Egyptian Russian University

In mobile security and privacy, android malware detection represents an important security challenge in the Android operating system. Since Android is customizable and an open-source operating system, malware such as spyware, Trojans, keyloggers, and other forms of malware can be injected by attackers to penetrate Android-based mobile devices. In response to these security threats, this paper introduces a practical comparison study to investigate an artificial intelligence framework consisting of nine machine learning techniques, and two neural networks techniques for accurately detecting android malware. For testing the efficiency of the proposed AI framework in detecting Android malware, a benchmark and labeled malware dataset from the Kaggle platform has been used to train and test the proposed AI framework. The experimental results showed that Random Forest and Gradient Boosting machines (GBM) outperformed other machine and Neural networks models in detecting Android malware, with malware detection accuracy reaching 97%. These findings can support strong recommendations to develop more robust and secure AI models for detecting recent Android attacks such as BingoMod malware, one of the recent Remote access trojans (RATs) android malware that allows cybercriminals to make unauthorized money transfers remotely.

There is justification for optimism about the potential contribution of alternative medicines to cancer management, which now ranks as the second leading cause of death globally. Primary carcinogens arise from several sources, including agriculture, industry, and dietary intake. Gastric cancer (GC) significantly affects an individual’s health due to its classification as a malignant tumor associated with elevated mortality and morbidity rates. Chemotherapy is now widely regarded as the gold standard for treating GC. Chemotherapy, however, exerts significant detrimental effects on human health, including irreversible damage to multiple organs. Consequently, it is essential to employ innovative strategies for cancer prevention. Natural products are now the focus of intensive study due to their efficacy against cancer and low toxicity levels. Natural compounds have shown a diverse range of anti-cancer properties. This review aims to emphasize studies on natural compounds that inhibit metastasis, induce apoptosis in GC, and decrease cellular proliferation. All the natural compounds from different sources were incorporated in this review not only medicinal plants derived compounds. This review aims to examine a comprehensive array of natural therapies that may enhance human health and facilitate GC prevention without inducing discernible negative effects. Moreover, this review aims to discuss the toxic side effects of phytochemicals and shed light on mechanisms underlying the action of potential natural products against GC. This review offers a novel perspective by integrating a broad spectrum of natural compounds from diverse sources, not limited to medicinal plants, to explore their anti-cancer properties against gastric cancer.

The influence of various patch shapes with different composite materials on the behavior of the repaired aluminum (2024-T3) plate with a central circular notch was investigated. Finite element analysis FEA for different scenarios of the repaired plate has been performed to obtain normal stress σY and stress concentration factor SCF. Comparison of FEA results was done to investigate the effect of parameters such as the material and shape patch. Also, the comparison between the effects of using a single patch and a double patch on the repaired plate was done. In the analysis, the patch shapes considered with the same volume are rectangular, circular, trapezoidal, triangle, regular hexagonal, rotated regular hexagonal, elliptical, rotated elliptical. Based on the reduction of SCF and σY, the comparison of results revealed that the optimum composite patch shape applied on a central circular notch plate, repaired, depends on the repair method, i.e., using a single or double patch. The optimum patch shape is a rotated regular hexagonal shape when using a single patch, but a rectangular shape when using a double patch.

The reno-protective potential of canagliflozin (Cana), an inhibitor of the sodium glucose–linked co-transporter-2 (SGLT-2), has been demonstrated in different models of kidney injury. However, its potential role in preventing glycerol (Gly)-induced acute kidney injury (AKI) remains to be divulged. Therefore, the aim of this study is to investigate the potential reno-protective effect of Cana and its underlying mechanism in a rat model of Gly-induced AKI. Rats were randomly allocated into five groups: normal, Gly, Gly pretreated with 10 mg/kg Cana, Gly pretreated with Cana 25 mg/kg, and normal pretreated with Cana 25 mg/kg for 14 consecutive days. Pretreatment with Cana improved renal structure and enhanced kidney functions manifested by reducing serum creatinine and blood urea nitrogen, as well as renal contents of neutrophil gelatinase-associated lipocalin and kidney injury molecule. Moreover, Cana signified its anti-inflammatory effect by reducing the Gly-induced elevation in renal contents of nuclear factor-κB and interleuκin-6. Additionally, Cana augmented the defense enzymatic antioxidants superoxide dismutase (SOD), manganese-SOD, and heme oxygenase-1, besides increasing the protein expression of the antioxidant transcription factor nuclear factor erythroid 2–related factor 2 to point for its ability to correct redox balance. Cana also upregulated the protein expression of the 5′ adenosine monophosphate-activated protein kinase (AMPK), Sirtuin1 (SIRT1), Forkhead box protein O3 (FOXO-3a), and peroxisome proliferator-activated receptor-gamma coactivator 1α (PGC-1α), as well as the transcriptional activity of growth arrest and DNA damage–inducible protein alpha (GAAD45a). In conclusion, Cana demonstrated potentially novel reno-protective mechanisms and mitigated the consequences of AKI through its antioxidant and anti-inflammatory properties, partially by activating the AMPK/SIRT1/FOXO-3a/PGC-1α pathway.

