Regulation of IFP in solid tumours through acoustic pressure to enhance infiltration of nanoparticles of various sizes.

Numerous nanomedicines have been developed recently that can accumulate selectively in tumours due to the enhanced permeability and retention (EPR) effect. However, the high interstitial fluid pressure (IFP) in solid tumours limits the targeted delivery of nanomedicines. We were previously able to relieve intra-tumoural IFP by low-frequency non-focused ultrasound (LFNFU) through ultrasonic targeted microbubble destruction (UTMD), improving the targeted delivery of FITC-dextran. However, the accumulation of nanoparticles of different sizes and the optimal acoustic pressure were not evaluated. In this study, we synthesised Cy5.5-conjugated mesoporous silica nanoparticles (Cy5.5-MSNs) of different sizes using a one-pot method. The Cy5.5-MSNs exhibited excellent stability and biosafety regardless of size. MCF7 tumour-bearing mice were subjected to UTMD over a range of acoustic pressures (0.5, 0.8, 1.5 and 2.0 MPa), and injected intravenously with Cy5.5-MSNs. Blood perfusion, tumour IFP and intra-tumoural accumulation of Cy5.5-MSNs were analysed. Blood perfusion and IFP initially rose, and then declined, as acoustic pressure intensified. Furthermore, UTMD significantly enhanced the accumulation of differentially sized Cy5.5-MSNs in tumour tissues compared to that of the control group, and the increase was sevenfold higher at an acoustic pressure of 1.5 MPa. Taken together, UTMD enhanced the infiltration and accumulation of Cy5.5-MSNs of different sizes in solid tumours by reducing intra-tumour IFP.

Pairwise constrained concept factorization for data representation.

Concept factorization (CF) is a variant of non-negative matrix factorization (NMF). In CF, each concept is represented by a linear combination of data points, and each data point is represented by a linear combination of concepts. More specifically, each concept is represented by more than one data point with different weights, and each data point carries various weights called membership to represent their degrees belonging to that concept. However, CF is actually an unsupervised method without making use of prior information of the data. In this paper, we propose a novel semi-supervised concept factorization method, called Pairwise Constrained Concept Factorization (PCCF), which incorporates pairwise constraints into the CF framework. We expect that data points which have pairwise must-link constraints should have the same class label as much as possible, while data points with pairwise cannot-link constraints will have different class labels as much as possible. Due to the incorporation of the pairwise constraints, the learning quality of the CF has been significantly enhanced. Experimental results show the effectiveness of our proposed novel method in comparison to the state-of-the-art algorithms on several real world applications.