Displaying publications 1 - 20 of 289 in total

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  1. Monte Carlo simulation of gold nanoparticles for X-ray enhancement application
    Dheyab MA, Aziz AA, Rahman AA, Ashour NI, Musa AS, Braim FS, et al.
    Biochim Biophys Acta Gen Subj, 2023 Apr;1867(4):130318.
    PMID: 36740000 DOI: 10.1016/j.bbagen.2023.130318
    BACKGROUND: Gold nanoparticles (Au NPs) are regarded as potential agents that enhance the radiosensitivity of tumor cells for theranostic applications. To elucidate the biological mechanisms of radiation dose enhancement effects of Au NPs as well as DNA damage attributable to the inclusion of Au NPs, Monte Carlo (MC) simulations have been deployed in a number of studies.

    SCOPE OF REVIEW: This review paper concisely collates and reviews the information reported in the simulation research in terms of MC simulation of radiosensitization and dose enhancement effects caused by the inclusion of Au NPs in tumor cells, simulation mechanisms, benefits and limitations.

    MAJOR CONCLUSIONS: In this review, we first explore the recent advances in MC simulation on Au NPs radiosensitization. The MC methods, physical dose enhancement and enhanced chemical and biological effects is discussed, followed by some results regarding the prediction of dose enhancement. We then review Multi-scale MC simulations of Au NP-induced DNA damages for X-ray irradiation. Moreover, we explain and look at Multi-scale MC simulations of Au NP-induced DNA damages for X-ray irradiation.

    GENERAL SIGNIFICANCE: Using advanced chemical module-implemented MC simulations, there is a need to assess the radiation-induced chemical radicals that contribute to the dose-enhancing and biological effects of multiple Au NPs.