Five sustainable and validated UV spectrophotometric methods were developed for analyzing chloramphenicol (CHL) and dexamethasone sodium phosphate (DSP) in pure and ophthalmic dosage forms. CHL was detected by zero order spectra method at 292.0 nm in the range 2.00–32.00 µg/mL with limits of detection (LOD) and quantification (LOQ) of 0.96 and 2.88, respectively. DSP was analyzed using the following four techniques: Induce dual wavelength (IDW), fourier self-deconvolution (FSD), ratio difference (RD), and derivative ratio (DD¹). The IDW method used at 239.0 and 254.0 nm with a linearity range of 4.00–40.00 µg/mL with LOD and LOQ values were 0.93 and 2.79, respectively. The FSD approach used at 242.0 nm, with a linearity range of 2.00–32.00 µg/mL and 0.65, 1.95 as values of LOD and LOQ, respectively. In the linearity range of 4.00–32.00 µg/mL, RD and DD¹ are applied. RD is utilized at 225.0–240.0 nm, while DD¹ is carried out at 249.0 nm. Values of LOD and LOQ for RD were 0.70 and 2.10 while for DD¹ were 0.80 and 2.40, respectively. These methods were evaluated for their environmental sustainability and validated according to ICH guidelines, overcoming challenges like spectral overlap and collinearity. Statistical comparisons with published methods revealed no significant differences.

Background Early antibiotic exposure in preterm infants may disrupt gut microbiome development, affecting health. However, its link to late-onset sepsis (LOS) remains unclear. This meta-analysis aims to clarify the association while addressing confounding bias. Methods This systematic review and meta-analysis, conducted per PRISMA guidelines, utilized PubMed, Scopus, Google Scholar, and Web of Science for comprehensive literature retrieval. Studies comparing preterm infants with sterile blood cultures who received early antibiotics (short or prolonged) to those without, using LOS as the primary outcome, were included. Comparisons between short- and prolonged-course antibiotics were also considered. Only studies with adjusted analyses for confounders were considered. Adjusted odds ratios (aOR) were meta-analyzed, and the prediction interval (PI) was calculated using R software. Results Ten studies met the eligibility criteria, comprising a total sample size of 55,089 preterm infants. Among these, nine studies included 33,549 preterm infants and compared prolonged antibiotic exposure to short exposure. Prolonged exposure was not significantly associated with LOS (pooled aOR = 1.2, 95% CI 0.99–1.46, P = 0.066, PI = 0.66 to 2.19, I² = 67%). Limiting the analysis to five studies with sample sizes over 1,000 reduced heterogeneity (I² = 30%) and provided a more precise confidence interval (pooled aOR = 1.03, 95% CI 0.91–1.15). Four studies, involving 41,938 preterm infants, examined preterm infants exposed to prolonged antibiotics versus those not exposed and found no significant association (aOR = 0.91, 95% CI 0.82–1.02, P = 0.1, PI = 0.72 to 1.16, I² = 0). All four studies had sample sizes exceeding 1,000. Additionally, these studies compared preterm infants with short antibiotic exposure to non-exposure, revealing a slightly lower risk of LOS (aOR = 0.87, 95% CI 0.77–0.98, P = 0.024, I² = 0) and a PI of 0.76 to 1.14. Conclusions Our findings indicate that prolonged early antibiotic exposure in preterm infants with sterile cultures does not significantly increase the risk of LOS compared to no antibiotic exposure. Interestingly, a shorter duration of antibiotic exposure might be associated with a slightly lower risk of LOS.

