Gold nanostars functionalized with amine-terminated PEG for X-ray/CT imaging and photothermal therapy

2015 ◽  
Vol 3 (21) ◽  
pp. 4330-4337 ◽  
Author(s):  
Ying Tian ◽  
Song Luo ◽  
Huaijiang Yan ◽  
Zhaogang Teng ◽  
Yuanwei Pan ◽  
...  
Keyword(s):  
Photothermal Therapy ◽  
Ct Imaging ◽  
Ct Guided ◽  
X Ray ◽  
Gold Nanostars

We present the great potential of gold nanostars decorated with amine-terminated PEG in the application of X-ray/CT-guided photothermal therapy.

Construction of polydopamine-coated gold nanostars for CT imaging and enhanced photothermal therapy of tumors: an innovative theranostic strategy

2016 ◽  
Vol 4 (23) ◽  
pp. 4216-4226 ◽  
Author(s):  
Du Li ◽  
Yongxing Zhang ◽  
Shihui Wen ◽  
Yang Song ◽  
Yueqin Tang ◽  
...  
Keyword(s):  
Photothermal Therapy ◽  
Ct Imaging ◽  
Gold Nanostars

A theranostic nanoplatform for in vivo CT imaging and enhanced PTT of tumors is reported.

scholarly journals Untrasmall Bi2S3 nanodots for in vivo X-ray CT imaging-guided photothermal therapy of cancer

RSC Advances ◽  
2017 ◽  
Vol 7 (47) ◽  
pp. 29672-29678 ◽  
Author(s):  
Zelun Li ◽  
Kelong Ai ◽  
Zhe Yang ◽  
Tianqi Zhang ◽  
Jianhua Liu ◽  
...  
Keyword(s):  
Cancer Treatment ◽  
Real Time ◽  
Photothermal Therapy ◽  
Therapeutic Response ◽  
Ct Imaging ◽  
X Ray

Theranostic nanomedicine has shown tremendous promise for more effective and predictive cancer treatment by real-time mornitoring of the delivery of therapeutics to tumors and subsequent therapeutic response.

scholarly journals Control of Arms of Au Stars Size and its Dependent Cytotoxicity and Photosensitizer Effects in Photothermal Anticancer Therapy

2019 ◽  
Vol 20 (20) ◽  
pp. 5011 ◽  
Author(s):  
Joanna Depciuch ◽  
Malgorzata Stec ◽  
Alexey Maximenko ◽  
Miroslawa Pawlyta ◽  
Jarek Baran ◽  
...  
Keyword(s):  
Potential Application ◽  
Photothermal Therapy ◽  
Anticancer Therapy ◽  
Heat Energy ◽  
X Ray Diffraction ◽  
X Ray ◽  
Gold Nanostars ◽  
Transmission Electron ◽  
193 Nm ◽  
Cells Cultured

Gold nanostars (AuS NPs) are a very attractive nanomaterial, which is characterized by high effective transduction of the electromagnetic radiation into heat energy. Therefore, AuS NPs can be used as photosensitizers in photothermal therapy (PTT). However, understanding the photothermal conversion efficiency in nanostars is very important to select the most appropriate shape and size of AuS NPs. Therefore, in this article, the synthesis of AuS NPs with different lengths of star arms for potential application in PTT was investigated. Moreover, the formation mechanism of these AuS NPs depending on the reducer concentration is proposed. Transmission electron microscopy (TEM) with selected area diffraction (SEAD) and X-ray diffraction (X-Ray) showed that all the obtained AuS NPs are crystalline and have cores with similar values of the diagonal (parameter d), from 140 nm to 146 nm. However, the widths of the star arm edges (parameter c) and the lengths of the arms (parameter a) vary between 3.75 nm and 193 nm for AuS1 NPs to 6.25 nm and 356 nm for AuS4 NPs. Ultraviolet-visible (UV-Vis) spectra revealed that, with increasing edge widths and lengths of the star arms, the surface plasmon resonance (SPR) peak is shifted to the higher wavelengths, from 640 nm for AuS1 NPs to 770 nm for AuS4 NPs. Moreover, the increase of temperature in the AuS NPs solutions as well as the values of calculated photothermal efficiency grew with the elongation of the star arms. The potential application of AuS NPs in the PTT showed that the highest decrease of viability, around 75%, of cells cultured with AuS NPs and irradiated by lasers was noticed for AuS4 NPs with the longest arms, while the smallest changes were visible for gold nanostars with the shortest arms. The present study shows that photothermal properties of AuS NPs depend on edge widths and lengths of the star arms and the values of photothermal efficiency are higher with the increase of the arm lengths, which is correlated with the reducer concentration.

