Super-sensitive bifunctional nanoprobe: Self-assembly of peptide-driven nanoparticles demonstrating tumor fluorescence imaging and therapy

The development of nanomedicine has recently achieved several breakthroughs in the field of cancer treatment; however, biocompatibility and targeted penetration of these nanomaterials remain as limitations, which lead to serious side effects and significantly narrow the scope of their application. The self-assembly of intermediate filaments with arginine-glycine-aspartate (RGD) peptide (RGD-IFP) was triggered by the hydrophobic cationic molecule 7-amino actinomycin D (7-AAD) to synthesize a bifunctional nanoparticle that could serve as a fluorescent imaging probe to visualize tumor treatment. The designed RGD-IFP peptide possessed the ability to encapsulate 7-AAD molecules through the formation of hydrogen bonds and hydrophobic interactions by a one-step method. This fluorescent nanoprobe with RGD peptide could be targeted for delivery into tumor cells and released in acidic environments such as endosomes/lysosomes, ultimately inducing cytotoxicity by arresting tumor cell cycling with inserted DNA. It is noteworthy that the RGD-IFP/7-AAD nanoprobe tail-vein injection approach demonstrated not only high tumor-targeted imaging potential, but also potent antitumor therapeutic effects in vivo. The proposed strategy may be used in peptide-driven bifunctional nanoparticles for precise imaging and cancer therapy.

Primary central nervous system lymphoma and glioblastoma image differentiation based on sparse representation system / 生物医学工程学杂志

It is of great clinical significance in the differential diagnosis of primary central nervous system lymphoma (PCNSL) and glioblastoma (GBM) because there are enormous differences between them in terms of therapeutic regimens. In this paper, we propose a system based on sparse representation for automatic classification of PCNSL and GBM. The proposed system distinguishes the two tumors by using of the different texture detail information of the two tumors on T1 contrast magnetic resonance imaging (MRI) images. First, inspired by the process of radiomics, we designed a dictionary learning and sparse representation-based method to extract texture information, and with this approach, the tumors with different volume and shape were transformed into 968 quantitative texture features. Next, aiming at the problem of the redundancy in the extracted features, feature selection based on iterative sparse representation was set up to select some key texture features with high stability and discrimination. Finally, the selected key features are used for differentiation based on sparse representation classification (SRC) method. By using ten-fold cross-validation method, the differentiation based on the proposed approach presents accuracy of 96.36%, sensitivity 96.30%, and specificity 96.43%. Experimental results show that our approach not only effectively distinguish the two tumors but also has strong robustness in practical application since it avoids the process of parameter extraction on advanced MRI images.

Development of Histopathological Breast Tumor Image Retrieval System Based on Internet Using a Content-based Retrieval Method / 대한의료정보학회지

OBJECTIVE: We have developed breast tumor image retrieval system using content-based retrieval method. It compares the breast tumor image with Fibrocystic Change images, Ductal Carcinoma in Situ images and Invasive Ductal Carcinoma images and find most similar one. Since the final diagnosis for breast tumor image is done only by pathologist manually, this system can provide the objectivity and the reproducibility for determining and diagnosing the breast tumor. METHODS: The breast tumor image features used in the content-based image retrieval are color feature, texture feature and texture features of wavelet transformed images. And the system can be accessed through the internet. We used Windows 2003 as an operating system, Internet Information Server 6.0 as Web a server and ms-sql server 2000 as a database server. Also we use ActiveX Data Object to connect database easily. RESULT: We evaluated the recall and precision performance of the system according to the combinations of feature types and usage of partial or whole image. Results showed that the use of multiple features and whole image gave consistently higher rates compared to the use of single feature and partial image. CONCLUSION: This retrieval system can help pathologist determine the type of breast tumor more efficiently. Also it is working based on the internet, we can use it for researching and teaching in pathology later.