Cytotoxicity Evaluation of Carbon Nanotubes for Biomedical and Tissue Engineering Applications

Carbon nanotubes (CNTs) are one of the most studied allotropes of carbon nanomaterials. The exceptional chemical and physical properties of CNTs make them potential candidates for several applications such as electrical, gene therapy, biosensors, and drug delivery applications. However, the toxicity of CNTs has been a major concern for their use in tissue engineering and biomedical applications. In this chapter, we present an overview of carbon nanotubes in biomedical and tissue engineering applications. We discussed various factors including impurities, length, agglomeration, and size of CNTs that cause toxicity of CNTs. Further, other toxic methods are also examined, and possible ways to overcome these challenges have been discussed

PREFORMULATION STUDIES FOR THE PREPARATION OF AMORPHOUS SOLID DISPERSIONS

The major challenges in the formulation of amorphous solid dispersions (ASDs) using hot-melt extrusion (HME) are the selection of an ideal polymeric carrier, optimization of HME processing conditions, and screening of the physical stability of the ASDs. Addressing these challenges using traditional approaches require extensive experimentation and large amounts of active pharmaceutical ingredients (API) which may not be feasible during the initial stages of product development. Therefore, there is a need to develop material-sparing techniques for the successful formulation of ASDs. The objective of the present study was to develop material-sparing techniques that can be used as pre-formulation tool during the formulation of ASDs. For this purpose, mefenamic acid (MFA) was used as a model drug and four chemically distinct polymers with close values of the solubility parameters, viz. Kollidon® VA64, Soluplus®, Pluronic® F68, and Eudragit® EPO, were used as polymeric carriers. The selection of an ideal polymer was carried out based on the solubility parameter approach, melting point depression method, thermodynamic phase diagrams, and Gibbs free energy plots. Then the HME processing conditions were determined based on a material-sparing technique using differential scanning calorimeter (DSC). The physical stability of the ASDs was estimated using the modified Avarami equation. Based on the results of the melting point depression, thermodynamic phase diagrams and Gibbs free energy plots, Eudragit® EPO was found to be an ideal polymer for the preparation of amorphous solid dispersion formulation of mefenamic acid. The design space for HME determined using DSC method showed that when 20% drug loaded MFA-EPO blends was heated at a rate of 5.5 °C/min to a temperature of 146 °C, the resulting ASD contained a residual crystallinity of 13.6% and drug degradation of 3.8%. The physical stability of the MFA-EPO ASDs determined using a modified Avarami equation showed that the rate of recrystallization changed significantly with the change in process temperature as compared to the change in the relative humidity. The study results show that the time frame and experiments required in the formulation of ASDs can be significantly reduced by using the material-sparing techniques developed based on the theoretical and experimental approaches

Characterization of Arabidopsis thaliana mutants lacking a jumonji domain containing histone demethylase and a set domain containing histone methyl transferase

Condensation of chromatin and alteration of chemical groups in the proteins around which the DNA is wrapped play major role in regulation of transcription. Histones are basic proteins rich in arginine and lysine residues which form the nucleosomal core. Histone modifications like acetylation, methylation, phosphorylation, etc. have broadened the horizon for researchers to study epigenetics more in detail. Histone methyl transferases and histone demethyl transferases are enzymes which add or remove methyl groups on histone lysine and arginine residues respectively. In this study a jumonji domain containing putative histone demethyltransferase has been shown to be responsible in controlling flowering phenotype in Arabidopsis thaliana. The knocked out mutants for this gene (JMJ14) showed an early flowering phenotype along with elevated levels of FT transcript (Flowering locus T, gene responsible for controlling the flowering time in Arabidopsis thaliana). We show that methylation was altered on H3K36 in the FT ene in the mutants using ChIP (chromatin immunoprecipitation experiments). The possible role of SDG8 gene, a histone methyl transferase in ABA signaling was also studied during the research. A SET domain containing Sdg8 (group 8 methyltransferase) mutant was found to be responsible for ABA signaled altered root growth in Arabidopsis thaliana. The cell number and cell size in roots decreased in both meristematic and elongation zones leading to decrease in root size in sdg8 mutants and number of root hairs increased when treated with Abscisic acid, a plant hormone. In this part of study, as part of an interaction between epigenetics and gene regulation, it was observed that a putative histone demethylase gene, JMJ14 was responsible for regulating the flowering time by controlling the expression of FT and SDG8 played a role in altered root growth in response to ABA in Arabidopsis thaliana. Further studies on these genes could lead to generation of commercial crops with phenotypes that would increase the plant productivity and be beneficial agronomically

Characterization of Arabidopsis thaliana mutants lacking a jumonji domain containing histone demethylase and a set domain containing histone methyl transferase

Condensation of chromatin and alteration of chemical groups in the proteins around which the DNA is wrapped play major role in regulation of transcription. Histones are basic proteins rich in arginine and lysine residues which form the nucleosomal core. Histone modifications like acetylation, methylation, phosphorylation, etc. have broadened the horizon for researchers to study epigenetics more in detail. Histone methyl transferases and histone demethyl transferases are enzymes which add or remove methyl groups on histone lysine and arginine residues respectively. In this study a jumonji domain containing putative histone demethyltransferase has been shown to be responsible in controlling flowering phenotype in Arabidopsis thaliana. The knocked out mutants for this gene (JMJ14) showed an early flowering phenotype along with elevated levels of FT transcript (Flowering locus T, gene responsible for controlling the flowering time in Arabidopsis thaliana). We show that methylation was altered on H3K36 in the FT ene in the mutants using ChIP (chromatin immunoprecipitation experiments). The possible role of SDG8 gene, a histone methyl transferase in ABA signaling was also studied during the research. A SET domain containing Sdg8 (group 8 methyltransferase) mutant was found to be responsible for ABA signaled altered root growth in Arabidopsis thaliana. The cell number and cell size in roots decreased in both meristematic and elongation zones leading to decrease in root size in sdg8 mutants and number of root hairs increased when treated with Abscisic acid, a plant hormone. In this part of study, as part of an interaction between epigenetics and gene regulation, it was observed that a putative histone demethylase gene, JMJ14 was responsible for regulating the flowering time by controlling the expression of FT and SDG8 played a role in altered root growth in response to ABA in Arabidopsis thaliana. Further studies on these genes could lead to generation of commercial crops with phenotypes that would increase the plant productivity and be beneficial agronomically

Gaussian Mixture Approach to Detect Drift

Historically it has been difficult to measure the deviation in the notion of a concept. Several schemes have been proposed to attack this challenging problem [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]. The central notion of all these efforts is to detect the change point where the data mining model deviates significantly with respect to the data characteristics that it was trained or built on. The process of detecting such change points is often termed as concept drift. Current state of algorithms assume attribute independence, view the problem as a supervised learning problem and also need tagged data. The proposed algorithm does not make any assumption among attribute independence and uses the covariance summary to detect concept drift in an unsupervised setting. The algorithm proposed in this thesis monitors the underlying characteristics of the input data, maintains data summaries of the various snapshots in time and utilizes effective distance metrics to determine when concept drifts. The technique was evaluated against synthetic and real data sets