Umm Al-Qura University
  • Mecca, Makkah Province, Saudi Arabia
Recent publications
Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) predisposed to the emergence of worldwide catastrophe that impels the evolution of safe and effective therapeutic system. Polyphenols as resveratrol (RSV) exhibit a well evidenced antiviral activity. Unfortunately, like most phenolic nutraceuticals, RSV suffers from restrained solubility and massive degradation in GIT and liver which in turn prohibit its clinical use. Herein, PEGylated bilosomes (PBs) contain PEGylated edge activator along with the traditional components as (Span 60, cholesterol and bile salts) were proposed to boost both permeability and bioavailability of RSV. The investigation of the prominent effect of the diverse variables on the characteristics of the vesicles and picking of the optimum formula were conducted via construction of 2³ factorial experiment. The appraisal of the formulae was conducted on the basis of entrapment efficiency percent (EE%), particle size (PS) and zeta potential (ZP). In addition, the spherical shaped optimal formula (F5) exhibited EE% of 86.1 ± 2.9%, PS of 228.9 ± 8.5 nm, and ZP of −39.8 ± 1.3 mV. The sorted optimum formula (F5) exhibited superior dissolution behaviors, and boosted Caco-2 cells cellular uptake by a round 4.7 folds relative to RSV dispersion. In addition, F5 demonstrated a complete in vitro suppression of SARS-CoV-2 at a concentration 0.48 μg/ml with 6.6 times enhancement in antiviral activity relative to RSV dispersion. The accomplished molecular modeling heavily provided proof for the possible interactions of resveratrol with the key residues of the SARS-CoV2 Mpro enzyme. Finally, F5 could be proposed as a promising oral panel of RSV for curation from SARS-CoV-2 infection.
The Internet of Medical Things (IoMT) effectively tackles several shortcomings of conventional healthcare systems. It includes medical personnel shortages, patient care quality, insufficient medical supplies, and healthcare expenditures. There are several advantages of using IoMT technology for enhanced treatment efficiency and quality, thus improving patient health. However, the frequency and magnitude of cyberattacks on IoMT are increasing at a breakneck pace. Therefore, this article proposes a cyberattack detection method for IoMT-based networks using ensemble learning and fog-cloud architecture to address security issues. The ensemble technique employs a set of LSTM networks as individual learners at the first level and stacks a decision tree on top of them to classify attack and normal events. In addition, we present a framework for deploying the proposed IoMT-based approach as Infrastructure as a Service (IaaS) in the cloud and Software as a Service (SaaS) in the fog. The proposed method is evaluated on the ToN-IoT dataset, and the outcomes reveal that it surpasses the baseline approaches in terms of precision by 4%.
Catheter‐associated urinary tract infection (CAUTI) is a common complication associated with indwelling urinary catheters, frequently used in healthcare settings. Nurses play a critical role in preventing CAUTI, as they are often responsible for inserting, maintaining and removing urinary catheters. Therefore, it is important to comprehensively assess nurses' level of knowledge about CAUTIs and the variables that influence their application of best practices and recommendations for preventing these infections. The PRISMA principles were used to conduct a literature search for relevant research publications across several online databases (Web of Science, PubMed, MEDLINE and Scopus). The quality of these studies was evaluated using the Mixed Methods Appraisal Tool. There were 397 research articles, however only 21 articles were included after the screening. The majority of participants possessed diplomas ranging from 3% to 88.2%. In addition, the percentage of nurses with bachelor's degree's ranges from 11.80% to 100%. Moreover, 23.90% of registered nurses hold a master's degree. Most nurses had between 1 and 5 and more than 5 years of experience. Nurses held good/adequate and average knowledge and practices regarding prevention and control of CAUTIs. Furthermore, age, gender, work experience, professional experience, in‐service training, CAUTI prevention guidelines, time, equipment, personnel availability and work unit were all identified barriers. While continuing/in‐service education and self‐guided modules served as facilitators for the prevention of CAUTIs. Meanwhile, studies were found of good methodological quality. Improving nurses' knowledge and practice towards preventing CAUTI is crucial to reducing the prevalence of the infection and improving patient outcomes. Implementing evidence‐based interventions can help bridge the gap in knowledge and practice among nurses, ultimately leading to better patient care and outcomes.