    Matched MeSH terms: X-Rays
  2. Patient's effective dose and performance assessment of computed radiography systems
    Bushra A, Sulieman A, Edam A, Tamam N, Babikir E, Alrihaima N, et al.
    Appl Radiat Isot, 2023 Mar;193:110627.
    PMID: 36584412 DOI: 10.1016/j.apradiso.2022.110627
    Computed tomography is widely used for planar imaging. Previous studies showed that CR systems involve higher patient radiation doses compared to digital systems. Therefore, assessing the patient's dose and CR system performance is necessary to ensure that patients received minimal dose with the highest possible image quality. The study was performed at three medical diagnostic centers in Sudan: Medical Corps Hospital (MCH), Advance Diagnostic Center (ADC), and Advance Medical Center (AMC). The following tools were used in this study: Tape measure, Adhesive tape, 1.5 mm copper filtration (>10 × 10 cm), TO 20 threshold contrast test object, Resolution test object (e.g., Huttner 18), MI geometry test object or lead ruler, Contact mish, Piranha (semiconductor detector), Small lead or copper block (∼5 × 5 cm), and Steel ruler, to do a different type of tests (Dark Noise, Erasure cycle efficiency, Sensitivity Index calibration, Sensitivity Index consistency, Uniformity, Scaling errors, Blurring, Limiting spatial Resolution, Threshold, and Laser beam Function. Entrance surface air kerma (ESAK (mGy) was calculated from patient exposure parameters using DosCal software for three imaging modalities. A total of 199 patients were examined (112 chest X rays, 77 lumbar spine). The mean and standard deviation (sd) for patients ESAK (mGy) were 2.56 ± 0.1 mGy and 1.6 mGy for the Anteroposterior (AP) and lateral projections for the lumbar spine, respectively. The mean and sd for the patient's chest doses were 0.1 ± 0.01 for the chest X-ray procedures. The three medical diagnostic centers' CR system performance was evaluated and found that all of the three centers have good CR system functions. All the centers satisfy all the criteria of acceptable visual tests. CR's image quality and sensitivity were evaluated, and the CR image is good because it has good contrast and resolution. All the CR system available in the medical centers and upgraded from old X-ray systems to new systems, has been found to work well. The patient's doses were comparable for the chest X-ray procedures, while patients' doses from the lumbar spine showed variation up to 2 folds due to the variation in patients' weight and X-ray machine setting. Patients dose optimization is recommended to ensure the patients received a minimal dose while obtaining the diagnostic findings.
    Matched MeSH terms: X-Rays
  3. Fulltext AI-based diagnosis of COVID-19 patients using X-ray scans with stochastic ensemble of CNNs
    Arora R, Bansal V, Buckchash H, Kumar R, Sahayasheela VJ, Narayanan N, et al.
    Phys Eng Sci Med, 2021 Dec;44(4):1257-1271.
    PMID: 34609703 DOI: 10.1007/s13246-021-01060-9
    According to the World Health Organization (WHO), novel coronavirus (COVID-19) is an infectious disease and has a significant social and economic impact. The main challenge in fighting against this disease is its scale. Due to the outbreak, medical facilities are under pressure due to case numbers. A quick diagnosis system is required to address these challenges. To this end, a stochastic deep learning model is proposed. The main idea is to constrain the deep-representations over a Gaussian prior to reinforce the discriminability in feature space. The model can work on chest X-ray or CT-scan images. It provides a fast diagnosis of COVID-19 and can scale seamlessly. The work presents a comprehensive evaluation of previously proposed approaches for X-ray based disease diagnosis. The approach works by learning a latent space over X-ray image distribution from the ensemble of state-of-the-art convolutional-nets, and then linearly regressing the predictions from an ensemble of classifiers which take the latent vector as input. We experimented with publicly available datasets having three classes: COVID-19, normal and pneumonia yielding an overall accuracy and AUC of 0.91 and 0.97, respectively. Moreover, for robust evaluation, experiments were performed on a large chest X-ray dataset to classify among Atelectasis, Effusion, Infiltration, Nodule, and Pneumonia classes. The results demonstrate that the proposed model has better understanding of the X-ray images which make the network more generic to be later used with other domains of medical image analysis.
    Matched MeSH terms: X-Rays
  4. Fulltext Deep Mining Generation of Lung Cancer Malignancy Models from Chest X-ray Images
    Horry M, Chakraborty S, Pradhan B, Paul M, Gomes D, Ul-Haq A, et al.
    Sensors (Basel), 2021 Oct 07;21(19).
    PMID: 34640976 DOI: 10.3390/s21196655
