transplanted stem cells in damaged tissues. Here, we aimed to test the effects of activating autophagy in adipose-derived mesenchymal stem/stromal cells (ADSCs) on the survival of ADSCs, and their therapeutic value in cisplatin-induced liver injury model. Autophagy was activated in ADSCs by rapamycin (50 nM/L) for two hours before transplantation and were compared to non-preconditioned ADSCs. Rapamycin preconditioning resulted in activated autophagy and improved survival of ADSCs achieved by increased autophagosomes, upregulated autophagy-specific LC3-II gene, decreased protein degradation/ubiquitination by downregulated p62 gene, downregulated mTOR gene, and finally, upregulated antiapoptotic BCL-2 gene. In addition, autophagic ADSCs transplantation in the cisplatin liver injury model, liver biochemical parameters (AST, ALT and albumin), lipid peroxidation (MDA), antioxidant profile (SOD and GPX) and histopathological picture were improved, approaching near-normal conditions. These promising autophagic ADSCs effects were achieved by modulation of components in TGF-β1/Smad and PI3K-AKT signaling pathways, besides reducing NF-κB gene expression (marker for inflammation), reducing TGF-β1 levels (marker for fibrosis) and increasing SDF-1 levels (liver regeneration marker) in liver. Therefore, current results highlight the importance of autophagy in augmenting the therapeutic potential of stem cell therapy in alleviating cisplatin-associated liver damage and opens the path for improved cell-based therapies, in general, and with chemotherapeutics, in particular.