This study investigates new anticonvulsant substances that target the epilepsy-associated carbonic anhydrase isoforms II and VII. The 1,2,3-triazole with a benzenesulfonamide motif is present in the produced molecules. Of these, 5b and 5c exhibited remarkable selectivity and inhibitory efficacy toward hCA VII and hCA II over hCA I. The KI values of 5b and 5c were 6.3 and 10.1 nM, respectively, and 21.6 and 18.9 nM, respectively. In a pilocarpine-induced paradigm, in vivo assessments showed decreased seizure severity and susceptibility with delayed seizure onset and diminished intensity. The quick absorption and in vivo stability of 5b were demonstrated by pharmacokinetic investigations. Evaluations of toxicity showed no neurotoxic effects and a high safety margin (LD50 > 2000 mg/kg). Mechanistic research has shown effectiveness in maintaining neuronal integrity, reducing mTOR activation, and raising hippocampus KCC2 levels. Compound 5b’s binding interactions with hCA II and hCA VII were clarified by docking and dynamics experiments.

Hesperidin, a bioflavonoid abundantly found in citrus fruits, offers a myriad of health benefits. With the food industry extensively utilizing citrus fruits, particularly for juice production, substantial quantities of by‐products such as peels, seeds, cells, and membrane residues accumulate. Remarkably, these by‐products serve as a valuable source of hesperidin. Consequently, the extraction of hesperidin from these by‐products has garnered significant scientific interest, aiming to harness its potential as a natural antioxidant. By shedding light on these aspects, this review provides a comprehensive review of hesperidin's role in enhancing human wellbeing, particularly in the context of chronic fatigue syndrome (CFS). By synthesizing current research, we elucidate the compound's antioxidant, anti‐inflammatory, and neuroprotective effects, which may mitigate symptoms associated with CFS. Furthermore, we introduce machine learning methodologies to predict hesperidin's efficacy in clinical settings, offering a novel perspective on personalized nutrition strategies. Our findings underscore the need for further empirical studies to validate these predictions and explore hesperidin's mechanisms of action. This review not only bridges the gap between nutrition science and pharmacology but also highlights the promising future of hesperidin as a nutraceutical in combating chronic health conditions.

Lipases are extraordinarily critical co-factor-independent enzymes with profound economic consequences. They are utilized extensively in production of fine chemicals, food, textile, pulp and paper, laundry, and biodiesel sectors. In the current study, the lipolytic activity of 141 fungal isolates—representing 21 genera and 38 species—that were isolated from samples of desert soil gathered from the Governorates of Sohag, Qena, and Aswan were examined. Of the 74 isolates showed positive lipase activity, 40 were high lipase producers. In terms of lipase production, Aspergillus terreus AUMC 15762 was the most effective strain. To enhance the synthesis of lipase from Aspergillus terreus AUMC 15762, Plackett–Burman design (PBD) was employed. For the maximal amount of lipase synthesis (103.3 U/mL), ammonium sulphate was required after three days at 25 °C, pH 4.0, and 3.0 g/L. Through the use of Trilite MA 12 anion exchanger and Sephadex G-100 column chromatography, lipase was purified 17.79 times and achieved 64.714 kDa molecular weight on SDS-PAGE. The highest possible specific activity of 3867.85 ± 214.28 U/mg was attained at pH 8.0 and 40 °C. The addition of KCl and ZnSO4 raised the lipase specific activity by 115.42%. Km of 19.0 mg/mL and Vmax of 1000 μmol/min were determined for the pure lipase. The effects of 20 U/mL pure lipase on corn and olive oily spots were examined in this work at pH 8.0 and 40 °C. The pure lipase completely removed oil contamination from fiber surfaces, as evidenced by the oily spots’ separation from the white cotton textiles after 60 min. This work offers a lipase produced from Aspergillus terreus species that showed promise for industrial laundry applications.

This paper presents a method for detecting diseases in oranges using a pretrained ResNet50 model. The diseases targeted are citrus canker, black spot, and citrus greening, in addition to fresh oranges. The goal is to achieve high classification accuracy while making the model more explainable to humans by using LIME (Local Interpretable Model-Agnostic Explanations) for interpretability. The dataset consists of 991 training images and 99 test images, divided across the four categories. The model achieved a test accuracy of 96.97%, a test loss of 3.6373, and a ROC AUC score of 0.978..