    Lung cancer is the leading cause of cancer death and morbidity worldwide. Many studies have shown machine learning models to be effective in detecting lung nodules from chest X-ray images. However, these techniques have yet to be embraced by the medical community due to several practical, ethical, and regulatory constraints stemming from the "black-box" nature of deep learning models. Additionally, most lung nodules visible on chest X-rays are benign; therefore, the narrow task of computer vision-based lung nodule detection cannot be equated to automated lung cancer detection. Addressing both concerns, this study introduces a novel hybrid deep learning and decision tree-based computer vision model, which presents lung cancer malignancy predictions as interpretable decision trees. The deep learning component of this process is trained using a large publicly available dataset on pathological biomarkers associated with lung cancer. These models are then used to inference biomarker scores for chest X-ray images from two independent data sets, for which malignancy metadata is available. Next, multi-variate predictive models were mined by fitting shallow decision trees to the malignancy stratified datasets and interrogating a range of metrics to determine the best model. The best decision tree model achieved sensitivity and specificity of 86.7% and 80.0%, respectively, with a positive predictive value of 92.9%. Decision trees mined using this method may be considered as a starting point for refinement into clinically useful multi-variate lung cancer malignancy models for implementation as a workflow augmentation tool to improve the efficiency of human radiologists.
    Matched MeSH terms: X-Rays
  5. Discrimination of dopamine by an electrode modified with negatively charged manganese dioxide nanoparticles decorated on a poly(3,4 ethylenedioxythiophene)/reduced graphene oxide composite
    Promsuwan K, Soleh A, Saisahas K, Saichanapan J, Kanatharana P, Thavarungkul P, et al.
    J Colloid Interface Sci, 2021 Sep;597:314-324.
    PMID: 33872888 DOI: 10.1016/j.jcis.2021.03.162
    A unique nanocomposite was fabricated using negatively charged manganese dioxide nanoparticles, poly (3,4-ethylenedioxythiophene) and reduced graphene oxide (MnO2/PEDOT/rGO). The nanocomposite was deposited on a glassy carbon electrode (GCE) functionalized with amino groups. The modified GCE was used to electrochemically detect dopamine (DA). The surface morphology, charge effect and electrochemical behaviours of the modified GCE were characterized by scanning electron microscopy, energy dispersive X-ray analysis (EDX), cyclic voltammetry and electrochemical impedance spectroscopy, respectively. The MnO2/PEDOT/rGO/GCE exhibited excellent performance towards DA sensing with a linear range between 0.05 and 135 µM with a lowest detection limit of 30 nM (S/N = 3). Selectivity towards DA was high in the presence of high concentrations of the typical interferences ascorbic acid and uric acid. The stability and reproducibility of the electrode were good. The sensor accurately determined DA in human serum. The synergic effect of the multiple components of the fabricated nanocomposite were critical to the good DA sensing performance. rGO provided a conductive backbone, PEDOT directed the uniform growth of MnO2 and adsorbed DA via pi-pi and electrostatic interaction, while the negatively charged MnO2 provided adsorption and catalytic sites for protonated DA. This work produced a promising biosensor that sensitively and selectively detected DA.
    Matched MeSH terms: X-Rays
  6. Fulltext Automatic COVID-19 Detection Using Exemplar Hybrid Deep Features with X-ray Images
    Barua PD, Muhammad Gowdh NF, Rahmat K, Ramli N, Ng WL, Chan WY, et al.
    Int J Environ Res Public Health, 2021 07 29;18(15).
    PMID: 34360343 DOI: 10.3390/ijerph18158052
    COVID-19 and pneumonia detection using medical images is a topic of immense interest in medical and healthcare research. Various advanced medical imaging and machine learning techniques have been presented to detect these respiratory disorders accurately. In this work, we have proposed a novel COVID-19 detection system using an exemplar and hybrid fused deep feature generator with X-ray images. The proposed Exemplar COVID-19FclNet9 comprises three basic steps: exemplar deep feature generation, iterative feature selection and classification. The novelty of this work is the feature extraction using three pre-trained convolutional neural networks (CNNs) in the presented feature extraction phase. The common aspects of these pre-trained CNNs are that they have three fully connected layers, and these networks are AlexNet, VGG16 and VGG19. The fully connected layer of these networks is used to generate deep features using an exemplar structure, and a nine-feature generation method is obtained. The loss values of these feature extractors are computed, and the best three extractors are selected. The features of the top three fully connected features are merged. An iterative selector is used to select the most informative features. The chosen features are classified using a support vector machine (SVM) classifier. The proposed COVID-19FclNet9 applied nine deep feature extraction methods by using three deep networks together. The most appropriate deep feature generation model selection and iterative feature selection have been employed to utilise their advantages together. By using these techniques, the image classification ability of the used three deep networks has been improved. The presented model is developed using four X-ray image corpora (DB1, DB2, DB3 and DB4) with two, three and four classes. The proposed Exemplar COVID-19FclNet9 achieved a classification accuracy of 97.60%, 89.96%, 98.84% and 99.64% using the SVM classifier with 10-fold cross-validation for four datasets, respectively. Our developed Exemplar COVID-19FclNet9 model has achieved high classification accuracy for all four databases and may be deployed for clinical application.
    Matched MeSH terms: X-Rays
  7. Biocompatible chitin-encapsulated CdS quantum dots: Fabrication and antibacterial screening
    Lim MJ, Shahri NNM, Taha H, Mahadi AH, Kusrini E, Lim JW, et al.
    Carbohydr Polym, 2021 May 15;260:117806.
    PMID: 33712152 DOI: 10.1016/j.carbpol.2021.117806
