Magnetic Resonance Imaging Property of Doxorubicin-Loaded Gadolinium/13X Zeolite/Folic Acid Nanocomposite

Ghaderi, Serveh

doi:10.31661/jbpe.v0i0.1254

Cited by 6 publications

(9 citation statements)

References 21 publications

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“…Magnetic resonance imaging (MRI) is a noninvasive and novel imaging modality [4][5][6]. The use of contrast agents (CAs) in MRI is offered to enhance image contrast as well as the identification of diseased cells [7].…”

Section: Introductionmentioning

confidence: 99%

“…), and physical differences (crystalline structure, magnetic fields, electrical fields, etc.) [1,6,10].…”

Section: Introductionmentioning

confidence: 99%

“…Tatlier and Erdem-Çenatalar [33] estimated the value of the FD of the 13X zeolite in the following earlier reports: 2.08. Finally, in this study, we will use the SEM image of the Gd 3+ /13X/DOX/FA nanocomposite [6].…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, there are substantial differences across related research regarding the type and structure of the contrast material used and the SEM image processing technique employed. Also, the FD method on a manufactured MRI nanocomposite with anticancer and targeting properties, Gd 3+ /13X/DOX/FA nanocomposite, will be performed for the first time [6].…”

Section: Introductionmentioning

confidence: 99%

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Fractal Dimension Image Processing for Feature Extraction and Morphological Analysis: Gd3+/13X/DOX/FA MRI Nanocomposite

Ghaderi

2023

Journal of Nanomaterials

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One of the most fundamental subjects in nanoscience and nanotechnology is structural analysis. We employed a scanning electron microscope (SEM) image of the manufactured Gd3+/13X/DOX/FA nanocomposite in this study. The size, dimensions, and morphology of nanocomposite materials were studied to ensure the uniformity and homogeneity of SEM images. This is the first study to look at segmented SEM images for fractal dimension (FD) and other statistical criteria, including average, maximum, minimum, skewness, and range for magnetic resonance imaging (MRI) nanocomposite. The average of FD (FDavg), the standard deviation of FD (FDsd), and the lacunarity of FD (FDlac) fractal data analysis criteria were also employed. The findings show that particle sizes and shapes vary because the minimum-to-maximum range is not zero, and our data provide a reasonable range. This interpretation is further supported by an analysis of the nanocomposite’s SEM image. At first glance, the image seemed to be uniform. However, when the calculations were performed, it was discovered that the generated particles were not particularly uniform. The particles were uniformly dispersed throughout all surfaces, although their sizes, dimensions, and morphologies varied. In conclusion, the study was supported by fractal data analysis, emphasizing the importance of structural analysis for future research, particularly for medical applications like MRI.

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Section: Introductionmentioning

confidence: 99%

“…), and physical differences (crystalline structure, magnetic fields, electrical fields, etc.) [1,6,10].…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Fractal Dimension Image Processing for Feature Extraction and Morphological Analysis: Gd3+/13X/DOX/FA MRI Nanocomposite

Ghaderi

2023

Journal of Nanomaterials

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“…Gadolinium is a choice positive contrast agent in MRI because of the seven unpaired electrons which make it one of the elements with a high magnetic moment [ 4 ]. In addition, gadolinium based nanomaterials have potential for anticancer drug loading and tumor targeting [ 5 , 6 ]. Size, shape and surface properties of gadolinium based nanomaterials are the important factors that influence correlation of gadolinium ions with water protons [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Effect of Neodymium Doping on MRI Relaxivity of Gadolinium Oxide Nanoparticles

2020

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Background: Gadolinium oxide nanoparticles as positive contrast material of magnetic resonance imaging (MRI) have attracted a great attention due to the appropriate magnetic properties. One of the most desirable features of these nanoparticles is their ability of doping with other lanthanides which can change their properties. Objective: This study aimed to investigate the effect of neodymium doping on MRI relaxivity of the gadolinium oxide nanoparticles. Material and Methods: In this experimental study, the oleic acid coated gadolinium oxide nanoparticles and the neodymium doped nanoparticles were prepared by polymer pyrolysis method. X-ray diffraction test and scanning electron microscopy were used for characterization of the particles. 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay was performed to investigate the in vitro cell toxicity of the nanoparticles. The r1 and r2 relaxivities were extracted from the T1 and T2 weighted MR images, respectively. Results: The average size of the cytocompatible spherical-like shape nanoparticles was 40 nm. The neodymium doped nanoparticles produced a significant decrease in the r1 relaxivity, and a 1.7 fold increase in the r2 relaxivity compared to the gadolinium oxide nanoparticles. Conclusion: Doping of neodymium into the gadolinium oxide nanoparticles suppresses the r1 relaxivity and enhances the r2 relaxivity of the nanoparticles.

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Highway Traffic Flow Prediction Algorithm Based on Multiscale Transformation and Convolutional Networks

Luo

Wang

Ming

2022

Journal of Control Science and Engineering

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In order to solve the problem that the traditional long-term high-speed traffic forecasting algorithm is affected by the approximation ability of the function and easy to fall into the local mass value, we wrote a multivariate-based highway traffic forecasting algorithm scaling and convolutional networks. Because the feedforward wavelet neural network algorithm predicts the short-term traffic flow in different areas, it is necessary to examine the ability to predict the difference between different models. From the standard feedforward wavelet neural network algorithm using global optimization capabilities, we improve the wolf pack algorithm, improve the search accuracy of the algorithm, get the best solution of the estimated value of the work according to the search results when completing the research objectives, and get the ability to predict the work of the model. Feedforward neural network algorithm: we develop and obtain the best short-term high-speed traffic forecast values. The results are as follows: after using the author’s algorithm, the processing time increases by 1.5 seconds, but the average percentage of errors decreases by more than 50%, in fact the error and the root mean square error decreased by about 30%, and the smoothing coefficient increased by about 1%. The prediction of the author’s algorithm for short-term high-speed traffic is better than the wavelet neural network prediction algorithm, and the prediction accuracy and stability of the author’s algorithm are higher.

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Magnetic Resonance Imaging Property of Doxorubicin-Loaded Gadolinium/13X Zeolite/Folic Acid Nanocomposite

Cited by 6 publications

References 21 publications

Fractal Dimension Image Processing for Feature Extraction and Morphological Analysis: Gd3+/13X/DOX/FA MRI Nanocomposite

Fractal Dimension Image Processing for Feature Extraction and Morphological Analysis: Gd3+/13X/DOX/FA MRI Nanocomposite

Effect of Neodymium Doping on MRI Relaxivity of Gadolinium Oxide Nanoparticles

Highway Traffic Flow Prediction Algorithm Based on Multiscale Transformation and Convolutional Networks

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