Shaqra University
  • Saudi Arabia
Recent publications
Objectives: Retro walking or backward walking expends greater energy and places less stress on joints compared with forward walking at a similar speed. This study conducted in obese young men was primarily aimed at comparing the effects of backward walking with forward walking on adiponectin levels. The secondary aim was to describe the effects of concomitant factors, namely C-reactive protein, body mass index (BMI), waist to height ratio, and waist to hip ratio, on adiponectin levels in obese young men. Methods: In this randomized comparative study, 102 participants underwent either retro walking or forward walking treadmill training four times a week for 12 weeks before and after which adiponectin, C-reactive protein, BMI, waist to height ratio, and waist to hip ratio were measured. Comparison of the measured values before and after intervention and between the groups was done, and the influence of C-reactive protein, BMI, waist to height ratio, and waist to hip ratio on adiponectin levels was determined. Results: Adiponectin levels were significantly increased (p < 0.001) and C-reactive protein, BMI, waist to height ratio, and waist to hip ratio were significantly decreased (p ≤ 0.001) post-intervention. The participants who underwent retro walking training showed a significantly higher change in C-reactive protein levels, BMI, and waist to hip ratio compared to the forward walking group (p < 0.001). Adiponectin levels were influenced by BMI (p < 0.001). Conclusion: Retro walking training leads to a greater increase in adiponectin and reduction in C-reactive protein, BMI, waist to height ratio, and waist to hip ratio compared to forward walking, and adiponectin levels are influenced by BMI. Retro walking treadmill training can be preferentially used to decrease cardiovascular risk factors.
The decoration of polypyrrole (Ppy) quantum dots on graphene oxide (GO), Ppy/GO, composite is prepared through the in situ polymerization process. The chemical structure of Ppy, GO, and Ppy/GO is confirmed using XRD, XPS, and FTIR analyses. The morphologies are confirmed using SEM and TEM analyses, in which TEM confirms the formation of quantum dot Ppy with an average particle size of 5 nm decorated on GO sheets. The Ppy/GO composite has a great optical property related to the absorbance in UV, Vis, and near IR region, with a small bandgap of (1.66 eV). These properties qualify the prepared composite for application as photoelectrode for H 2 gas evolution from sewage water (third treated stage, pH 7.2). The H 2 evolution rate is represented by the elec-trochemical measurements of current density (J ph). The effect of on/off chopped light on the responsivity of the photoelectrode is mentioned, in which the J ph values increase from-4 to-12 lA cm-2 , respectively. Moreover, the J ph value changed from-4.32 to-4.89 lA cm-2 , with decreasing in the monochromatic wavelengths from 730 to 440 nm, respectively. This electrochemical testing study confirms the ability of the Ppy/GO thin film photoelectrode for H 2 gas production from wastewater.a
Introduction: Childhood abuse could potentially cause negative health consequences later in life, where they influence individuals' physiological, psychological, and behavioral health. Screening for ACEs is not widely incorporated during routine primary healthcare. The information about past childhood abuse screening among adult patients is elusive. The aim of the study was to investigate healthcare providers (HCPs) practices, skills, attitudes, and perceived barriers related to past childhood abuse screening among adult patients in Saudi Arabia. Design: Cross-sectional study. Methods: Data were collected from healthcare facilities in the Riyadh and Madinah regions of Saudi Arabia using a self-reported questionnaire. Results: A total of 126 HCPs completed the survey. Less than one-third of the HCPs reported routinely (usually or always) screening for childhood abuse. HCPs were more concerned that they would offend their patients by examining history of adversities. HCP practice location, the extent to which they think it is part of their responsibilities to screen for history of adversities, and their self-reported of adverse childhood experiences were significantly associated with screening practices for childhood abuse. Four perceived barriers were significantly associated with HCP screening. Conclusion: Screening for past adversities is vital for identifying childhood trauma among the public; therefore, we might participate in reducing childhood trauma and further controlling consequences in the future. Developing a screening form for childhood abuse or adversities and providing this form in healthcare settings are appropriate at this stage. Clinical relevance: Early screening for ACEs is recommended, which prioritizes health promotion and disease prevention. It is highly needed to increase HCP awareness toward childhood abuse, screening for it, and reflection on it.