    Chitin-encapsulated cadmium sulfide quantum dots (CdS@CTN QDs) were successfully synthesized from chitin and Cd(NO3)2 precursor using the colloidal chemistry method, toward the development of biocompatible and biodegradable QDs for biomedical applications. CdS@CTN QDs exhibited the nanocrystalline cubic CdS encapsulated by α-chitin. The average particle size of CdS@CTN QDs was estimated using empirical Henglein model to be 3.9 nm, while their crystallite size was predicted using Scherrer equation to be 4.3 nm, slightly larger compared to 3-mercaptopropionic acid-capped CdS QDs (3.2 and 3.6 nm, respectively). The mechanism of formation was interpreted based on the spectroscopic data and X-ray crystal structures of CdS@CTN QDs fabricated at different pH values and mass ratios of chitin to Cd(NO3)2 precursor. As an important step to explore potential biomolecular and biological applications of CdS@CTN QDs, their antibacterial activities were tested against four different bacterial strains; i.e. Escherichia coli, Bacillus subtillus, Staphylococcus aureus and Pseudomonas aeruginosa.
    Matched MeSH terms: X-Rays
  8. Probing n-Octyl α-d-Glycosides Using Deuterated Water in the Lyotropic Phase by Deuterium NMR
    Wan Iskandar WFN, Salim M, Patrick M, Timimi BA, Zahid NI, Hashim R
    J Phys Chem B, 2021 05 06;125(17):4393-4408.
    PMID: 33885309 DOI: 10.1021/acs.jpcb.0c10629
    The lyotropic phase behavior of four common and easily accessible glycosides, n-octyl α-d-glycosides, namely, α-Glc-OC8, α-Man-OC8, α-Gal-OC8, and α-Xyl-OC8, was investigated. The presence of normal hexagonal (HI), bicontinuous cubic (VI), and lamellar (Lα) phases in α-Glc-OC8 and α-Man-OC8 including their phase diagrams in water reported previously was verified by deuterium nuclear magnetic resonance (2H NMR), via monitoring the D2O spectra. Additionally, the partial binary phase diagrams and the liquid crystal structures formed by α-Gal-OC8 and α-Xyl-OC8 in D2O were constructed and confirmed using small- and wide-angle X-ray scattering and 2H NMR. The average number of bound water molecules (nb) per headgroup in the Lα phase was determined by the systematic measurement of the quadrupolar splitting of D2O over a wide range of molar ratio values (glycoside/D2O), especially at high glucoside composition. The number of bound water molecules bound to the headgroup was found to be around 1.5-2.0 for glucoside, mannoside, and galactoside, all of which possesses four OH groups. In the case of xyloside, which has only three OH groups, the bound water content is ∼2.0. Our findings confirmed that the bound water content of all n-octyl α-d-glycosides studied is lower compared to the number of possible hydrogen bonding sites possibly due to the fact that most of the OH groups are involved in intralayer interaction that holds the lipid assembly together.
    Matched MeSH terms: X-Rays
  9. Fulltext Advanced Nanoscale Surface Characterization of CuO Nanoflowers for Significant Enhancement of Catalytic Properties
    Khan MA, Nayan N, Shadiullah, Ahmad MK, Fhong SC, Tahir M, et al.
    Molecules, 2021 May 04;26(9).
    PMID: 34064537 DOI: 10.3390/molecules26092700
    In this work, advanced nanoscale surface characterization of CuO Nanoflowers synthesized by controlled hydrothermal approach for significant enhancement of catalytic properties has been investigated. The CuO nanoflower samples were characterized by field-emission scanning electron microscopy (FE-SEM), X-ray powder diffraction (XRD), X-ray photoelectron spectroscopy (XPS), Raman spectroscopy, high-resolution transmission electron microscopy (HR-TEM), selected-area electron diffraction (SAED), high-angular annular dark field scanning transmission electron microscopy (HAADF-STEM) with elemental mapping, energy dispersive spectroscopy (STEM-EDS) and UV-Vis spectroscopy techniques. The nanoscale analysis of the surface study of monodispersed individual CuO nanoflower confirmed the fine crystalline shaped morphology composed of ultrathin leaves, monoclinic structure and purified phase. The result of HR-TEM shows that the length of one ultrathin leaf of copper oxide nanoflower is about ~650-700 nm, base is about ~300.77 ± 30 nm and the average thickness of the tip of individual ultrathin leaf of copper oxide nanoflower is about ~10 ± 2 nm. Enhanced absorption of visible light ~850 nm and larger value of band gap energy (1.68 eV) have further supported that the as-grown material (CuO nanoflowers) is an active and well-designed surface morphology at the nanoscale level. Furthermore, significant enhancement of catalytic properties of copper oxide nanoflowers in the presence of H2O2 for the degradation of methylene blue (MB) with efficiency ~96.7% after 170 min was obtained. The results showed that the superb catalytic performance of well-fabricated CuO nanoflowers can open a new way for substantial applications of dye removal from wastewater and environment fields.
    Matched MeSH terms: X-Rays
  10. Fulltext Pinch-off syndrome from a chemoport catheter successfully managed with endovascular retrieval
    Chuah JS, Wong WL, Bakin S, Lim RZM, Lee EP, Tan JH
    Ann Med Surg (Lond), 2021 May;65:102294.
    PMID: 33948169 DOI: 10.1016/j.amsu.2021.102294
    Introduction and importance: A totally implantable venous access device (TIVAD), also referred to as 'chemoport', is frequently used for oncology patients. Chemoport insertion via the subclavian vein access may compress the catheter between the first rib and the clavicle, resulting in pinch-off syndrome (POS). The sequela includes catheter transection and subsequent embolization. It is a rare complication with incidence reported to be 1.1-5.0% and can lead to a devastating outcomes.

