ABSTRACT
The cell nucleus serves as the pivotal command center of living cells, and delivering therapeutic agents directly into the nucleus can result in highly efficient anti-tumor eradication of cancer cells. However, nucleus-targeting drug delivery is very difficult due to the presence of numerous biological barriers. Here, three antitumor drugs (DNase I, ICG: indocyanine green, and THP: pirarubicin) were sequentially triggered protein self-assembly to produce a nucleus-targeting and programmed responsive multi-drugs delivery system (DIT). DIT consisted of uniform spherical particles with a size of 282 ± 7.7 nm. The acidic microenvironment of tumors and near-infrared light could successively trigger DIT for the programmed release of three drugs, enabling targeted delivery to the tumor. THP served as a nucleus-guiding molecule and a chemotherapy drug. Through THP-guided DIT, DNase I was successfully delivered to the nucleus of tumor cells and killed them by degrading their DNA. Tumor acidic microenvironment had the ability to induce DIT, leading to the aggregation of sufficient ICG in the tumor tissues. This provided an opportunity for the photothermal therapy of ICG. Hence, three drugs were cleverly combined using a simple method to achieve multi-drugs targeted delivery and highly effective combined anticancer therapy.
Subject(s)
Antineoplastic Agents , Cell Nucleus , Deoxyribonuclease I , Doxorubicin , Drug Delivery Systems , Drug Liberation , Animals , Mice , Antineoplastic Agents/pharmacology , Antineoplastic Agents/chemistry , Antineoplastic Agents/administration & dosage , Cell Line, Tumor , Cell Nucleus/metabolism , Deoxyribonuclease I/metabolism , Doxorubicin/pharmacology , Doxorubicin/chemistry , Doxorubicin/administration & dosage , Doxorubicin/analogs & derivatives , Drug Carriers/chemistry , Indocyanine Green/chemistry , Tumor Microenvironment/drug effects , Male , Mice, Inbred BALB C , Mice, NudeABSTRACT
Since the conventional split-merge algorithm is sensitive to the object scale variance and splitting starting point, a piecewise split-merge polygon-approximation method is proposed to extract the object contour features. Specifically, the contour corner is used as the starting point for the contour piecewise approximation to reduce the sensitivity of the contour segment for the starting point; then, the split-merge algorithm is used to implement the polygon approximation for each contour segment. Both the distance ratio and the arc length ratio instead of the distance error are used as the iterative stop condition to improve the robustness to the object scale variance. Both the angle and length as two features describe the shape of the contour polygon; they have a strong coupling relationship since they affect each other along the contour order relationship. To improve the description correction of the contour, these two features are combined to construct a Coupled Hidden Markov Model to detect the object by calculating the probability of the contour feature. The proposed algorithm is validated on ETHZ Shape Classes and INRIA Horses standard datasets. Compared with other contour-based object-detection algorithms, the proposed algorithm reduces the feature number and improves the object-detection rate.
