Brij Niosomes as Carriers for Sustained Drug Delivery─A Fluorescence-Based Approach to Probe the Niosomal Microenvironment.

Niosomes were prepared using a triad of polyoxyethylene alkyl ether surfactants. The focus was to elucidate the effects of varying alkyl chain length and varying hydrophilic headgroups on the structure of the niosomes, with an aim to design niosomes for efficient encapsulation and release of both hydrophobic and hydrophilic drugs. The phase transitions of the surfactants were ascertained by differential scanning calorimetry. It was found that the headgroup has a profound influence on the niosomal bilayer. Fluorescent probes Coumarin 153 (C-153) and 1,6-diphenyl-1,3,5-hexatriene were used to probe the structural integrity of the niosomal bilayer under stress conditions. Other aspects of the niosomes were probed by following the aggregation of the dyes fluorescein (FL) and Nile Red, red edge excitation shift, and fluorescence resonance energy transfer (FRET) between them. Fluorescence lifetime imaging microscopy provides proof of the exact location of the donor and acceptor dyes in the niosomes under FRET condition. It was also shown that the niosomes are efficient "carriers" for entrapment and controlled release of the chemotherapeutic drug 5-fluorouracil. It was found that a rigid niosomal bilayer leads to controlled drug release. The present work is relevant for the future use of these niosomes for cargo entrapment.

Tailoring Niosomes- Implications for Controlled Cargo Release and Function as Nanoreactors.

Nonionic surfactant vesicles (Niosomes) were prepared using polyoxyethylene alkyl ether (Brij 58).The impact of variation of the Brij: cholesterol molar ratio on the niosomal structure was studied. Fluorescence studies performed with the membrane probe 1,6-Diphenyl-1,3,5-triene (DPH) gave important insight on the bilayer integrity of the niosomes in response to environmental perturbations. The aim of the work being assessment of the efficacy of the niosomes as "drug release vehicles", release studies were performed with a xanthene dye Carboxyfluorescein (CF). Further, the vesicles were used as nanoreactors for the synthesis of gold nanoparticles (GNPs) as it is often useful to house nanoparticles in biological /biomimicking environments. Stable, spherical GNPs of diameter 6-10 nm were formed in these vesicles. As the vesicular bilayer mimics the cell membrane, the present work is relevant to the use of the GNPs for diagnostic and therapeutic purpose. It has also been established that fluorescence resonance energy transfer (FRET) effectively occurs between DPH and CF in the niosomes. The FRET studies provide important insight on the location of dyes within the vesicles thus indicating the prospective applications of this fluorescence technique for tracking the location of probes in biomimicking systems which maybe extrapolated to in vivo biological systems in future.

BIOINTMED: integrated biomedical knowledge base with ontologies and clinical trials.

Biomedical data are complex and heterogeneous. An ample reliable quantity of data is important for understanding and exploring the domain. The work aims to integrate biomedical data from various heterogeneous sources like dictionaries or corpus and amalgamate them into a uniform format for easier access by the end-user like biologist, pharmacist, and data scientist. The proposed integrated biomedical knowledge base, BIOINTMED, has 11,299, 12,981, 4428, 61,491, 48,663, and 13,146 unique entities for drugs, diseases, targets, genes, biomedical pathways, and adverse events, respectively. The uniform aggregated collection is also explored to study the interaction among these entity pairs. Finally, a complete statistical analysis of the consolidated biomedical entities is provided.

"Theranostic" role of bile salt-capped silver nanoparticles - gall stone/pigment stone disruption and anticancer activity.

Silver nanoparticles (AgNPs) have been synthesized in situ in micelles formed by the bile salt sodium deoxycholate (NaDC). The AgNPs exhibit "green" fluorescence. It has been shown in the present study that they can disrupt the components of gall stones/pigment stones. This unique ability of the AgNPs has been observed upon detailed study of the interaction between the endobiotic pigment bilirubin (BR) and bile salt (NaDC). In addition, these AgNPs show significant cytotoxicity towards the breast cancer cells (MCF-7). Thus the AgNPs synthesized in this work show important physiological activity and can serve as prospective "Theranostic Materials" in future. Their green fluorescence bears relevance to future diagnostic applications while their anticancer activity and disruptive action upon BR aggregates in bile salt micelles is extremely important for therapeutic purpose. This is the first report of the use of metal nanoparticles in disruption of components of gall stones/pigment stones and thus the present work has very important physiological significance. The detailed spectral studies indicate that bile salts increase the dimerization of BR which could be linked to increased solubilisation of BR in bile salt media and consequent bile stone/pigment stone formation. Importantly, an increase in red fluorescence was observed (upon dimerization of BR), which is important for cancer detection and studying the metabolism of biological tissues.

Feature selection techniques for maximum entropy based biomedical named entity recognition.

Named entity recognition is an extremely important and fundamental task of biomedical text mining. Biomedical named entities include mentions of proteins, genes, DNA, RNA, etc which often have complex structures, but it is challenging to identify and classify such entities. Machine learning methods like CRF, MEMM and SVM have been widely used for learning to recognize such entities from an annotated corpus. The identification of appropriate feature templates and the selection of the important feature values play a very important role in the success of these methods. In this paper, we provide a study on word clustering and selection based feature reduction approaches for named entity recognition using a maximum entropy classifier. The identification and selection of features are largely done automatically without using domain knowledge. The performance of the system is found to be superior to existing systems which do not use domain knowledge.