    Case presentation: 50-year-old male had his chemoport inserted for adjuvant chemotherapy 3 years ago. During the removal, remaining half of the distal catheter was not found. There was no difficulties during the removal. Chest xray revealed that the fractured catheter had embolized to the right ventricle. Further history taking, he did experienced occasional palpitation and chest discomfort for the past six months. Electrocardiogram and cardiac enzymes were normal. Urgent removal of the fractured catheter via the percutaneous endovascular approach, under fluoroscopic guidance by an experience interventional radiologist was done. The procedure was successful without any complication. Patient made an uneventful recovery. He was discharged the following day, and was well during his 3rd month follow up.

    Conclusion: Early detection and preventive measures can be done to prevent pinch-off syndrome. Unrecognized POS can result in fatal complications such as cardiac arrhythmia and septic embolization. Retrieval via the percutaneous endovascular approach provide excellent outcome in the case of embolized fractured catheter.

    Matched MeSH terms: X-Rays
  11. Fulltext Synthesis and cytotoxic activity of organotin(IV) diallyldithiocarbamate compounds as anticancer agent towards colon adenocarcinoma cells (HT-29)
    Haezam FN, Awang N, Kamaludin NF, Mohamad R
    Saudi J Biol Sci, 2021 May;28(5):3160-3168.
    PMID: 34025187 DOI: 10.1016/j.sjbs.2021.02.060
    Context: Diphenyltin(IV) diallyldithiocarbamate compound (Compound 1) and triphenyltin(IV) diallyldithiocarbamate compound (Compound 2) are two newly synthesised compounds of organotin(IV) with diallyldithiocarbamate ligands.

    Objective: To assess the cytotoxic effects of two synthesised compounds against HT-29 human colon adenocarcinoma cells and human CCD-18Co normal colon cells.

    Materials and methods: Two successfully synthesised compounds were characterised using elemental (carbon, hydrogen, nitrogen, and sulphur) analysis, Fourier-Transform Infrared (FTIR), and 1H, 13C 119Sn Nucleus Magnetic Resonance (NMR) spectroscopies. The single-crystal structure of both compounds was determined by X-ray single-crystal analysis. The cytotoxicity of the compounds was assessed using 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazholium bromide (MTT) assay upon 24 h of treatment. While the mode of cell death was determined based on the externalisation of phosphatidylserine using a flow cytometer.

    Results: The elemental analysis data of the two compounds showed an agreement with the suggested formula of (C6H5)2Sn[S2CN(C3H5)2]2 for Compound 1 and (C6H5)3Sn[S2CN(C3H5)2] for Compound 2. The two major peaks of infrared absorbance, i.e., ν(C = N) and ν(C = S) were detected at the range of 1475-1479 cm-1 and 972-977 cm-1, respectively. The chemical shift of carbon in NCS2 group for Compound 1 and 2 were found at 200.82 and 197.79 ppm. The crystal structure of Compound 1 showed that it is six coordinated and crystallised in monoclinic, P21/c space group. While the crystal structure of Compound 2 is five coordinated and crystallised in monoclinic, P21/c space group. The cytotoxicity (IC50) of the two compounds against HT-29 cell were 2.36 μM and 0.39 μM. Meanwhile, the percentage of cell death modes between 60% and 75% for compound 1 and compound 2 were mainly due to apoptosis, suggesting that both compounds induced growth arrest.

    Conclusion: Our study concluded that the synthesised compounds showed potent cytotoxicity towards HT-29 cell, with the triphenyltin(IV) compound showing the highest effect compared to diphenyltin(IV).