In this paper, we present the applications of Generative Artificial Intelligence (GAI) in preotecting 6G-enabled IoT networks from different cyber threats. The paper shall leverage Generative Adversarial Networks (GANs) and federated learning, in order to propose a novel framework for detecting therefore mitigates sophisticated cyber-attacks in real-time. Internet of Things (IoT) devices using 6G wireless networks shall provide opportunities for enhancing connectivity by enabling secured smart applications. However, these 6G advancements also introduce significant cybersecurity challenges which require robust threat prevention strategies. The proposed framework takes advantages of the inherent 6G capability, such as high data rates, low latency, and network slicing. It implemented a distributed, AI-driven security mechanism that adapts to evolving threats. The simulation results and it analysis, demonstrates the "effectiveness" of the proposed framework in preventing a wide range of cyber-attacks, such as phishing, malware, and denial-of-service (DoS) attacks. It also ensures the integrity, confidentiality, and availability of IoT services. The results of this study demonstrate the integration of AI in next-generation wireless networks, and offer insights into the design of resilient and the application of adaptive cybersecurity solutions.

In this paper, we present a prototype of a miniature radar system using the ESP32 microcontroller and an HC-SR04 ultrasonic sensor. The prototype utilizes a servo motor to rotate the ultrasonic sensor across a 180-degree arc to mimic the functionality of traditional radar systems. The prototype enables the radar to scan and detect objects and support applications in autonomous vehicle navigation, monitoring, and proximity detection in smart home systems. The prototype highlights the adaptability of the ESP32 and ultrasonic technology in creating cost-effective, scalable, and efficient radar-like systems.

This study presents the development and validation of an advanced object detection model based on YOLOv9 for the automated identification of dental caries. Utilizing a dataset of 270 dental images sourced from Kaggle, this project introduces a methodological framework that includes image preprocessing, augmentation, and annotation to address the challenge of limited data. The dataset was expanded through augmentation techniques such as flipping, rotation, shearing, and noise addition, resulting in a final dataset size that supports robust model training and evaluation. Employing the YOLOv9 model, known for its speed and accuracy, we conducted extensive training, optimization, and validation processes. Our results showcase a significant improvement in model performance, with a mean Average Precision (mAP) increase from 59.6% to 93.485%, alongside substantial gains in precision, recall, and F1 scores for detecting dental caries. This study not only highlights the effectiveness of YOLOv9 in dental anomaly detection but also sets a precedent for employing advanced object detection models in medical imaging analysis, offering potential enhancements in diagnostic accuracy and efficiency.

Cardiovascular diseases are the major cause of global mortality, and often require the concomitant use of a number of drugs to prevent and reduce these deaths. The challenge is to find effective and accurate methods for analyzing these drugs in plasma. This research introduces an innovative, sustainable HPLC-FLD method for the concurrent determination of bisoprolol (BIS), amlodipine besylate (AML), telmisartan (TEL), and atorvastatin (ATV) within human plasma. Chromatographic separation was achieved using an isocratic elution mode on a Thermo Hypersil BDS C18 column (150 × 4.6 mm, 5.0 μm), while the mobile phase comprised of ethanol and 0.03 M potassium phosphate buffer (pH 5.2) in a 40:60 ratio, with a flow rate of 0.6 mL/min. The eluate was analyzed using UV detection within the wavelength range of 210–260 nm to confirm effective separation of the four cardiovascular drugs. For enhanced specificity, a fluorescence detector was set to 227ex/298em for BIS, 294ex/365em for TEL, 274ex/378em for ATV, and 361ex/442em for amlodipine. The method was validated following the International Council for Harmonisation (ICH) guidelines. Linearity was established within the ranges of 5–100 ng/mL for BIS and AML, 0.1–5 ng/mL for TEL, and 10–200 ng/mL for ATV, ensuring accuracy and precision. The significant of the current work represented in introduction of a highly sensitive, and selective analytical method, utilizing an economical sample preparation strategy, for the simultaneous determination of four different cardiovascular drugs (bisoprolol, amlodipine, telmisartan, and atorvastatin) in spiked human plasma. The extraction of sample was carried by liquid-liquid extraction (LLE) and analyzed by LC-fluorescence detector. The chromatographic run was short (less than10 min) which is a greet economical value.