Tuberculosis (TB), an infectious disease caused by the Mycobacterium tuberculosis (Mtb), has been responsible for the deaths of millions of individuals around the globe. A vital protein in viral pathogenesis known as resuscitation promoting factor (RpfB) has been identified as a potential therapeutic target of anti-tuberculosis drugs. This study offered an in silico process to examine possible RpfB inhibitors employing a computational drug design pipeline. In this study, a total of 1228 phytomolecules were virtually tested against the RpfB of Mtb. These phytomolecules were sourced from the NP-lib database of the MTi-OpenScreen server, and five top hits (ZINC000044404209, ZINC000059779788, ZINC000001562130, ZINC000014766825, and ZINC000043552589) were prioritized for compute intensive docking with dock score ≤ − 8.5 kcal/mole. Later, molecular dynamics (MD) simulation and principal component analysis (PCA) were used to validate these top five hits. In the list of these top five hits, the ligands ZINC000044404209, ZINC000059779788, and ZINC000043552589 showed hydrogen bond formation with the functional residue Glu²⁹² of the RpfB protein suggesting biological significance of the binding. The RMSD study showed stable protein–ligand complexes and higher conformational consistency for the ligands ZINC000014766825, and ZINC000043552589 with RMSD 3–4 Å during 100 ns MD simulation. The overall analysis performed in the study suggested promising binding of these compounds with the RpfB protein of the Mtb at its functional site, further experimental investigation is needed to validate the computational finding.
Parkinson's disease (PD) is an advanced neurodegenerative disease (NDD) caused by the degeneration of dopaminergic neurons (DNs) in the substantia nigra (SN). As PD is an age-related disorder, the majority of PD patients are associated with musculoskeletal disorders with prolonged use of analgesic and anti-inflammatory agents, such as non-steroidal anti-inflammatory drugs (NSAIDs). Therefore, NSAIDs can affect PD neuropathology in different ways. Thus, the objective of the present narrative review was to clarify the potential role of NSAIDs in PD according to the assorted view of preponderance. Inhibition of neuroinflammation and modulation of immune response by NSAIDs could be an effective way in preventing the development of NDD. NSAIDs affect PD neuropathology in different manners could be beneficial or detrimental effects. Inhibition of cyclooxygenase 2 (COX2) by NSAIDs may prevent the development of PD. NSAIDs afforded a neuroprotec-tive role against the development and progression of PD neuropathology through the modulation of neuroinflammation. Though, NSAIDs may lead to neutral or harmful effects by inhibiting neuroprotective prostacyclin (PGI2) and accentuation of pro-inflammatory leukotrienes (LTs). In conclusion, there is still a potential conflict regarding the effect of NSAIDs on PD neuropathology.
In today's digital era, the paradoxical leadership (PXL) style is in vogue due to its ability to drive employee behavior within an organization. Drawing on the leader-member exchange and social exchange theories, this study investigates the link between PXL with work engagement (ENG) and employee voice behavior (EVB) by means of perceived organizational support (POS). The current study also explores work environment (ENV) in the hotel setting as a moderator. Simple random sampling has been used to gather data from 493 employees working in five-star hotels in Sharm El-Sheikh. The partial least square (PLS)-structural equation modeling (SEM) analysis revealed that POS partially mediated the association of POS with ENG and EVB. Moreover, ENV helped strengthen the positive effects of POS on both ENG and EVB. The study concludes with beneficial contributions for researchers and practitioners in the context of hotel management.
A new layered hybrid compound, based on imidazole derivative and Zn (II), was synthesized and characterized by X-ray diffraction, infrared spectroscopy, and UV–VIS absorption spectroscopy. According to the X-ray structure, the structure was determined in the monoclinic system (C2/c space group). The packing of bis-(5-nitrobenzimidzolium) tetrachlorozincate monohydrate is characterized by an alternation of corrugated layers parallel to the (102) planes, which are composed of (C7H6N3O2)+ groups, [ZnCl4]2− anions, and water molecules. There are many H-bonds between organic molecules, inorganic groups, and water molecules. These are the main forces that hold the supramolecular structure together. Through the use of contact enrichment ratios and Hirshfeld surfaces, the intermolecular interactions of the structure were investigated. With the aid of complex impedance spectroscopy, it is possible to comprehend the electrical behavior of the synthesized substance. Molecular docking was further employed to analyze the molecular mechanism and modalities of interactions between (C7H6N3O2)2[ZnCl4]·H2O and double-stranded DNA, revealing a high degree of stability and spontaneity at the molecular level. Moreover, utilizing disk diffusion methods, the antibacterial activity of the Zn (II) complex alone and with antibiotics was studied, revealing a synergistic impact against Klebsiella pneumonia and Staphylococcus aureus.