    Matched MeSH terms: X-Rays
  12. Fulltext Exploring the effect of image enhancement techniques on COVID-19 detection using chest X-ray images
    Rahman T, Khandakar A, Qiblawey Y, Tahir A, Kiranyaz S, Abul Kashem SB, et al.
    Comput Biol Med, 2021 May;132:104319.
    PMID: 33799220 DOI: 10.1016/j.compbiomed.2021.104319
    Computer-aided diagnosis for the reliable and fast detection of coronavirus disease (COVID-19) has become a necessity to prevent the spread of the virus during the pandemic to ease the burden on the healthcare system. Chest X-ray (CXR) imaging has several advantages over other imaging and detection techniques. Numerous works have been reported on COVID-19 detection from a smaller set of original X-ray images. However, the effect of image enhancement and lung segmentation of a large dataset in COVID-19 detection was not reported in the literature. We have compiled a large X-ray dataset (COVQU) consisting of 18,479 CXR images with 8851 normal, 6012 non-COVID lung infections, and 3616 COVID-19 CXR images and their corresponding ground truth lung masks. To the best of our knowledge, this is the largest public COVID positive database and the lung masks. Five different image enhancement techniques: histogram equalization (HE), contrast limited adaptive histogram equalization (CLAHE), image complement, gamma correction, and balance contrast enhancement technique (BCET) were used to investigate the effect of image enhancement techniques on COVID-19 detection. A novel U-Net model was proposed and compared with the standard U-Net model for lung segmentation. Six different pre-trained Convolutional Neural Networks (CNNs) (ResNet18, ResNet50, ResNet101, InceptionV3, DenseNet201, and ChexNet) and a shallow CNN model were investigated on the plain and segmented lung CXR images. The novel U-Net model showed an accuracy, Intersection over Union (IoU), and Dice coefficient of 98.63%, 94.3%, and 96.94%, respectively for lung segmentation. The gamma correction-based enhancement technique outperforms other techniques in detecting COVID-19 from the plain and the segmented lung CXR images. Classification performance from plain CXR images is slightly better than the segmented lung CXR images; however, the reliability of network performance is significantly improved for the segmented lung images, which was observed using the visualization technique. The accuracy, precision, sensitivity, F1-score, and specificity were 95.11%, 94.55%, 94.56%, 94.53%, and 95.59% respectively for the segmented lung images. The proposed approach with very reliable and comparable performance will boost the fast and robust COVID-19 detection using chest X-ray images.
    Matched MeSH terms: X-Rays
  13. Fulltext Medical image-based detection of COVID-19 using Deep Convolution Neural Networks
    Gaur L, Bhatia U, Jhanjhi NZ, Muhammad G, Masud M
    Multimed Syst, 2021 Apr 28.
    PMID: 33935377 DOI: 10.1007/s00530-021-00794-6
    The demand for automatic detection of Novel Coronavirus or COVID-19 is increasing across the globe. The exponential rise in cases burdens healthcare facilities, and a vast amount of multimedia healthcare data is being explored to find a solution. This study presents a practical solution to detect COVID-19 from chest X-rays while distinguishing those from normal and impacted by Viral Pneumonia via Deep Convolution Neural Networks (CNN). In this study, three pre-trained CNN models (EfficientNetB0, VGG16, and InceptionV3) are evaluated through transfer learning. The rationale for selecting these specific models is their balance of accuracy and efficiency with fewer parameters suitable for mobile applications. The dataset used for the study is publicly available and compiled from different sources. This study uses deep learning techniques and performance metrics (accuracy, recall, specificity, precision, and F1 scores). The results show that the proposed approach produced a high-quality model, with an overall accuracy of 92.93%, COVID-19, a sensitivity of 94.79%. The work indicates a definite possibility to implement computer vision design to enable effective detection and screening measures.
    Matched MeSH terms: X-Rays
  14. Fulltext Influence of Irradiation Time on the Structural and Optical Characteristics of CuSe Nanoparticles Synthesized via Microwave-Assisted Technique
    Shitu IG, Liew JYC, Talib ZA, Baqiah H, Awang Kechik MM, Ahmad Kamarudin M, et al.
    ACS Omega, 2021 Apr 27;6(16):10698-10708.
    PMID: 34056223 DOI: 10.1021/acsomega.1c00148
    A rapid, sustainable, and ecologically sound approach is urgently needed for the production of semiconductor nanomaterials. CuSe nanoparticles (NPs) were synthesized via a microwave-assisted technique using CuCl2·2H2O and Na2SeO3 as the starting materials. The role of the irradiation time was considered as the primary concern to regulate the size and possibly the shape of the synthesized nanoparticles. A range of characterization techniques was used to elucidate the structural and optical properties of the fabricated nanoparticles, which included X-ray diffraction, energy-dispersive X-ray spectroscopy (EDX), atomic force microscopy, field emission scanning electron microscopy, Raman spectroscopy (Raman), UV-Visible diffuse reflectance spectroscopy (DRS), and photoluminescence spectroscopy (PL). The mean crystallite size of the CuSe hexagonal (Klockmannite) crystal structure increased from 21.35 to 99.85 nm with the increase in irradiation time. At the same time, the microstrain and dislocation density decreased from 7.90 × 10-4 to 1.560 × 10-4 and 4.68 × 10-2 to 1.00 × 10-2 nm-2, respectively. Three Raman vibrational bands attributed to CuSe NPs have been identified in the Raman spectrum. Irradiation time was also seen to play a critical role in the NP optical band gap during the synthesis. The decrease in the optical band gap from 1.85 to 1.60 eV is attributed to the increase in the crystallite size when the irradiation time was increased. At 400 nm excitation wavelength, a strong orange emission centered at 610 nm was observed from the PL measurement. The PL intensity is found to increase with an increase in irradiation time, which is attributed to the improvement in crystallinity at higher irradiation time. Therefore, the results obtained in this study could be of great benefit in the field of photonics, solar cells, and optoelectronic applications.
