Purpose To evaluate the effect of zirconia priming with MDP-Salt before MDP containing primers and self-adhesive cement on the shear bond strength. Materials and methods Fully sintered high translucent zirconia specimens (n = 120) were assigned into 2 groups (n = 60 each): Control (No Pretreatment) and Methacryloyloxydecyl dihydrogen phosphate salt (MDP-Salt) pretreated. Each group was divided into 3 subgroups (n = 20) according to cementation protocol: 1) MDP + Silane primer and conventional resin cement, 2) MDP+ Bisphenyl dimethacrylate (BPDM) primer and conventional resin cement, and 3) MDP containing self-adhesive resin cement. Shear bond strength (SBS) was measured after 10,000 thermocycling. Contact angle was measured for tested groups. Surface topography was assessed using a 3D confocal laser scanning microscope (CLSM). Weibull analysis was performed for SBS and one-way ANOVA for contact angle and surface topography measurements (α = 0.05). Results The use of MDP-Salt significantly improved the SBS (p < .05) for all tested subgroups. Self-adhesive cement showed an insignificant difference with MDP + Silane group for both groups (p > .05). MDP + BPDM showed a significantly lower characteristic strength compared to self-adhesive resin cement when both were pretreated with MDP-Salt. No difference between all tested groups in the surface topographic measurements while MDP-Salt showed the highest contact angle. Conclusion MDP-Salt pretreatment can improve bonding performance between zirconia and MDP containing products.
The ongoing evolution of digitization and digital transformation, especially since the emergence of Industry 4.0, has led to the generation of vast volumes of data, information, and outcomes. Artificial Intelligence (AI) has emerged as a promising avenue to analyze this massive influx of data and has the potential to impact every facet of our lives. This project introduces a Data-Driven Framework (DDF) designed to assess the potential of digitization and AI integration. The framework categorizes the assessment across various stages of the data lifecycle, applying the PESTEL analysis as an illustrative example to comprehend potential legal, ethical, and contextual risks associated with each stage. The data lifecycle, as perceived from the user’s standpoint, encompasses processes such as acquisition, annotation, collection, storage, processing, analysis, dissemination, and more. The study provides both quantitative and qualitative analyses, utilizing predictive models to identify requirements and gaps in achieving full digitization and AI application potential. While the Sustainable Plan 2030 serves as the project’s focal point, the framework's applicability extends to diverse plans beyond the Arab region. By adopting this comprehensive approach, the project sheds light on the intricate interplay between digitization, AI, and various environmental factors, ensuring informed decision-making and responsible implementation for sustainable growth.
A library of 24 congeners of the natural product sulfuretin were evaluated against nine panels representing nine cancer diseases. While sulfuretin elicited very weak activities at 10 µM concentration, congener 1t was identified as a potential compound triggering growth inhibition of diverse cell lines. Mechanistic studies in HCT116 colon cancer cells revealed that congener 1t dose-dependently increased levels of cleaved-caspases 8 and 9 and cleaved-PARP, while it concentration-dependently decreased levels of CDK4, CDK6, Cdc25A, and Cyclin D and E resulting in induction of cell cycle arrest and apoptosis in colon cancer HCT116 cells. Mechanistic study also presented MET receptor tyrosine kinase as the molecular target mediating the anticancer activity of compound 1t in HCT116 cells. In silico study predicted folded p-loop conformation as the form of MET receptor tyrosine kinase responsible for binding of compound 1t. Together, the current study presents compound 1t as an interesting anticancer lead for further development.
A series of rosmarinic acid-β-amino-α-ketoamide hybrids were synthesized and rationally repurposed towards the identification of new antileishmanial hit compounds. Two hybrids, 2g and 2h, showed promising activity (IC 50 values of 9.5 and 8.8 µM against Leishmania donovani promastigotes, respectively). Their activities were comparable to erufosine. In addition, cytotoxicity evaluation employing human THP-1 cells revealed that the two hybrids 2g and 2h possess no cytotoxic effects up to 100 µM, while erufosine possessed cytotoxicity with CC 50 value of 19.4 µM. In silico docking provided insights into structure-activity relationship emphasizing the importance of the aliphatic chain at the α-carbon of the cinnamoyl carbonyl group establishing favorable binding interactions with LdCALP and LARG in both hybrids 2g and 2h. In light of these findings, hybrids 2g and 2h are suggested as potential safe antileishmanial hit compounds for further development of anti-leishmanial agents.
