Delivery of chemotherapeutic drug targeting folate receptor to oral cancer cells using functionalized carbon nanospheres.

Folate receptor (FR) (α) has long been the subject of active interest as regards its potential to serve as a target for cancer therapy. FR has been found to be overexpressed in several cancers, including clinical samples of different stages from OSCC (oral squamous cell carcinoma) patients. However, no clear correlation or conclusive finding has been obtained so far which might indicate the efficacy of FR as a credible target for the treatment of OSCC. All cell lines to be used were assessed for FR-expression. Subsequently, we developed glucose-derived carbon nanospheres (CSPs) and primed them with a Folate-based cationic lipid FA8 and the chemotherapeutic drug doxorubicin (DOX). CSP based delivery systems along with pristine drug DOX were characterized and treated subsequently toin vitrocultures of OSCC cells and assessed for cancer cell targetability as well as cell death. Subsequently, treatment was administered to immunocompetent C57 mice carrying MOC2 based syngeneic OSCC tumours and assessed for tumour regression and toxicity. Ligand primed targeted CSPs exhibited commendable drug uptake as well as efficient induction of cell death. Further, receptor blocking studies revealed FR-mediated uptake, preferentially in cancer cells. Drug once delivered by ligand-primed CSPs was retained longer inside cells than pristine drug alone, indicating possibilities of better therapeutic outcome. In animal studies, CSP-FA8-DOX (Ligand primed targeted CSP) demonstrated significant regression in tumour size compared to pristine DOX as well as CSP-DOX (non-targeted CSP) treated animals. FR-mediated system CFD demonstrated targeted drug uptake and apoptotic death selectively in cancer cells. Significant tumour regression was also observedin vivo. Overall, it may be presumed that the FR is a therapeutic target with substantial potential in OSCC treatment.

View-invariant Deep Architecture for Human Action Recognition using Two-stream Motion and Shape Temporal Dynamics.

Human action Recognition for unknown views, is a challenging task. We propose a deep view-invariant human action recognition framework, which is a novel integration of two important action cues: motion and shape temporal dynamics (STD). The motion stream encapsulates the motion content of action as RGB Dynamic Images (RGB-DIs), which are generated by Approximate Rank Pooling (ARP) and processed by using finetuned InceptionV3 model. The STD stream learns long-term view-invariant shape dynamics of action using a sequence of LSTM and Bi-LSTM learning models. Human Pose Model (HPM) generates view-invariant features of structural similarity index matrix (SSIM) based key depth human pose frames. The final prediction of the action is made on the basis of three types of late fusion techniques i.e. maximum (max), average (avg) and multiply (mul), applied on individual stream scores. To validate the performance of the proposed novel framework, the experiments are performed using both cross-subject and cross-view validation schemes on three publically available benchmarks- NUCLA multi-view dataset, UWA3D-II Activity dataset and NTU RGB-D Activity dataset. Our algorithm outperforms existing state-of-the-arts significantly, which is measured in terms of recognition accuracy, receiver operating characteristic (ROC) curve and area under the curve (AUC).