Nitrates level in water is a worldwide problem that represents a risk to the environment and people’s health; efforts are currently devoted to the development and implementation of new biomaterials for their removal. In this study, chitosan (Ch) from shrimp waste and the related epichlorohydrin-modified crossover chitosan (Ch-EPI) were used to remove nitrates from aqueous solutions. The mechanism of selective nitrate removal was elucidated and validated by theoretical calculations. The physicochemical performance of Ch and Ch-EPI was investigated through the main parameters pH, adsorption capacity, contact time, initial nitrate concentration, coexisting anions, and temperature. The experimental data were fitted to widely used adsorption kinetic models and adsorption isotherms. The maximum percentage of nitrate adsorption was reached at an equilibrium pH of 4.0 at an adsorbent dose of 2.0 g/L after a contact time of 50 min. Competing anion experiments show that chloride and sulfate ions have minimal and maximal effects on nitrate adsorption by Ch-EPI. Experimental adsorption data are best fitted to pseudo-second-order kinetic and isothermal Langmuir models. The maximum adsorption capacities of Ch and Ch-EPI for nitrate removal were 12.0 mg/g and 38 mg/g, respectively.
In order to achieve rapid detection of galactooligosaccharides (GOS), fructooligosaccharides (FOS), calcium (Ca), and vitamin C (Vc), four micronutrient components in infant formula milk powder, this study employed four methods, namely Standard Normal Variate (SNV), Multiplicative Scatter Correction (MSC), Normalization (Nor), and Savitzky–Golay Smoothing (SG), to preprocess the acquired original spectra of the milk powder. Then, the Competitive Adaptive Reweighted Sampling (CARS) algorithm and Random Frog (RF) algorithm were used to extract representative characteristic wavelengths. Furthermore, Partial Least Squares Regression (PLSR) and Support Vector Regression (SVR) models were established to predict the contents of GOS, FOS, Ca, and Vc in infant formula milk powder. The results indicated that after SNV preprocessing, the original spectra of GOS and FOS could effectively extract feature wavelengths using the CARS algorithm, leading to favorable predictive results through the CARS-SVR model. Similarly, after MSC preprocessing, the original spectra of Ca and Vc could efficiently extract feature wavelengths using the CARS algorithm, resulting in optimal predictive outcomes via the CARS-SVR model. This study provides insights for the realization of online nutritional component detection and optimization control in the production process of infant formula.
A series of chromium-doped calcium ferrite samples of composition Ca0.5Mg0.5Fe12−xCrxO19 (x = 0.0, 0.5, 1.0, 1.5, 2.0) was meticulously prepared using the standard sol-gel combustion route. Structural and morphological properties were comprehensively studied via X-ray diffraction and Scanning Electron Microscopy (SEM). The lattice parameters ‘a’ and ‘c’ unequivocally validated the space group of the samples as P63/mmc (194). Moreover, the lattice parameters derived from the XRD data demonstrated commendable agreement with the values extrapolated from theoretical cation distribution and Rietveld refinement calculations. It was further confirmed that the crystallite size, as determined by XRD, concurred excellently with the particle size measurements from the scanning electron micrographs. The samples’ magnetic characterization was carried out using a vibrating sample magnetometer (VSM). Notably, the saturation magnetization (Ms) and remanence (Mr) were found to exhibit an increase, while coercivity (Hc) displayed a concurrent decrease with higher substitution ratios. The reason behind the enhancement of saturation magnetization and remanence, along with the reduction in coercivity, was elucidated based on involved factors such as crystallite size, anisotropy constant, and cation distribution. Results showed that including chromium significantly softened the ferrite materials, making them ideal for advanced high-end recording applications. The samples also exhibited low coercivity and impressive saturation magnetization, enhancing their value as storage devices tailored for information storage and recording purposes.