Background: In patients with suspected pulmonary embolism (PE), the literature suggests the overuse of computerized tomography pulmonary angiography (CTPA) and underuse of clinical decision rules before imaging request. This study determined the potential for avoidable CTPA using the modified Wells score (mWS) and D-dimer assay in patients with suspected PE. Methods: This hospital-based retrospective study analyzed the clinical data of 661 consecutive patients with suspected PE who underwent CTPA in the emergency department of a tertiary hospital for the use of a clinical prediction rule (mWS) and D-dimer assay. The score was calculated retrospectively from the available data in the files of patients who did not have a documented clinical prediction rule. Overuse (avoidable) CTPA was defined as D-dimer negativity and PE unlikely for this study. Results: Of 661 patients' data examined, clinical prediction rules were documented in 15 (2.3%). In total, 422 patients (63.8%) had required information on modified Wells criteria and D-dimer assays and were included for further analysis. PE on CTPA was present in 22 (5.21%) of PE unlikely (mWS ≤4) and 1 (0.24%) of D-dimer negative patients. Thirty patients (7.11%) met the avoidable CTPA (DD negative+PE unlikely) criteria, and it was significantly associated with dyspnea. The value of sensitivity of avoidable CTPA was 100%, whereas the positive predictive value was 90.3%. Conclusion: Underutilization of clinical prediction rules before prescribing CTPA is common in emergency departments. Therefore, a mandatory policy should be implemented regarding the evaluation of avoidable CTPA imaging to reduce CTPA overuse.
Background: Substance use disorders are economically and socially devastating to families and societies. Expectations of the patients and their families during the posttreatment phase of substance use disorder need to be emphasized to maintain a patient's sobriety and prevent relapse. Aim: The aim of this study was to examine the prediction power of personal and sociodemographic factors of patients and their families to treatment outcomes. Methods: A descriptive, correlational, cross-sectional design was used. Data were collected from 80 patients treated for substance use disorders from a major psychiatric hospital and their family members regarding social, health, and psychological expectations. A three-step multiple hierarchical regression analysis was used to predict the power of personal and sociodemographic characteristics of patients' and their families' expectations to treatment outcomes. Results: Heroin use, codeine use, family education, and family support were significant predictors of rehabilitation expectation (p < .05). Level of education and heroin use were predictors for lower levels of rehabilitation expectation, whereas family support and codeine use were indicators for higher scores of rehabilitation expectation. Conclusion: Emphasis should be given to expectations of the patient and their family through appropriate psychoeducation and enhanced understanding and partnership.
Recent research has focused on photovoltaic (PV) systems due to their important properties. The efficiency of the PV system can be enhanced by many Maximum Power Point Tracking (MPPT) algorithms proposals. MPPT algorithms are used to achieve maximum PV output power by optimizing the duty cycle of the DC–DC buck/boost converter. This paper introduces an RNA algorithm as an efficient MPPT algorithm for the photovoltaic system. This proposed RNA algorithm consists of two main segments. The first segment is an artificial neural network for generating reference power. The second segment is a proposed Recursive Bit Assignment (RBA) network to allow variable step size of the boost converter duty cycle. The instant PV power adopts the RBA network to produce the variable duty cycle increment value. Additionally, the neural network is implemented in such a way to obtain the best performance. Many simulation results using MATLAB to test the system performance are presented. The performance characteristics of the photovoltaic system with variable irradiance and variable temperature are simulated. From results, the proposed RNA algorithm achieves fast tracking time, high energy efficiency, true maximum power point and acceptable ripple. Additionally, comparisons between the RNA algorithm and other related algorithms such as Perturb and Observe, the Neural Network and the Adaptive Neural Inference System Algorithms are executed. The proposed RNA algorithm achieves the best performance in all case studies such as; irradiance profile variation, severe temperature and irradiance diversions, and partial shading conditions. Besides, the experimental circuit of the PV system is also presented.
Breast cancer in women is the most frequently diagnosed and major leading cause of cancer deaths. Due to the complex nature of microcalcification and masses, radiologists fail to diagnose breast cancer properly. In this research paper, we have employed a novel Deep Convolutional Neural Network (DCNN) model using a transfer learning strategy and compared the results with Machine Learning (ML) techniques such as Support vector machine (SVM) kernels and Decision Trees based on different features extracting strategies to distinguish cancer mammograms from normal subjects. In this study, we first extracted the hand-crafted features such as as texture, morphological, entropy-based, scale-invariant feature transform (SIFT), and elliptic Fourier descriptors (EFDs) and fed into machine learning algorithm for classification. We then utilized the deep learning algorithms with transfer learning approach. The deep learning models yielded the highest detection performance with default and optimized parameters i.e. GoogleNet yielded accuracy (99.26%), AUC (0.9998) with default parameters and AlexNet yielded accuracy (99.26%), AUC (0.9996) with optimized parameters. The results reveal that proposed approach is more robust for early detection of breast mammograms which can be best utilized for improved diagnosis and prognosis.