CDK2 is a key player in cell cycle processes. It has a crucial role in the progression of various cancers. Hepatocellular carcinoma (HCC) and colorectal cancer (CRC) are two common cancers that affect humans worldwide. The available therapeutic options suffer from many drawbacks including high toxicity and decreased specificity. Therefore, there is a need for more effective and safer therapeutic agents. A series of new pyrazolo[3,4-d]pyrimidine analogs was designed, synthesized, and evaluated as anticancer agents against the CRC and HCC cells, HCT116, and HepG2, respectively. Pyrazolo[3,4-d]pyrimidinone derivatives bearing N5-2-(4-halophenyl) acetamide substituents were identified as the most potent amongst evaluated compounds. Further evaluation of CDK2 kinase inhibition of two potential cytotoxic compounds 4a and 4b confirmed their CDK2 inhibitory activity. Compound 4a was more potent than the reference roscovitine regarding the CDK2 inhibitory activity (IC 50 values: 0.21 and 0.25 µM, respectively). In silico molecular docking provided insights into the molecular interactions of compounds 4a and 4b with important amino acids within the ATP-binding site of CDK2 (Ile10, Leu83, and Leu134). Overall, compounds 4a and 4b were identified as interesting CDK2 inhibitors eliciting antiproliferative activity against the CRC and HCC cells, HCT116 and HepG2, respectively, for future further investigations and development.
Although histone deacetylase (HDAC) inhibitors show promise in treating various types of hematologic malignancies, they have some limitations, including poor pharmacokinetics and off‐target side effects. Prodrug design has shown promise as an approach to improve pharmacokinetic properties and to improve target tissue specificity. In this work, several bioreductive prodrugs for class I HDACs were designed based on known selective HDAC inhibitors. The zinc‐binding group of the HDAC inhibitors was masked with various nitroarylmethyl residues to make them substrates of nitroreductase (NTR). The developed prodrugs showed weak HDAC inhibitory activity compared to their parent inhibitors. The prodrugs were tested against wild‐type and NTR‐transfected THP1 cells. Cellular assays showed that both 2‐nitroimidazole‐based prodrugs 5 and 6 were best activated by the NTR and exhibited potent activity against NTR‐THP1 cells. Compound 6 showed the highest cellular activity (GI 50 = 77 nM) and exhibited moderate selectivity. Moreover, activation of prodrug 6 by NTR was confirmed by liquid chromatography‐mass spectrometry analysis, which showed the release of the parent inhibitor after incubation with Escherichia coli NTR. Thus, compound 6 can be considered a novel prodrug selective for class I HDACs, which could be used as a good starting point for increasing selectivity and for further optimization.
In this research, a new one-axis servomechanism investigation is presented, taking into account parameter fluctuation and system uncertainty. Additionally, a novel method for very effective TID control was created to accurately monitor a chosen profile. A comparative study between the suggested control method and the well-known controllers (PID and Nonlinear PID) is also included. The COVID-19 optimization technique was used to discover the best control parameters. Through the online simulation, the servomechanism system's settings were modified at random within a predetermined range. As nonlinearity resources (friction, backlash, environmental influences), these parameters fluctuate and contribute to system uncertainty. It had been completed and looked at to compare the linear and nonlinear models. The results demonstrate that the suggested controller is capable of tracking the number of operational points with high accuracy, a short rising time, and little overrun.
