NP [Hexyl-Amine]

The design and development of drug-delivery nanocarriers with high loading capacity, excellent biocompatibility, targeting ability and controllability have been the ultimate goal of the biomedical research community. In this work, we have reported the synthesis and characterization of novel and smart thermoresponsive polymer coated and Fe(3)O(4)embedded hollow mesoporous silica (HmSiO(2)) based multifunctional superparamagnetic nanocarriers for the delivery of doxorubicin (Dox) for cancer treatment. P(NIPAM-MAm) coated and Fe(3)O(4)nanoparticle (NP) embedded hollow mesoporous silica nanocomposite (HmSiO(2)-F-P(NIPAM-MAm)) was prepared by thein situpolymerization of NIPAM and MAm monomers on the surface of hollow mesoporous silica NPs (HmSiO(2)) in the presence of Fe(3)O(4)NPs, oxidizer and crosslinker. TEM analysis showed nearly spherical morphology of HmSiO(2)-F-P(NIPAM-MAm) nanocarrier with a diameter in the range of 100-300 nm. The coating of P(NIPAM-MAm) layer and embedding of Fe(3)O(4)NPs on the surface of the HmSiO(2)NPs was revealed by HRTEM analysis. XRD and FTIR analysis also confirmed the presence of P(NIPAM-MAm) shells and Fe(3)O(4)NPs on hollow mesoporous silica NPs. VSM analysis suggested the superparamagnetic nature of HmSiO(2)-F-P(NIPAM-MAm) nanocarrier. DSC analysis of HmSiO(2)-F-P(NIPAM-MAm) nanocarrier showed a phase transition at the temperature of similar to 38 degrees C. The prepared HmSiO(2)-F-P(NIPAM-MAm) nanocarrier was investigated for its suitability for drug-delivery application using doxorubicin as the model drug by anin vitromethod. The encapsulation efficiency and encapsulation capacity were found to be 95% and 6.8%, respectively. HmSiO(2)-F-P(NIPAM-MAm)-Dox has shown a pH and temperature-dependent Dox release profile. A relatively faster release of Dox from the nanocarrier was observed at temperature above the lower critical solution temperature (LCST) than below the LCST. HmSiO(2)-F-P(NIPAM-MAm) nanocarrier was found to be biocompatible in nature.In vitrocytotoxicity studies against Hela cells suggested that the HmSiO(2)-F-P(NIPAM-MAm)-Dox nanocomposite nanocarrier has good anticancer activity.In vitrocellular uptake study of HmSiO(2)-F-P(NIPAM-MAm)-Dox nanocomposite nanocarrier demonstrated its good internalisation ability into Hela cells. Thus, the prepared nanocomposites show potential as nanocarrier for targeted and controlled drug delivery for cancer treatment.

Privacy issues during data publishing is an increasing concern of involved entities. The problem is addressed in the field of statistical disclosure control with the aim of producing protected datasets that are also useful for interested end users such as government agencies and research communities. The problem of producing useful protected datasets is addressed in multiple computational privacy models such as k-anonymity in which data is clustered into groups of at least k members. Microaggregation is a mechanism to realize k-anonymity. The objective is to assign records of a dataset to clusters and replace the original values with their associated cluster centers which are the average of assigned values to minimize information loss in terms of the sum of within group squared errors (SSE). While the problem is shown to be NP-hard in general, there is an optimal polynomial-time algorithm for univariate datasets. This paper shows that the assignment of the univariate microaggregation algorithm cannot produce optimal partitions for integer observations where the computed centroids have to be integer values. In other words, the integrality constraint on published quantities has to be addressed within the algorithm steps and the optimal partition cannot be attained using only the results of the general solution. Then, an effective method that considers the constraint is proposed and analyzed which can handle very large numerical volumes. Experimental evaluations confirm that the developed algorithm not only produces more useful datasets but also is more efficient in comparison with the general optimal univariate algorithm. (C) 2020 ISC. All rights reserved.