One of humanity’s most devastating health crises was COVID-19. Billions of people suffered during this pandemic. In comparison with previous global pandemics that have been faced by the world before, societies were more accurate with the technical support system during this natural disaster. The intersection of data from healthcare units and the analysis of this data into various sophisticated systems were critical factors. Different healthcare units have taken special consideration to advance technical inputs to fight against such situations. The field of natural language processing (NLP) has dramatically supported this. Despite the primitive methods for monitoring the bio-metric factors of a person, the use of cognitive science has emerged as one of the most critical features during this pandemic era. One of the essential features is the potential to understand the data based on various texts and user inputs. The deployment of various NLP systems is one of the most challenging factors in handling the bulk amount of data flowing from multiple sources. This study focused on developing a powerful application to advise patients suffering from ailments related to COVID-19. The use of NLP refers to facilitating a user to identify the present critical situation and make necessary decisions while getting infected. This article also summarises the challenges associated with NLP and its usage for future NLP-based applications focusing on healthcare units. There are a couple of applications that reside for android-based systems as well as web-based chat-bot systems. In terms of security and safety, application development for iOS is more advanced. This study also explains the block meant of an application for advising COVID-19 infection. A natural language processing powered application for an iOS operating system is indeed one of its kind, which will help people who need to advise proper guidance. The article also portrays NLP-based application development for healthcare problems associated with personal reporting systems.
Polypyrrole (Ppy) and Ppy/Ni(OH)2‐NiO nanocomposite are prepared through the in situ polymerization oxidation reaction (on glass), in which the composite is formed through the oxidation of NiSO4. The prepared materials were characterized to reveal their morphology, chemical structure, and optical properties. The Ppy and Ppy/Ni(OH)2‐NiO have average particle sizes of 160 and 250 nm, respectively. Moreover, the bandgap values are 1.70 and 1.44 eV, respectively. Through the composite, the polymer acts as a shell (40 nm), while the Ni(OH)2‐NiO is the core. Analysis of the Ppy illustrates that the crystalline nature of the polymer increases after the composite formation, in which the pure Ppy has no sharp peaks, while the characteristic two peaks are formed after the composite formation. Moreover, a hexagonal Ni(OH)2 is confirmed inside the composite. The applications of the glass/Ppy and glass/Ppy/Ni(OH)2‐NiO are carried out as optoelectronic devices, in which the testing is carried out under light/dark and monochromatic wavelengths light, the Jph values are 0.12 and 0.85 mA·cm−2, respectively. The photoresponsivity (R) for the composite optoelectronic is 12.5 mA·W−1 while the detectivity (D) is 2.79*1011 Jones.
Machine learning is being used by researchers and computer scientists to develop a new method for predicting rainfall. Due to the non-linear relationship between input data and output conditions, rainfall prediction is hard, so deep neural network (DNN) models substitute for costly, complex systems. Deep neural network-based weather forecasting models can be designed quickly and cheaply to predict rainfall. On the other hand, water levels depend on rainfall. Unpredictable rainfall due to climate change might cause floods or droughts. Many individuals, especially farmers, rely on rain forecasts. In our study, we focus on the area of marshes in southern Iraq, some of the most famous landmarks in the area (and the world), where the Shatt al-Arab flows into the Arabic Gulf and the Tigris and Euphrates rivers developed within the Mesopotamian plain to create a natural balance. Since the beginning of the 1980s, the wetlands, sometimes known as "the marshes," have experienced droughts. And by the late 1990s, a sizable portion of the marshes had dried up, leaving the arid and salty Sabkha lands void of life, particularly lands with vast bodies of water and high levels of human activity. Moreover, the corresponding regions have undergone visible hydrological and climatic changes. In this study focuses on the marshes of southern Iraq and aims to develop a rainfall forecasting model. We propose a novel approach based on optimized LSTM and hybrid deep learning algorithms to improve the forecasting of average monthly rainfall. To evaluate the efficiency of the predictions, a comparison of the predicted rainfall and the actual recorded rainfall is made, and the performance and accuracy of the models are examined. The hybrid convolutional stacked bidirectional long-short term memory (CNN-BDLSTMs) outperformed the other models.