A new series of thiazolyl-pyrazoline derivatives (4a–d, 5a–d 6a, b, 7a–d, 8a, b, and 10a, b) have been designed and synthesized through the combination of thiazole and pyrazoline moieties, starting from the key building blocks pyrazoline carbothioamides (1a–b). These eighteen derivatives have been designed as anticipated EGFR/HER2 dual inhibitors. The efficacy of the developed compounds in inhibiting cell proliferation was assessed using the breast cancer MCF-7 cell line. Among the new synthesized thiazolyl-pyrazolines, compounds 6a, 6b, 10a, and 10b displayed potent anticancer activity toward MCF-7 with IC50 = 4.08, 5.64, 3.37, and 3.54 µM, respectively, when compared with lapatinib (IC50 = 5.88 µM). In addition, enzymatic assays were also run for the most cytotoxic compounds (6a and 6b) toward EGFR and HER2 to demonstrate their dual inhibitory activity. They revealed promising inhibition potency against EGFR with IC50 = 0.024, and 0.005 µM, respectively, whereas their IC50 = 0.047 and 0.022 µM toward HER2, respectively, compared with lapatinib (IC50 = 0.007 and 0.018 µM). Both compounds 6a and 10a induced apoptosis by arresting the cell cycle of the MCF-7 cell line at the G1 and G1/S phases, respectively. Molecular modeling studies for the promising candidates 6a and 10a showed that they formed the essential binding with the crucial amino acids for EGFR and HER2 inhibition, supporting the in vitro assay results. Furthermore, ADMET study predictions were carried out for the compounds in the study.
In fact, bonding of zirconia restorations is still a big challenge in clinical situations and many bonding protocols discussed in literature might be still controversial. The aim of this was to study assess the bond strength of zirconia after alumina and glass-bead pre-treatments with two different primers in combination with conventional resin cement and 10-methacryloyloxydecyl dihydrogen phosphate (MDP) containing self-adhesive resin cement without priming. Fully sintered high translucent zirconia samples (n = 160) were assigned into 2 groups of pre-treatments (n = 80): Alumina-sandblasting (AB) and Glass-bead (GB). Then, each group was divided into 4 sub-groups according to priming and cement used (n = 20 each): conventional self-adhesive resin cement, MDP-silane Primer, MDP primer both with conventional self-adhesive resin cement, and MDP contained cement. Shear bond strength (SBS) was measured after thermocycling. Failure mode was analyzed using stereomicroscope. Contact angle and surface topography were investigated using other fully sintered samples (n = 30) constructed for that sole purpose, divided into control (no pre-treatment [unmodified], alumina-, and glass-bead sandblasted groups). Two-way ANOVA was performed for SBS and failure mode was analyzed. The use of Alumina-sandblasting showed higher SBS compared to Glass-bead pre-treatment for MDP-silane primer (p = 0.034) and MDP primer (p < 0.001). While MDP contained cement showed higher but insignificant SBS when pre-treated with glass-beads. Alumina-sandblasting and glass-bead pre-treatments improve bond strength of zirconia combined using primers before cementation with conventional resin cement. Also, self-adhesive MDP contained cement along with surface pre-treatment showed the highest achievable bond strength. It was concluded that both alumina-sandblasting and glass-bead blasting improved SBS combined with MDP containing self-adhesive resin cement reducing the required clinical steps during cementation of zirconia restorations.
This paper presents an online educational platform that leverages facial expression recognition technology to monitor students’ progress within the classroom. Periodically, a camera captures images of students in the classroom, processes these images, and extracts facial data through detection methods. Subsequently, students’ learning statuses are assessed using expression recognition techniques. The developed approach then dynamically refines and enhances teaching strategies using the acquired learning status data. In the course of the experiment, we enhance facial expression recognition accuracy through the utilization of ResNet-50 for effective feature extraction. Additionally, by adjusting the residual down-sampling module, we bolster the correlation among input features, thus mitigating the loss of feature information. Simultaneously, a convolutional attention mechanism module is incorporated to reduce the influence of irrelevant areas within the feature map. The proposed method achieves an accuracy of 87.62% and 88.13 % on the RAF-DB and FER2013 expression datasets, respectively. In comparison with the original ResNet-50 network and the expression recognition outcomes found in existing literature, the suggested approach demonstrates enhanced accuracy and improved detection of students’ learning states and expression variations. Consequently, the application of facial expression recognition technology in online learning, along with the optimization of online teaching resources and strategies grounded in the results of recognition, holds tangible value for augmenting the quality of online learning experiences. We have benchmarked the proposed model against state-of-the-art techniques and conducted evaluations using the FER-2013, CK+, and KDEF datasets. The significance of these results lies in their potential application within educational institutions.