    Matched MeSH terms: X-Rays
  15. Fulltext Influence of Sintering Temperature of Kaolin, Slag, and Fly Ash Geopolymers on the Microstructure, Phase Analysis, and Electrical Conductivity
    Zulkifli NNI, Abdullah MMAB, Przybył A, Pietrusiewicz P, Salleh MAAM, Aziz IH, et al.
    Materials (Basel), 2021 Apr 26;14(9).
    PMID: 33925777 DOI: 10.3390/ma14092213
    This paper clarified the microstructural element distribution and electrical conductivity changes of kaolin, fly ash, and slag geopolymer at 900 °C. The surface microstructure analysis showed the development in surface densification within the geopolymer when in contact with sintering temperature. It was found that the electrical conductivity was majorly influenced by the existence of the crystalline phase within the geopolymer sample. The highest electrical conductivity (8.3 × 10-4 Ωm-1) was delivered by slag geopolymer due to the crystalline mineral of gehlenite (3Ca2Al2SiO7). Using synchrotron radiation X-ray fluorescence, the high concentration Ca boundaries revealed the appearance of gehlenite crystallisation, which was believed to contribute to development of denser microstructure and electrical conductivity.
    Matched MeSH terms: X-Rays
  16. Fulltext Intelligent Bone Age Assessment: An Automated System to Detect a Bone Growth Problem Using Convolutional Neural Networks with Attention Mechanism
    Zulkifley MA, Mohamed NA, Abdani SR, Kamari NAM, Moubark AM, Ibrahim AA
    Diagnostics (Basel), 2021 Apr 24;11(5).
    PMID: 33923215 DOI: 10.3390/diagnostics11050765
    Skeletal bone age assessment using X-ray images is a standard clinical procedure to detect any anomaly in bone growth among kids and babies. The assessed bone age indicates the actual level of growth, whereby a large discrepancy between the assessed and chronological age might point to a growth disorder. Hence, skeletal bone age assessment is used to screen the possibility of growth abnormalities, genetic problems, and endocrine disorders. Usually, the manual screening is assessed through X-ray images of the non-dominant hand using the Greulich-Pyle (GP) or Tanner-Whitehouse (TW) approach. The GP uses a standard hand atlas, which will be the reference point to predict the bone age of a patient, while the TW uses a scoring mechanism to assess the bone age using several regions of interest information. However, both approaches are heavily dependent on individual domain knowledge and expertise, which is prone to high bias in inter and intra-observer results. Hence, an automated bone age assessment system, which is referred to as Attention-Xception Network (AXNet) is proposed to automatically predict the bone age accurately. The proposed AXNet consists of two parts, which are image normalization and bone age regression modules. The image normalization module will transform each X-ray image into a standardized form so that the regressor network can be trained using better input images. This module will first extract the hand region from the background, which is then rotated to an upright position using the angle calculated from the four key-points of interest. Then, the masked and rotated hand image will be aligned such that it will be positioned in the middle of the image. Both of the masked and rotated images will be obtained through existing state-of-the-art deep learning methods. The last module will then predict the bone age through the Attention-Xception network that incorporates multiple layers of spatial-attention mechanism to emphasize the important features for more accurate bone age prediction. From the experimental results, the proposed AXNet achieves the lowest mean absolute error and mean squared error of 7.699 months and 108.869 months2, respectively. Therefore, the proposed AXNet has demonstrated its potential for practical clinical use with an error of less than one year to assist the experts or radiologists in evaluating the bone age objectively.
    Matched MeSH terms: X-Rays
  17. Fulltext Preparation and Characterization of New Sol-Gel Hybrid Inulin-TEOS Adsorbent
    Mohd Yusop H, Mohd Ismail AIH, Wan Ismail WN
    Polymers (Basel), 2021 Apr 15;13(8).
    PMID: 33921052 DOI: 10.3390/polym13081295
    A new biopolymer-silica hybrid material consisting of inulin and tetraethoxysilane (TEOS) for use as an adsorbent was successfully synthesized via the sol-gel method in acidic conditions. The hydrolysis and condensation processes were attained in water/ethanol solution. Three molar ratios of inulin:TEOS (1:1, 1:2, and 2:1) were prepared and dried at various temperatures (50, 60, and 70 °C). The optimized molar ratio of 2:1 with a drying temperature of 70 °C was found to obtain the best morphology and characteristics for absorbent properties. Fourier transform infrared spectroscopy (FTIR) analysis showed a strong interaction between inulin and TEOS, which was also observed using energy dispersive X-ray spectroscopy (EDX). Field emission scanning electron microscopy (FESEM) images revealed the presence of nanoparticles on the rough surface of the hybrid sol-gel. X-ray diffractometer (XRD) analysis showed the amorphous state of the silica network where the inulin existed as an anhydrous crystalline phase. Brunauer-Emmet-Teller (BET) analysis confirmed that the composite was mesoporous, with 17.69 m2/g surface area and 34.06 Å pore size. According to thermogravimetric analysis (TGA) results, the hybrid inulin-TEOS adsorbent was thermally stable under a temperature of 200 °C.
    Matched MeSH terms: X-Rays
  18. Edible Bird's Nest Attenuates Menopause-Related Bone Degeneration in Rats via Increaing Bone Estrogen-Receptor Expression
    Hou ZP, Tang SY, Ji HR, He PY, Li YH, Dong XL, et al.
    Chin J Integr Med, 2021 Apr;27(4):280-285.
    PMID: 31872369 DOI: 10.1007/s11655-019-3209-1
    OBJECTIVE: To investigate the mechanistic basis for the attenuation of bone degeneration by edible bird's nest (EBN) in ovariectomized rats.