Oral cancer is a deadly form of cancerous tumor that is widely spread in low and middle-income countries. An early and affordable oral cancer diagnosis might be achieved by automating the detection of precancerous and malignant lesions in the mouth. There are many research attempts to develop a robust machine-learning model that can detect oral cancer from images. However, these are still lacking high precision in oral cancer detection. Therefore, this work aims to propose a new approach capable of detecting oral cancer in medical images with higher accuracy. In this work, a novel and robust oral cancer detection based on a convolutional neural network (CNN) and optimized deep belief network (DBN). The design parameters of CNN and DBN are optimized using a new optimization algorithm, which is developed as a hybrid of Particle Swarm Optimization (PSO) and Al-Biruni Earth Radius (BER) Optimization algorithms and is denoted by (PSOBER). Using a standard biomedical images dataset available on the Kaggle repository, the proposed approach shows promising results outperforming various competing approaches with an accuracy of 97.35%. In addition, a set of statistical tests, such as One-way analysis-of-variance (ANOVA) and Wilcoxon signed-rank tests, are conducted to prove the significance and stability of the proposed approach. The proposed methodology is solid and efficient, and specialists can adopt it. However, additional research on a larger scale dataset is required to confirm the findings and highlight other oral features that can be utilized for cancer detection.
In this paper, we denote the Lie algebra of smooth vector fields on RP1 by V(RP1). This paper focuses on two parts. In the beginning, we determine the cohomology space of aff(1) with coefficients in Dτ,λ,μ;ν. Afterward, we classify aff(1)-invariant fourth-linear differential operators from V(RP1) to Dτ,λ,μ;ν vanishing on aff(1). This result enables us to compute the aff(1)-relative cohomology of V(RP1) with coefficients in Dτ,λ,μ;ν.
Modular multilevel inverters (MMIs) for medium-voltage (MV) grid-connected systems are gaining attention in solar photovoltaic power (PV) applications. Existing MV power electronic converters require large passive components, huge line-frequency step-up transformers, and additional conversion power stages for maximum power extraction. This paper presents a new three-phase modular inverter (TPMI) based on a novel dual-isolated SEPIC/CUK (DISC) converter for large-scale PV (LSPV) plants. The proposed TPMI is synthesized from series DISC submodules (SMs) to reduce the size and improve the performance of the energy conversion system. Employing high-frequency transformers (HFTs) into the SMs can provide the required galvanic isolation and voltage boosting in addition to reducing the size compared with line-frequency step-up transformers. The chosen DISC converter reduces the required filtering capacitances thanks to its operation as a current-source converter, resulting in improved lifetime, scalability, and resilience of the inverter. The state-space model of the DISC is presented and its performance in PV grid-tied systems using simulations is evaluated. To validate the mathematical analyses and computer simulations, a small-scale experimental prototype is built and tested.
A better quality radiographic image helps the radiologist to make a proper diagnosis of the disease. In general, the use of more radiation provides a better quality image, but it gives the patient a higher radiation dose, which shows the need for optimization of imaging conditions to minimize the risk to patients from excessive radiation exposure. In this study, the chest X-ray exposure factors for 178 patients with different body mass index (BMI) values have been analyzed using the Python Machine Learning algorithm. Patient data were collected from the King Abdullah bin Abdulaziz University Hospital, Saudi Arabia. The role of BMI in the selection of radiation exposure factors (kVp, mAs) was evaluated. It has been found that the BMI of each patient has specific exposure factors, and that if it gets higher than the specific value it could harm the patient's health. The obtained results provide detailed information about the relation between BMI and optimal chest X-ray exposure factors without affecting the quality of the X-ray image.
The synthetic food additive dye induces amyloid fibrillation has many implications in the laboratory and industries. The effect of Allura red (AR), on the fibrillation of ovalbumin (Ova) at pH 2.0 was investigated. The influence of salt and pH was also seen on AR-induced Ova aggregation. We have used several spectroscopic and microscopy techniques to characterize the changes. The turbidity data suggest that concentrations above 0.05 mM of AR induce aggregation, and the size of aggregates increased in response to AR concentration. The kinetics data showed that the AR induces Ova aggregation quickly without lag time. The aggregates induced by AR have amyloid-like aggregates confirmed by far-UV CD and TEM. NaCl has very marginal effects in AR-induced aggregation. The turbidity results clearly state that Ova is not forming aggregates with pH above 4.0 due to electrostatic repulsion. However, Ova forms bigger aggregates in the presence of 0.5 mM AR at a pH below 4.0. These spectroscopic data suggest that the amyloid fibrillation that occurs in Ova is due to electrostatic and hydrophobic interaction. The amyloid fibrillation induced by AR dye in protein should be taken seriously for food safety purposes.
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838 members
Dr. Gulam Mustafa
  • College of Pharmacy
R. Palanivel
  • Mechanical Engineering
Moêz Smiri
  • Biology Department
Mohamed A. El Hamd
  • Pharmaceutical Sciences
Saudi Arabia