Sensor Networks (WSN) are mainly composed of intelligent interconnected devices (things) that require remote ubiquitous over-the-air re-programming. Conse- quently, existing functionalities are updated, and firmware errors and security flaws are corrected along with the addition of new functionality. These enable their designated applications to be dynamically re-purposed. In this paper, a reliable design of an IoT Wireless Microchip ATMEL AVR Programmer device that provides firmware OTA updates utilizing Wi-Fi and LoRa technologies is proposed. It supports both the official Program and Debug Interface (PDI) and Tiny Programming Interface (TPI) for Microchip ATMEL AVR programming and debug- ging. TPI and PDI protocols are supported by the Microchip ATMEL AVR ATtiny and ATxmega family of microcontrollers. The research’s fundamental contribution is to realize wireless communication for the standard official Microchip ATMEL AVR Programmer for remotely writing, reading, erasing, and verifying a microcontroller’s Flash and EEPROM memories without the need for a custom bootloader. Simulations are performed utilizing the AVRDUDESS and the NetBurner Virtual COMM Port Converter applications that proved robust firmware OTA up- dating procedures. Experimental work is conducted on the AVR ATtiny10/20 and ATxmega32A4U microcontrollers, which proved an outstanding performance of sending a 6.315KB firmware file within 25 and 35 seconds using Wi-Fi and LoRa respectively.
The current study investigates the anticancer effects of PEGylated chitosan nanoparticles (CS NPs) coloaded with betaine (BT) and nedaplatin (ND) on breast adenocarcinoma (MCF-7) cells and breast cancer-bearing rats. Hereof, the ionotropic gelation approach was implemented for the synthesis of PEG-uncoated and PEG-coated CS NPs encompassing either BT, ND, or both (BT-ND). The sizes of the developed BT/CS NPs, ND/CS NPs, and BT-ND/CS NPs were 176.84 ± 7.45, 204.1 ± 13.6, and 201.1 ± 23.35 nm, respectively. Meanwhile, the sizes of the synthesized BT/PEG-CS NPs, ND/PEG-CS NPs, and BT-ND/PEG-CS NPs were 165.1 ± 32.40, 148.2 ± 20.98, and 143.7 ± 7.72 nm, respectively. The surface charges of the fabricated nanoparticles were considerably high. All of the synthesized nanoparticles displayed a spherical form and significant entrapment efficiency. Release experiments demonstrated that the PEGylated and non-PEGylated CS NPs could discharge their contents into the tumor cells’ microenvironments (pH 5.5). In addition, the NPs demonstrated an outstanding ability to reduce the viability of the MCF-7 cell line. In addition, BT-ND/PEG-CS NPs were found to be the strongest among all NP preparations, where they caused around 90% decrease in the size of mammary gland tumors in rats compared to vehicle-treated animals.