    METHODS: Forty-two female Sprage-Dawley rats were randomized into 7 groups (6 in each group). The ovariectomized (OVX) and OVX + 6%, 3%, and 1.5% EBN and OVX +estrogen groups were given standard rat chow alone, standard rat chow +6%, 3%, and 1.5% EBN, or standard rat chow +estrogen therapy (0.2mg/kg per day), respectively. The sham-operation group was surgically opened without removing the ovaries. The control group did not have any surgical intervention. After 12 weeks of intervention, blood samples were taken for serum estrogen, osteocalcin, and osteoprotegerin, as well as the measurement of magnesium, calcium abd zinc concentrations. While femurs were removed from the surrounding muscles to measure bone mass density using the X-ray edge detection technique, then collected for histology and estrogen receptor (ER) immunohistochemistry.

    RESULTS: Ovariectomy altered serum estrogen levels resulting in increased food intake and weight gain, while estrogen and EBN supplementation attenuated these changes. Ovariectomy also reduced bone ER expression and density, and the production of osteopcalcin and osteorotegerin, which are important pro-osteoplastic hormones that promote bone mineraliztion and density. Conversely, estrogen and EBN increased serum estrogen levels leading to increased bone ER expression, pro-osteoplastic hormone production and bone density (all P<0.05).

    CONCLUSION: EBN could be used as a safe alternative to hormone replacement therapys for managing menopausal complications like bone degeneration.

    Matched MeSH terms: X-Rays
  19. Application of Bpw34 photodiode and cold white LED as diagnostic X-ray detectors: A comparative analysis
    Damulira E, Yusoff MNS, Omar AF, Mohd Taib NH, Ahmed NM
    Appl Radiat Isot, 2021 Apr;170:109622.
    PMID: 33592486 DOI: 10.1016/j.apradiso.2021.109622
    This study compares the real-time dosimetric performance of a bpw34 photodiode (PD) and cold white light-emitting diodes (LEDs) based on diagnostic X-ray-induced signals. Signals were extracted when both the transducers were under identical exposure settings, including source-to-detector distance (SDD), tube voltage (kVp), and current-time product (mAs). The transducers were in a photovoltaic configuration, and black vinyl tape was applied on transducer active areas as a form of optical shielding. X-ray beam spectra and energies were simulated using Matlab-based Spektr functions. Transducer performance analysis was based on signal linearity to mAs and air kerma, and sensitivity dependence on absorbed dose, energy, and dose rate. Bpw34 PD and cold white LED output signals were 84.8% and 85.5% precise, respectively. PD signals were 94.7% linear to mAs, whereas LED signals were 91.9%. PD and LED signal linearity to dose coefficients were 0.9397 and 0.9128, respectively. Both transducers exhibited similar dose and energy dependence. However, cold white LEDs were 0.73% less dose rate dependent than the bpw34 PD. Cold white LEDs demonstrated potential in detecting diagnostic X-rays because their performance was similar to that of the bpw34 PD. Moreover, the cold white LED array's dosimetric response was independent of the heel effect. Although cold white LED signals were lower than bpw34 PD signals, they were quantifiable and electronically amplifiable.
    Matched MeSH terms: X-Rays
  20. Characterization of soy-lignin bonded Rhizophora spp. particleboard as substitute phantom material for radiation dosimetric studies - Investigation of CT number, mass attenuation coefficient and effective atomic number
    Zuber SH, Hashikin NAA, Mohd Yusof MF, Aziz MZA, Hashim R
    Appl Radiat Isot, 2021 Apr;170:109601.
    PMID: 33515930 DOI: 10.1016/j.apradiso.2021.109601
    Experimental particleboards are made from Rhizophora spp. wood trunk with three different percentages of lignin and soy flour (0%, 6% and 12%) as adhesives. The objective was to investigate the equivalence of Rhizophora spp. particleboard as phantom material with human soft tissue using Computed Tomography (CT) number. The linear and mass attenuation coefficient of Rhizophora spp. particleboard at low energy range was also explored using X-ray Fluorescence (XRF) configuration technique. Further characterization of the particleboard was performed to determine the effective atomic number, Zeff using Energy Dispersive X-Ray (EDX) method. Adhesive-bonded Rhizophora spp. particleboard showed close similarities with water, based on the average CT numbers, electron density calibration curve and the analysis of CT density profile, compared to the binderless particleboard. The effective atomic number obtained from the study indicated that the attenuation properties of all the particleboards at different percentages of adhesives were almost similar to water. The mass attenuation coefficient calculated from XRF configuration technique showed good agreement with water from XCOM database, suggesting its potential as phantom material for radiation study.
    Matched MeSH terms: X-Rays
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