Dual Drug Conjugate Loaded Nanoparticles for the Treatment of Cancer

Two antineoplastic agents, Imatinib (IM) and 5-Fluorouracil (FU) were conjugated by hydrolysable linkers through an amide bond and entrapped in polymeric Human Serum Albumin (HSA) nanoparticles. The presence of dual drugs in a common carrier has the advantage of reaching the site of action simultaneously and acting at different phases of the cell cycle to arrest the growth of cancer cells before they develop chemoresistance. The study has demonstrated an enhanced anticancer activity of the conjugate, and conjugate loaded stealth HSA nanoparticles (NPs) in comparison to the free drug in A-549 human lung carcinoma cell line and Zebra fish embryos (Danio rerio). Hydrolysability of the conjugate has also been demonstrated with complete hydrolysis being observed after 12 h. In vivo pharmacodynamics study in terms of tumor volume and pharmacokinetics in mice for conjugate (IM-SC-FU) and conjugate loaded nanoparticles showed significant anti-cancer activity. The other parameters evaluated were particle size (86nm), Poly Dispersive Index (PDI) (0.209), zeta potential (-49mV), drug entrapment efficiency (96.73%) and drug loading efficiency (89%). Being in stealth mode gives the potential for the NPs to evade Reticulo-Endothelial system (RES), achieve passive targeting by Enhanced Permeation Retention (EPR) effect with controlled release of the therapeutic agent. As the conjugate cleaves into individual drugs in the tumor environment, this promises better suppression of cancer chemoresistance by delivering dual drugs with different modes of action at the same site, thereby synergistically inhibiting the growth of cancerous tissue.

Organization dependent collective magnetic properties of secondary nanostructures with differential spatial ordering and magnetic easy axis orientation

Achieving control on the formation of different organization states of magnetic nanoparticles is crucial to harness their organization dependent physical properties in desired ways. In this study, three organization states of iron oxide nanoparticles (gamma-Fe2O3), defining as (i) assembly (ii) network aggregate and (iii) cluster, have been developed by simply changing the solvent evaporation conditions. All three systems have retained the same phase and polydispersity of primary particles. Magnetic measurements show that the partial alignment of the easy axes of the particles in the network system due to the stacking aggregation morphology can result in significant enhancement of the coercivity and remanence values, while the opposite is obtained for the cluster system due to the random orientation of easy axes. Partial alignment in the aggregate system also results in noticeable non -monotonic field dependence of ZFC peak temperature (TpeaB). The lowest value of the blocking temperature (TB) for the cluster system is related to the lowering of the effective anisotropy due to the strongest demagnetizing effect. FC (Field cooled) memory effect was observed to be decreasing with the increasing strength of dipolar interaction of organization states. Therefore, the stacking aggregation and the cluster formation are two interesting ways of magnetic nanoparticles organization for modulating collective magnetic properties significantly, which can have renewed application potentials from recording devices to biomedicine. (C) 2016 Elsevier B.V. All rights reserved.

Cost effective campaigning in social networks

Campaigners are increasingly using online social networking platforms for promoting products, ideas and information. A popular method of promoting a product or even an idea is incentivizing individuals to evangelize the idea vigorously by providing them with referral rewards in the form of discounts, cash backs, or social recognition. Due to budget constraints on scarce resources such as money and manpower, it may not be possible to provide incentives for the entire population, and hence incentives need to be allocated judiciously to appropriate individuals for ensuring the highest possible outreach size. We aim to do the same by formulating and solving an optimization problem using percolation theory. In particular, we compute the set of individuals that are provided incentives for minimizing the expected cost while ensuring a given outreach size. We also solve the problem of computing the set of individuals to be incentivized for maximizing the outreach size for given cost budget. The optimization problem turns out to be non trivial; it involves quantities that need to be computed by numerically solving a fixed point equation. Our primary contribution is, that for a fairly general cost structure, we show that the optimization problems can be solved by solving a simple linear program. We believe that our approach of using percolation theory to formulate an optimization problem is the first of its kind. (C) 2016 Elsevier B.V. All rights reserved.