Gastrointestinal (GI) diseases have become a global health issue and an economic burden due to their wide distribution, late prognosis, and the inefficacy of recent available medications. Therefore, it is crucial to search for new strategies for their management. In the recent decades, mesenchymal stem cells (MSCs) therapy has attracted attention as a viable option for treating a myriad of GI disorders such as hepatic fibrosis (HF), ulcerative colitis (UC), acute liver injury (ALI), and non-alcoholic fatty liver disease (NAFLD) due to their regenerative and paracrine properties. Importantly, recent studies have shown that MSC-derived extracellular vesicles (MSC-EVs) are responsible for most of the therapeutic effects of MSCs. In addition, EVs have revealed several benefits over their parent MSCs, such as being less immunogenic, having a lower risk of tumour formation, being able to cross biological barriers, and being easier to store. MSC-EVs exhibited regenerative, anti-oxidant, anti-inflammatory, anti-apoptotic, and anti-fibrotic effects in different experimental models of GI diseases. However, a key issue with their clinical application is the maintenance of their stability and efficacy following in vivo transplantation. Preconditioning of MSC-EVs or their parent cells is one of the novel methods used to improve their effectiveness and stability. Herein, we discuss the application of MSC-EVs in several GI disorders taking into account their mechanism of action. We also summarise the challenges and restrictions that need to be overcome to promote their clinical application in the treatment of various GI diseases as well as the recent developments to improve their effectiveness. Graphical abstract A representation of the innovative preconditioning techniques that have been suggested for improving the therapeutic efficacy of MSC-EVs in GI diseases. The pathological conditions in various GI disorders (ALI, UC, HF and NAFLD) create a harsh environment for EVs and their parents, increasing the risk of apoptosis and senescence of MSCs and thereby diminishing MSC-EVs yield and restricting their large-scale applications. Preconditioning with pharmacological agents or biological mediators can improve the therapeutic efficacy of MSC-EVs through their adaption to the lethal environment to which they are subjected. This can result in establishment of a more conducive environment and activation of numerous vital trajectories that act to improve the immunomodulatory, reparative and regenerative activities of the derived EVs, as a part of MSCs paracrine system. ALI, acute liver injury; GI diseases, gastrointestinal diseases; HF, hepatic fibrosis; HSP, heat shock protein; miRNA, microRNA; mRNA, messenger RNA; MSC-EVs, mesenchymal stem cell-derived extracellular vesicles; NAFLD, non-alcoholic fatty liver disease; UC, ulcerative colitis.
Background In 2019, the U.S. Food and Drug Administration approved a brand-new combination of linagliptin and empagliflozin in a formulation called Glyxambi® tablets for managing type 2 diabetes mellitus. Nowadays, spectrophotometric techniques occupy the first place among their peers in terms of ease of application, friendliness to the environment, and low costs. Objective This research discusses the development of two very simple spectrophotometric protocols based on zero-order spectra for the determination of linagliptin and empagliflozin. Methods The developed protocols were the induced dual-wavelength and absorption correction protocols. Linagliptin could be determined directly at 305 nm, at which the empagliflozin spectrum was zero-crossing. Empagliflozin was determined using the two developed protocols. The induced dual-wavelength technique was developed by calculating the equality factor of linagliptin to cancel its interference. The absorption correction technique was developed by measuring the correction absorption factor. Results The concentration ranges of linagliptin and empagliflozin were 1–10 µg/mL and 3–30 µg/mL, respectively. Excellent recovery results were found in bulk, dosage form, and synthetic mixtures. Low LOD and LOQ values were obtained, indicating the high sensitivity of the protocols. The statistical Student’s t-test was performed to compare the results of the applied and reported protocols, indicating no difference between them. Conclusion The proposed protocols have the advantages of being straightforward, affordable, and requiring no sophisticated manipulations, just simple mathematical calculations. The proposed protocols are acceptable for routine usage in QC laboratories and in future research applications. Highlights Two novel univariate methods were developed for quantitative analysis of linagliptin and empagliflozin in their pharmaceutical and laboratory mixtures, and produced satisfactory results.
Dialogue generation systems (DGS) is an important topic in the field of Natural Language Processing (NLP). It enables a wide range of real-world applications to interact with humans in various languages naturally and intelligently. Due to the complexity of the Arabic language, the development of an Arabic DGS is a challenge. In this paper, an Arabic DGS is developed using a sequence-to-sequence (Seq2Seq) framework comprised of Long-Short Term Memory (LSTM). A dataset of 12k conversations is translated from English to Arabic for the development process. The proposed DGS is analyzed, and its effectiveness is assessed using the Bilingual Evaluation Understudy (BLEU) score. The TensorFlow deep learning framework is used to test the proposed DGS. Also, the proposed DGS is tested and compared with a recently developed DGS. The Experiments revealed that the proposed Arabic DGS is effective in eliciting positive responses, with a BLEU score of 0.5988.
Institution pages aggregate content on ResearchGate related to an institution. The members listed on this page have self-identified as being affiliated with this institution. Publications listed on this page were identified by our algorithms as relating to this institution. This page was not created or approved by the institution. If you represent an institution and have questions about these pages or wish to report inaccurate content, you can contact us here.