Background Ritonavir was recently combined with nirmatrelvir in a new approved co-packaged medication form for the treatment of COVID-19. Quantitative analysis based on fluorescence spectroscopy measurement was extensively used for sensitive determination of compounds exhibited unique fluorescence features. Objective The main objective of this work was to develop higher sensitive cost effective spectrofluorometric method for selective determination of ritonavir in the presence of nirmatrelvir in pure form, pharmaceutical tablet as well as in spiked human plasma. Methods Ritonavir was found to exhibit unique native emission fluorescence at 404 nm when excited at 326 nm. On the other hand, nirmatrelvir had no emission bands when excited at 326 nm. This feature allowed selective determination of ritonavir without any interference from nirmatrelvir. The variables affecting fluorescence intensity of ritonavir were optimized in terms of sensitivity parameters and principles of green analytical chemistry. Ethanol was used a green solvent which provided efficient fluorescence intensity of the cited drug. Results The method was validated in accordance with the ICH Q2 (R1) standards in terms of linearity, limit of detection (LOD), limit of quantification (LOQ), accuracy, precision and specificity. The described method was successfully applied for ritonavir assay over the concentration range of 2.0–20.0 ng/mL. Conclusion Ritonavir determination in the spiked human plasma was successfully done with satisfactory accepted results.
A precise, Eco-friendly, and highly sensitive RP-HPLC method was employed using quality-by-design principles to concurrently identify cephalexin and cefixime residues in the manufacturing machines using a hypersil BDS C18 column (250 × 4.6 mm, 5 μm) at wavelength 254 nm. The Box–Behnken design was applied to obtain the best chromatographic conditions with the fewest possible trials. Three independent factors viz organic composition, flow rate, and pH were used to assess their effects on the responses' resolution and retention time. Overlay plot and desirability functions were implemented to predict responses of the high resolution and relatively short retention time using a mobile phase composed of acidic water: acetonitrile (85:15, v/v) at pH 4.5 adjusted by phosphoric acid with a flow rate of 2.0 mL/min. The spectral overlapping of the drugs was successfully resolved by the mean centering ratio (MCR) spectra approach at 261 nm and 298 nm for cephalexin and cefixime, respectively. Good linearity results were obtained for the suggested HPLC and MCR methods over the concentration range of (0.05–10 ppm) and (5–30 ppm) with a detection limit of 0.003, 0.004, 0.26, and 0.23 ppm, and quantitation limits of 0.008, 0.013, 0.79, and 0.68 ppm for cephalexin and cefixime, respectively, with a correlation coefficient of ≥ 0.9998 and good swab recovery results of 99–99.5%. A process capability index was accomplished for chemical and micro results, illustrating that both are extremely capable. The suggested method was effectively validated using ICH recommendations.
Objectives Nuclear factor erythroid-2-related factor 2 (NRF2), a transcriptional gene factor related to nuclear factor erythroid 2, plays a role in the development of gliomas with isocitrate dehydrogenase (IDH) mutation. Its impact on tumour recurrence has seldom been investigated. Methods A group of 34 patients diagnosed with Grade 4 astrocytoma was included in a retrospective cohort. NRF2 protein and gene expressions were assessed using different profiling assays. The association between IDH mutation, NRF2 expression, and tumor recurrence was investigated. Results The mean patients’ age in this study was 50 years. Out of the total number of tumors analyzed, 21 of them had IDH mutation. NRF2 protein expression was found to be overexpressed in 27 tumors and reduced in 7 tumors. Additionally, NRF2 gene was upregulated in 24 tumors and downregulated in 10 tumors. Insignificant statistical difference was observed in recurrence-free interval (RFI) between patients with upregulated or downregulated NRF2 gene or protein expressions (p-value>0.05). However, this relationship is distinctive when NRF2 expression was compared to IDH mutation. Tumors with different levels of NRF2 expressions and IDH mutations showed significant statistical variation in RFI (p-value=0.001). There was insignificant impact on RFI among patients receiving different chemotherapies (TMZ or TMZ plus) who had abnormal NRF2 gene activities (p-value=0.97). Conclusions WHO-Grade 4 astrocytoma with IDH mutation and altered NRF2 expression showed a delayed tumor recurrence compared to IDH-wildtype glioblastoma.
This chapter examines the impact of bank characteristic in Islamic banks in the Kingdom of Saudi Arabia over the 2004–2019 period. In order to analyze the determinants of Islamic banking profitability in the Kingdom of Saudi Arabia, the panel autoregressive distributed lag (ARDL) method was used. The result of this chapter shows that total liquid assets (TLA) and rate of return on investment (ROI) is positively associated with profitability in Islamic banks at long run. The short-run estimation shows that capital adequacy ratio (CAR) and total liquid assets variables have a significant positive effect on return on assets index.