Hyaluronic Acid Stabilized Iodine-Containing Nanoparticles with Au Nanoshell Coating for X-ray CT Imaging and Photothermal Therapy of Tumors

2016 ◽  
Vol 8 (41) ◽  
pp. 27622-27631 ◽  
Author(s):  
Xinghua Liu ◽  
Chunhui Gao ◽  
Junheng Gu ◽  
Yunfang Jiang ◽  
Xinlin Yang ◽  
...  
Keyword(s):  
Hyaluronic Acid ◽  
Photothermal Therapy ◽  
Ct Imaging ◽  
X Ray

Albumin stabilized gold nanostars: a biocompatible nanoplatform for SERS, CT imaging and photothermal therapy of cancer

RSC Advances ◽  
2016 ◽  
Vol 6 (87) ◽  
pp. 84025-84034 ◽  
Author(s):  
Sisini Sasidharan ◽  
Dhirendra Bahadur ◽  
Rohit Srivastava
Keyword(s):  
Cancer Cells ◽  
Photothermal Therapy ◽  
High Stability ◽  
Ct Imaging ◽  
Gold Nanostars ◽  
Ct Contrast

Albumin stabilization of gold nanostars, which demonstrate high stability, biocompatibility, superior CT contrast, SERS and photothermal cytotoxicity towards cancer cells.

scholarly journals Integrating Machine/Deep Learning Methods and Filtering Techniques for Reliable Mineral Phase Segmentation of 3D X-ray Computed Tomography Images

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4595
Author(s):  
Parisa Asadi ◽  
Lauren E. Beckingham
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Random Forest ◽  
Ct Images ◽  
Ct Imaging ◽  
Learning Method ◽  
Learning Methods ◽  
X Ray ◽  
Machine Learning Methods ◽  
Filtering Techniques

X-ray CT imaging provides a 3D view of a sample and is a powerful tool for investigating the internal features of porous rock. Reliable phase segmentation in these images is highly necessary but, like any other digital rock imaging technique, is time-consuming, labor-intensive, and subjective. Combining 3D X-ray CT imaging with machine learning methods that can simultaneously consider several extracted features in addition to color attenuation, is a promising and powerful method for reliable phase segmentation. Machine learning-based phase segmentation of X-ray CT images enables faster data collection and interpretation than traditional methods. This study investigates the performance of several filtering techniques with three machine learning methods and a deep learning method to assess the potential for reliable feature extraction and pixel-level phase segmentation of X-ray CT images. Features were first extracted from images using well-known filters and from the second convolutional layer of the pre-trained VGG16 architecture. Then, K-means clustering, Random Forest, and Feed Forward Artificial Neural Network methods, as well as the modified U-Net model, were applied to the extracted input features. The models’ performances were then compared and contrasted to determine the influence of the machine learning method and input features on reliable phase segmentation. The results showed considering more dimensionality has promising results and all classification algorithms result in high accuracy ranging from 0.87 to 0.94. Feature-based Random Forest demonstrated the best performance among the machine learning models, with an accuracy of 0.88 for Mancos and 0.94 for Marcellus. The U-Net model with the linear combination of focal and dice loss also performed well with an accuracy of 0.91 and 0.93 for Mancos and Marcellus, respectively. In general, considering more features provided promising and reliable segmentation results that are valuable for analyzing the composition of dense samples, such as shales, which are significant unconventional reservoirs in oil recovery.

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