scholarly journals RHAMM-Target Peptides as Molecular Imaging Probes for the Imaging of RHAMM-Expressing Cancer Cells

2021 ◽  
Vol 12 (1) ◽  
pp. 2-24
Keyword(s):  
Cancer Cells ◽  
Molecular Diagnosis ◽  
Elisa Method ◽  
Early Diagnosis Of Cancer ◽  
Molecular Imaging Probes ◽  
Alanine Scan ◽  
Imaging Probes ◽  
Surface Receptor ◽  
Diagnosis Of Cancer ◽  
Specificity Of Binding

The incidence of cancer in the world is growing steadily. Therefore, it is necessary to develop new approaches for the early diagnosis of cancer. This work is devoted to the study of the potential of RHAMM-target peptides for molecular diagnosis of cancer. The key amino acids of the RHAMM target peptides were identified by the alanine scan method. The specificity of binding of peptides to RHAMM-CT was assessed using competitive HA substitution by the ELISA method. RHAMM-CT was obtained by genetic engineering and isolated by affinity chromatography. The interaction of RHAMM target peptides with the surface receptor of tumor cells was evaluated by confocal microscopy. It has been shown that fragment EEGEEZ in the peptides' composition is necessary for binding to the RHAMM-CT. The results showed that the RHAMM-target peptides bind specifically to the RHAMM-CT and competitively substituted HA at the RHAMM. It has been found that aggrecan is unable to displace peptides from the HA binding site of RHAMM-CT. The results showed that the FITC peptide binds specifically to RHAMM on the surface of prostate cancer cells. Therefore, RHAMM-target peptides have the potential for early molecular diagnosis of cancer.

scholarly journals Multimodal Imaging Probe for Melanoma Evaluating of PTK7 Expression by Sgc8-c Aptamer Recognition

2021 ◽  
Author(s):  
Estefanía Sicco ◽  
Amy Mónaco ◽  
Marcelo Fernandez ◽  
María Moreno ◽  
Victoria Calzada ◽  
...  
Keyword(s):  
Molecular Imaging ◽  
Dna Aptamer ◽  
Specific Expression ◽  
Imaging Probe ◽  
Molecular Imaging Probes ◽  
Imaging Probes ◽  
Diagnosis Of Cancer ◽  
Animal Tumor

Abstract Melanoma is one of the most aggressive and deadly skin cancers, and although histopathological criteria are used for its prognosis, biomarkers are necessary to identify the different evolution stages. The applications of molecular imaging include the in vivo diagnosis of cancer with probes that recognize the tumor-biomarkers specific expression allowing external images acquisitions and evaluations of the biological process in quali-quantitative ways. Aptamers are oligonucleotides that recognize targets with high affinity and specificity presenting advantages that make them interesting molecular imaging probes. Sgc8-c (DNA-aptamer) selectively recognizes PTK7-receptor overexpressed in various types of tumors. Herein, Sgc8-c was evaluated, in two melanoma models, non-metastatic and metastatic, as molecular imaging probe for in vivo diagnostic. Firstly, two probes, radio- and fluorescent-probe, were in vitro evaluated verifying the high specific PTK7 recognition and its internalization in tumor cells by the endosomal route. Secondly, in vivo proof of concept was performed in animal tumor models. Likewise, they have rapid clearance from blood exhibiting excellent target (tumor)/non-target organ ratios. Furthermore, optimal biodistribution was observed 24 hours after probes-injections accumulating almost exclusively in the tumor tissue. Sgc8-c is a potential tool for their specific use in the early detection of melanoma.

scholarly journals The Identification Nanoparticle Sensor Using Back Propagation Neural Network Optimized by Genetic Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yiwen Hu ◽  
Ashutosh Sharma ◽  
Gaurav Dhiman ◽  
Mohammad Shabaz
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Early Diagnosis ◽  
Cancer Cells ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Dark Field ◽  
Green Fluorescence ◽  
Early Diagnosis Of Cancer ◽  
Diagnosis Of Cancer