Abstract The significance of fuzzy volume percentage on the unsteady flow of MHD tangent hyperbolic fuzzy hybrid nanofluid towards an exponentially stretched surface is scrutinized. The heat transport mechanism is classified by Joule heating, nonlinear thermal radiation, boundary slippage, and convective circumstances. Ethylene glycol (EG) as a host fluid along with the nanomaterial’s Cu and $${\text{Al}}_{{2}} {\text{O}}_{{3}}$$ Al 2 O 3 are used for heat transfer analysis is also considered in this investigation. The nonlinear governing PDEs are meant to be converted into ODEs employing appropriate renovations. Then, a built-in MATLAB program bvp4c is employed to acquire the outcome of the given problem. The variation of flow rate, thermal heat, drag force and Nusselt number and their influence on fluid flow with heat transfer have been scrutinized through graphs. An increase in thermal radiation, power law index and nanoparticle volume friction heightens the heat transmission rate. Skin friction is diminished by swelling the power-law index, Weissenberg number, and ratio parameters, whereas it is increased by enhancing the magnetic parameter. The heat transfer rate upsurges with an increase in Weissenberg number and nanoparticle volume fraction. Also, the nanoparticle volume percentage is expressed as a triangular fuzzy number (TFN). The triangular membership function (MF) and TFN are regulated by the $$\chi - {\text{cut}}$$ χ - cut parameter, which has a range of 0 to 1. In comparison to nanofluids, hybrid nanofluids have a higher heat transmission rate, according to the fuzzy analysis. This investigation has applications in the areas of paper manufacturing, metal sheet cooling and crystal growth.
secretase 1 (BACE1) is an enzyme that is involved in generating beta-amyloid peptides and is believed to have a significant role in the development of Alzheimer’s disease (AD). Therefore, BACE1 has gained attention as a potential therapeutic target for treating AD. Modern drug discovery studies are being conducted to identify potential inhibitors of BACE1, with the goal of reducing the production of beta-amyloid peptides and, thus, slowing the progression of AD. Here, we used a multistep virtual screening methodology to identify phytoconstituents from the IMPPAT library that could inhibit the activity of BACE1. Molecular docking was employed to select initial hits based on their binding affinity toward BACE1. Screening for PAINS patterns, ADMET and PASS properties, was then used to identify potential molecules for BACE1 inhibition. In the end, we discovered two natural compounds, Peiminine and 27-Deoxywithaferin A, which demonstrated a strong affinity, effectiveness, and specific interactions for the BACE1-active site. The elucidated molecules also displayed drug likeliness. A 200 ns molecular dynamics (MD) simulation was conducted to investigate the interaction mechanism, complex stability, and conformational dynamics of BACE1 with Peiminine and 27-Deoxywithaferin A. The MD simulations demonstrated that BACE1 was stable during the simulation with Peiminine and 27-Deoxywithaferin A. Overall, the results suggested that Peiminine and 27-Deoxywithaferin A hold significant potential as scaffolds in drug development efforts targeting BACE1 for the purpose of treating AD.
Distinctness, uniformity, and stability (DUS) test is the legal requirement in crop breeding to grant the intellectual property right for new varieties by evaluating their morphological characteristics across environments. On the other hand, molecular markers accurately identify genetic variations and validate the purity of the cultivars. Therefore, genomic DUS can improve the efficiency of traditional DUS testing. In this study, 112 Egyptian fenugreek genotypes were grown in Egypt at two locations: Wadi El-Natrun (Wadi), El-Beheira Governorate, with salty and sandy soil, and Giza, Giza governorate, with loamy clay soil. Twelve traits were measured, of which four showed a high correlation above 0.94 over the two locations. We observed significant genotype-by-location interactions (GxL) for seed yield, as it was superior in Wadi, with few overlapping genotypes with Giza. We attribute this superiority in Wadi to the maternal habitat, as most genotypes grew in governorates with newly reclaimed salty and sandy soil. As a first step toward genomic DUS, we performed an association study, and out of 38,142 SNPs, we identified 39 SNPs demonstrating conditional neutrality and four showing pleiotropic effects. Forty additional SNPs overlapped between both locations, each showing a similar impact on the associated trait. Our findings highlight the importance of GxL in validating the effect of each SNP to make better decisions about its suitability in the marker-assisted breeding program and demonstrate its potential use in registering new plant varieties.
