New developments in breast cancer therapy: role of iron oxide nanoparticles

Abstract

Breast cancer is one of the leading causes of deaths in females worldwide. The high metastatic rate and drug resistance makes it one of the difficult cancers to treat. Early diagnosis and treatment are keys to better survival of breast cancer patients. Conventional treatment approaches like chemotherapy, radiotherapy and surgery suffer from major drawbacks. Novel approaches to improve cancer therapy with minimal damage to normal tissues and better quality of life for cancer patients need to be developed. Among various approaches used for treatment and diagnosis of breast cancer, use of nanoparticles (NPs) is coming up as a new and promising treatment regime. It can help overcome various limitations of conventional therapies like non-targeted effects, resistance to treatment, late diagnosis, etc. Among various nanoparticles studied for their biomedical applications, especially for breast cancer therapy, iron oxide nanoparticles (IONPs) are perhaps the most exciting due to their biocompatibility, biodegradability, size and properties like superparamagnetism. Besides, IONPs are also the only metal oxide nanoparticles approved for clinical use in magnetic resonance imaging (MRI) which is an added advantage for early detection. Therefore in this mini review, we are discussing the developments made in the use of IONPs for breast cancer therapy over the short span of the last five years i.e. 2010−2015. Since late diagnosis and therapy resistance are important drawbacks in breast cancer therapy, the potential of IONPs to overcome these limitations are also evaluated.

Biosensing-by-Learning Direct Targeting Strategy for Enhanced Tumor Sensitization

“…IA,1 , which indicates a uniform magnetic field in the surveillance domain [17], [18] and is dependent on the iterative method described in Section IV. The next location of G 1 at time instant t (1) IT,1 is then updated according to:…”

“…where · 1 denotes the ℓ 1 norm and v 1 t (1) IA,1 is the velocity of G 1 at t (1) IA,1 given the taxicab geometry of the vascular network. Finally, q 1 t (1) IA,1 is the position "quantization" error due to the discrete lattice pattern of the vasculature as illustrated in Fig.…”

“…3) IT. For TDM1, from t (1) IT,1 to t (1) IA,2 , G 1 operates in the IT mode. In the absence of a steering field, G 1 follows a random walk in the lattice (i.e., at each intersection it has the same probability to either move up or to the right) and swims towards various locations in equal observation time intervals along a zigzag pathway,…”

“…IT,1 /(K + 1). Various imaging modalities such as MRI [1] and microwave imaging [12] can be used to detect the magnetic contrast induced by multiple magnetic nanoswimmers simultaneously, which allows for tracking of all the nanoswimmers. In contrast to mathematical computing where the location of a guess input is known exactly, the guess location in the current "natural" computing needs to be estimated.…”

“…IT,2 , · · · , t (1) IT,N , respectively. A new steering vector for G 1 is then computed by following a specified algorithm as discussed in Section IV, which is used to guide the movement of G 1 during the next IA operation at t (2) IA,1 as shown in Fig.…”