This study draws attention towards the application of identification nanoparticle (NPs) sensor based on back propagation (BP) neural network optimized by genetic algorithm (GA) in the early diagnosis of cancer cells. In this study, the traditional and optimized BP neural networks are compared in terms of error between the actual value and the predictive value, and they are further applied to the NP sensor for early diagnosis of cancer cells. The results show that the root mean square (RMS) and mean absolute error (MAE) of the optimized BP neural network are comparatively much smaller than the traditional ones. The particle size of silicon-coated fluorescent NPs is about 105 nm, and the relative fluorescence intensity of silicon-coated fluorescent NPs decreases slightly, maintaining the accuracy value above 80%. In the fluorescence imaging, it is found that there is obvious green fluorescence on the surface of the cancer cells, and the cancer cells still emit bright green fluorescence under the dark-field conditions. In this study, a phenolic resin polymer CMK-2 with a large surface area is successfully combined with Au. NPs with good dielectric property and bioaffinity are selectively bonded to the modified electrode through a sulfur-gold bond to prepare NP sensor. The sensor shows good stability, selectivity, and anti-interference property, providing a new method for the detection of early cancer cells.

scholarly journals On-Chip Liquid Biopsy: Progress in Isolation of Exosomes for Early Diagnosis of Cancer

2017 ◽  
Vol 112 (3) ◽  
pp. 461a ◽  
Author(s):  
Sung Cheol Kim ◽  
Navneet Dogra ◽  
Benjamin H. Wunsch ◽  
Joshua T. Smith ◽  
Stacey M. Gifford ◽  
...  
Keyword(s):  
Early Diagnosis ◽  
Liquid Biopsy ◽  
Early Diagnosis Of Cancer ◽  
Diagnosis Of Cancer ◽  
On Chip

Autofluorescent gelatin nanoparticles as imaging probes to monitor matrix metalloproteinase metabolism of cancer cells

2016 ◽  
Vol 104 (11) ◽  
pp. 2854-2860 ◽  
Author(s):  
Bo Cai ◽  
Lang Rao ◽  
Xinghu Ji ◽  
Lin-Lin Bu ◽  
Zhaobo He ◽  
...  
Keyword(s):  
Matrix Metalloproteinase ◽  
Cancer Cells ◽  
Gelatin Nanoparticles ◽  
Imaging Probes

scholarly journals Breast Thermograms Asymmetry Analysis using Gabor filters

2021 ◽  
Vol 309 ◽  
pp. 01109
Author(s):  
Priyanka Yadlapalli ◽  
Madhavi K Reddy ◽  
Sunitha Gurram ◽  
J Avanija ◽  
K Meenakshi ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Early Stage ◽  
Cost Effective ◽  
Gabor Filters ◽  
Support Vector ◽  
Svm Classifier ◽  
Infrared Thermal Imaging ◽  
Younger Women ◽  
Early Diagnosis Of Cancer ◽  
Diagnosis Of Cancer

Women are far more likely than males to acquire breast cancer, and current research indicates that this is entirely avoidable. It is also to blame for higher death rates among younger women compared to older women in nearly all developing nations. Medical imaging modalities are continuously in need of development. A variety of medical techniques have been employed to detect breast cancer in women. The most recent studies support mammography for breast cancer screening, although its sensitivity and specificity remain suboptimal, particularly in individuals with thick breast tissue, such as young women. As a result, alternative modalities, such as thermography, are required. Digital Infrared Thermal Imaging (DITI), as it is known, detects and records temperature changes on the skin’s surface. Thermography is well-known for its non-invasive, painless, cost-effective, and high recovery rates, as well as its potential to identify breast cancer at an early stage. Gabor filters are used to extract the textural characteristics of the left and right breasts. Using a support vector machine, the thermograms are then classified as normal or malignant based on textural asymmetry between the breasts (SVM). The accuracy achieved by combining Gabor features with an SVM classifier is around 84.5 percent. The early diagnosis of cancer with thermography enhances the patient’s chances of survival significantly since it may detect the disease in its early stages.

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