Preference analysis is an essential component of the decision-making (DM) process for identifying the optimal object. The rough set (RS) theory has been successfully extended to accommodate preference analysis by substituting the equivalence relation (ER) with the dominance relation (DR). On the other hand, bipolarity refers to the explicit handling of positive and negative aspects of data. In this article, we first established the idea of bipolar fuzzy preference relation (BFPR) from the bipolar fuzzy information system and studied some of its basic properties. Then, based on BFPR, a novel idea of \((\alpha , \beta )\)-bipolar fuzzified preference RSs (\((\alpha , \beta )\)-BFPRSs) is offered. Several significant structural properties of \((\alpha , \beta )\)-BFPRSs are analyzed in detail. Moreover, some significant uncertainty measures associated with \((\alpha , \beta )\)-BFPRSs are proposed. Meanwhile, we put forward the idea of bipolar fuzzy preference \(\delta \)-covering (\(BFP\delta C\)), bipolar fuzzy preference \(\delta \)-neighborhood (BFP\(\delta \)-nghd), and bipolar preference \(\delta \)-neighborhood (BP\(\delta \)-nghd). Moreover, we establish a new type of bipolar fuzzy RS (BFRS) model in the context of BFP\(\delta \)-nghd, and several relevant properties are explored. Using the theory of the \(BFP\delta C\)-based BFRS model, we develop a novel approach for multi-criteria decision-making (MCDM) problem. A real-life example of a smartphone selection is provided to show the applicability of our suggested approach. We further investigate the effectiveness of the developed technique using a comparative analysis. Last but not least, theoretical investigations and practical examples reveal that our suggested approach dramatically enriches the RS theory and offers a novel knowledge discovery strategy that is applicable in real-world circumstances.
Herein, titanium dioxide (TiO2) nanoparticles (NPs) were prepared via the sol–gel technique; then, they were incorporated into a ternary blend polymer matrix to design polymer nanocomposite (PNC) films through the solution casting technique. The ternary blend polymer matrix consisted of polyvinyl alcohol (PVA), polyvinyl pyrrolidone (PVP), and carboxymethyl cellulose (CMC). X-ray diffraction (XRD) analysis revealed reductions in the crystallinity structure of the polymer matrix after adding TiO2 NPs. The optical study manifested increases in the refractive index and reduction in the optical bandgap values, which reduced from 4.97 eV for the pure polymer blend to 4.77 eV for the PNC film at TiO2 content of 3 wt%. Additionally, the transmission edge gradually shifted towards lower energy. The PNC films exhibited considerable improvements in the dielectric constant (ε′), dielectric loss (ε′′), dielectric moduli (M′ and M′′), and electrical conductivity characteristics over the range of frequency range from 0.1 Hz to 10 MHz. The addition of TiO2 NPs improved the electrical conductivity and dielectric constant significantly. The electrical conductivity increased by over ten times compared to the pure ternary polymer blend, and ε′ also rose four-fold at 100 Hz. The enhancement in the electrical and dielectric parameters of the PNC films after adding TiO2 nanofiller could indicate the suitability of these samples for flexible-type energy storage applications, such as dielectric capacitors. Graphical Abstract
This paper's primary goal is to diagnose COVID‐19 contamination based on the artificial intelligence approach automatically. We used convolutional neural network deep learning algorithm for analyzing the ECG images to detect cardiac abnormalities, consequent of the contamination by the SARS‐CoV‐2 virus, responsible for the COVID‐19 epidemic. We designed, trained, and evaluated the performance of two deep learning models (MobileNetV2 and VGG16) in detecting and distinguishing between two different classes (healthy subjects and COVID‐19 positive cases). Indeed, this virus attacks the human respiratory system, which could affect the heart system. Thus, developing a deep learning model could help for a quick and efficient diagnosis, prediction, and physician decision‐making. The performed deep learning model will be used for predicting abnormal cardiac activities consequent to the contamination by the virus. The overall classification rate achieved by the models was 99.34% and 99.67% for MobileNetV2 and VGG16, respectively. Therefore, this approach can efficiently contribute to the diagnosis of COVID‐19 contamination.
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7,494 members
Muhammad Ahmed
  • College of Pharmacy
Nasser Attia Elhawary
  • College of Medicine
Mohamed Boustimi
  • Department of Physics
Taif Road, 21955, Mecca, Makkah Province, Saudi Arabia