Neurodegeneration is the progressive loss of neurons that results in neurodegenerative diseases (NDs). Currently, there are few effective treatments for NDs, such as Alzheimer’s disease, Parkinson’s disease, Multiple sclerosis, and Huntington’s disease, which involve gradual neuronal death and cognitive deterioration. Alkaloids are naturally occurring molecules with a variety of biological properties. Recent studies have shown that these compounds may be able to modulate signaling pathways linked to many diseases. Alkaloids, with their antioxidant and neuroprotective properties, have the potential to treat neurodegeneration by simultaneously affecting multiple disease parts and modifying neuroinflammatory responses. These interact with various molecular targets, such as transcription factors, receptors, and enzymes involved in neuronal survival and homeostasis. The development of complete therapeutic techniques can be facilitated by alkaloid-based multi-target approaches, which challenge the intricate nature of neurodegenerative pathways. The review highlights the potential of alkaloid-based multi-target strategies in treating NDs and calls for further research to understand their clinical applications fully. Future studies should focus on finding neuroprotective alkaloids, investigating their mechanisms, and evaluating their therapeutic potential. Understanding how alkaloids interact with key pathways in NDs is essential for developing effective therapies. Graphical abstract

Introduction Chronic obstructive pulmonary disease (COPD) is a well-known respiratory illness, and COPD patients oscillate between a stable state and an exacerbated state. which can lead to disease deterioration. Studies suggest that respiratory microbiome dysbiosis plays a vital role in COPD exacerbation. However, the exact microbial composition among different clinical states of COPD is still elusive. Objectives To determine and compare the respiratory microbiome composition in different COPD clinical states, namely, the stable state (S-COPD) and the acute exacerbated state (AE-COPD). Methods In this study, 35 sputum samples were collected from COPD patients: S-COPD patients (n = 18), and AE-COPD patients (n = 17). The sputum microbiome was analyzed via 16S rRNA gene sequencing. Bioinformatics analysis was used to determine changes in the microbiota among the comparison groups. Results The most abundant phyla among all the samples were Proteobacteria, Fusobacteria, Firmicutes, and Actinobacteria, with Paracoccus, Streptomyces Leptotrichia Fusobacterium and Ruminococcaceae being the most prevalent genera.A dissimilarity in abundance across the studied COPD states was observed, with signi cantly greater abundance of Proteobacteria and Fusobacteria in S-COPD patients and greater abundance of Firmicutes in AE-COPD patients at the phylum level. Paracoccus, Fusobacterium, Streptococcus, Haemophilus and Moraxella were signi cantly different between the two groups and were more prevalent in S-COPD, whereas Cellulosilyticum, Streptomyces, Leptotrichia, Ruminococcaceae_UCG_014 and Atopobium were more prevalent in exacerbated individuals. Alpha diversity revealed greater diversity in stable versus exacerbated patients, and a PCoA plot of Bray-Curtis and weighted UniFrac distances revealed that stable patients were highly clustered, whereas exacerbated patients were more disseminated. At the genus level, LEfSe analysis revealed the dominance of Cellulosilytic, Liptotrichia and Streptomyces in the AE-COPD group, whereas the S-COPD group microbiome was dominated by the genera Paracoccus, Fusobacterium, Streptococcus Haemophilus and Moraxella (p < 0.05). Conclusion The results of the present study suggest that COPD patients have unique microbial pro les that differ across different states, with increased abundances of Proteobacteria, chie y Paracoccus. These ndings need more research to clarify the de nite role of microbiome dysbiosis in COPD pathogenesis.

Introduction Avanafil (AVA) is a very efficient phosphodiesterase type 5 inhibitor for the treatment of erectile dysfunction. However, it has limited bioavailability when taken orally and considerable first-pass metabolism. Enhancing its solubility and choosing an alternative delivery route may enhance its effectiveness and duration of action. Methods Eight complex formulations were elaborated and analyzed at various ratios using different polyethylene glycols and hydroxypropyl-beta-cyclodextrin (HP-β-CD). Sublingual tablets containing AVA were designed and optimized using the Quality-by-design approach. The tablets’ pre-compression and post-compression properties were evaluated. The in-vivo pharmacokinetic behavior of the optimized tablet was assessed and compared with that of the commercial oral tablets in human volunteers. Results The HP-β-CD–AVA inclusion complex (1:1 molar ratio) showed an optimum solubilization capacity with an amount suitable for incorporation into sublingual tablets. The total amounts of superdisintegrants and Plasdone XL and the percentage of starch significantly influenced the length of time it took for 80% of the AVA to be released from the sublingual tablets, the tablet hardness, and the length of time for tablet disintegration. The optimized AVA sublingual tablet exhibited a 5.98-fold increase in the AVA mean residence time over the commercial tablet, with greater plasma exposure over 72 hours and 1356.42% relative bioavailability. Conclusion The sublingual tablets of the solubility-enhanced HP-β-CD–AVA inclusion complex represent a promising strategy to improve AVA bioavailability and bypass the first-pass effect. Furthermore, their extended activity offers potential clinical benefits, particularly for ED patients, such as ease of administration and reduced side effects.