Displaying publications 1 - 20 of 276 in total

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  1. Auto-detection of the coronavirus disease by using deep convolutional neural networks and X-ray photographs
    Hussein AM, Sharifai AG, Alia OM, Abualigah L, Almotairi KH, Abujayyab SKM, et al.
    Sci Rep, 2024 Jan 04;14(1):534.
    PMID: 38177156 DOI: 10.1038/s41598-023-47038-3
    The most widely used method for detecting Coronavirus Disease 2019 (COVID-19) is real-time polymerase chain reaction. However, this method has several drawbacks, including high cost, lengthy turnaround time for results, and the potential for false-negative results due to limited sensitivity. To address these issues, additional technologies such as computed tomography (CT) or X-rays have been employed for diagnosing the disease. Chest X-rays are more commonly used than CT scans due to the widespread availability of X-ray machines, lower ionizing radiation, and lower cost of equipment. COVID-19 presents certain radiological biomarkers that can be observed through chest X-rays, making it necessary for radiologists to manually search for these biomarkers. However, this process is time-consuming and prone to errors. Therefore, there is a critical need to develop an automated system for evaluating chest X-rays. Deep learning techniques can be employed to expedite this process. In this study, a deep learning-based method called Custom Convolutional Neural Network (Custom-CNN) is proposed for identifying COVID-19 infection in chest X-rays. The Custom-CNN model consists of eight weighted layers and utilizes strategies like dropout and batch normalization to enhance performance and reduce overfitting. The proposed approach achieved a classification accuracy of 98.19% and aims to accurately classify COVID-19, normal, and pneumonia samples.
    Matched MeSH terms: X-Rays
  2. The potential role of artificial intelligence-assisted chest X-ray imaging in detecting early-stage lung cancer in the community-a proposed algorithm for lung cancer screening in Malaysia
    Sachithanandan A, Lockman H, Azman RR, Tho LM, Ban EZ, Ramon V
    Med J Malaysia, 2024 Jan;79(1):9-14.
    PMID: 38287751
    INTRODUCTION: The poor prognosis of lung cancer has been largely attributed to the fact that most patients present with advanced stage disease. Although low dose computed tomography (LDCT) is presently considered the optimal imaging modality for lung cancer screening, its use has been hampered by cost and accessibility. One possible approach to facilitate lung cancer screening is to implement a risk-stratification step with chest radiography, given its ease of access and affordability. Furthermore, implementation of artificial-intelligence (AI) in chest radiography is expected to improve the detection of indeterminate pulmonary nodules, which may represent early lung cancer.

    MATERIALS AND METHODS: This consensus statement was formulated by a panel of five experts of primary care and specialist doctors. A lung cancer screening algorithm was proposed for implementation locally.

    RESULTS: In an earlier pilot project collaboration, AI-assisted chest radiography had been incorporated into lung cancer screening in the community. Preliminary experience in the pilot project suggests that the system is easy to use, affordable and scalable. Drawing from experience with the pilot project, a standardised lung cancer screening algorithm using AI in Malaysia was proposed. Requirements for such a screening programme, expected outcomes and limitations of AI-assisted chest radiography were also discussed.

    CONCLUSION: The combined strategy of AI-assisted chest radiography and complementary LDCT imaging has great potential in detecting early-stage lung cancer in a timely manner, and irrespective of risk status. The proposed screening algorithm provides a guide for clinicians in Malaysia to participate in screening efforts.

    Matched MeSH terms: X-Rays
  3. Low dose and low dose rate radioluminescence dosimetry
    Bradley DA, Essa RZ, Peh SC, Teow SY, Chew MT, Zubair HT, et al.
    Appl Radiat Isot, 2023 Aug;198:110875.
    PMID: 37257265 DOI: 10.1016/j.apradiso.2023.110875
    Review is provided of a number of low-dose, low dose rate situations that in study require advances in the development of dosimetric facilities. Using a clinical linac set up to provide doses down to the few mGy level, the performance of a real-time radioluminescence system has then been illustrated, accommodating pulsed as well as continuous dose delivery. The system gate times provide for tracking of the pattern of dose delivery, allowing detailed account of dose and dose-rate variations. The system has been tested in both x-ray and electron mode dose delivery.
    Matched MeSH terms: X-Rays
  4. Fulltext Development of Debiasing Technique for Lung Nodule Chest X-ray Datasets to Generalize Deep Learning Models
    Horry MJ, Chakraborty S, Pradhan B, Paul M, Zhu J, Loh HW, et al.
    Sensors (Basel), 2023 Jul 21;23(14).
    PMID: 37514877 DOI: 10.3390/s23146585
    Screening programs for early lung cancer diagnosis are uncommon, primarily due to the challenge of reaching at-risk patients located in rural areas far from medical facilities. To overcome this obstacle, a comprehensive approach is needed that combines mobility, low cost, speed, accuracy, and privacy. One potential solution lies in combining the chest X-ray imaging mode with federated deep learning, ensuring that no single data source can bias the model adversely. This study presents a pre-processing pipeline designed to debias chest X-ray images, thereby enhancing internal classification and external generalization. The pipeline employs a pruning mechanism to train a deep learning model for nodule detection, utilizing the most informative images from a publicly available lung nodule X-ray dataset. Histogram equalization is used to remove systematic differences in image brightness and contrast. Model training is then performed using combinations of lung field segmentation, close cropping, and rib/bone suppression. The resulting deep learning models, generated through this pre-processing pipeline, demonstrate successful generalization on an independent lung nodule dataset. By eliminating confounding variables in chest X-ray images and suppressing signal noise from the bone structures, the proposed deep learning lung nodule detection algorithm achieves an external generalization accuracy of 89%. This approach paves the way for the development of a low-cost and accessible deep learning-based clinical system for lung cancer screening.
    Matched MeSH terms: X-Rays
  5. 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
  6. 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
  7. Fulltext Investigating the hard X-ray production via proton spallation on different materials to detect elements
    Khezripour S, Rezaie M, Hassanpour M, Hassanpour M, Rashed Iqbal Faruque M, Uddin Khandaker M
    PLoS One, 2023;18(8):e0288287.
    PMID: 37594963 DOI: 10.1371/journal.pone.0288287
    Various atomic and nuclear methods use hard (high-energy) X-rays to detect elements. The current study aims to investigate the hard X-ray production rate via high-energy proton beam irradiation of various materials. For which, appropriate conditions for producing X-rays were established. The MCNPX code, based on the Monte Carlo method, was used for simulation. Protons with energies up to 1650 MeV were irradiated on various materials such as carbon, lithium, lead, nickel, salt, and soil, where the resulting X-ray spectra were extracted. The production of X-rays in lead was observed to increase 16 times, with the gain reaching 0.18 as the proton energy increases from 100 MeV to 1650 MeV. Comparatively, salt is a good candidate among the lightweight elements to produce X-rays at a low proton energy of 30 MeV with a production gain of 0.03. Therefore, it is suggested to irradiate the NaCl target with 30 MeV proton to produce X-rays in the 0-2 MeV range.
    Matched MeSH terms: X-Rays
  8. 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
  9. 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
  10. 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
  11. 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
  12. 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
  13. 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
  14. 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
  15. 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
  16. 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
  17. 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
  18. 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
  19. 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
  20. 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
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