Cycloheximide, a protein synthesis inhibitor, was encapsulated in cross-linked gelatin nanoparticles (Type B, Bovine skin, 75 Bloom) of 168nm diameter with 26% entrapment efficiency. In-vitro release kinetics of the drug from the nanoparticles was done in phoshate buffer saline (PBS) at pH 7.4 and pH 5.8. The release kinetics showed a bi-phasic curve. Interestingly, the release of drug is approx 90% in acidic pH as compared to 50% release in neutral pH. The particle size was determined by Dynamic Light Scattering (DLS) technique, and size distribution spectra at different pH were observed to vary inversely with increase in pH. These drug loaded nanoparticles were found to be stable in whole blood showing negligible haemolysis. Cytotoxicity in HBL-100 and MCF-7, breast cancer cell lines was done in a 24-72 hrs assay, showing increased anti-tumour activity over a period of time indicating slow release. Dose dependant cytotoxicity was observed after 24 hours upto 72 hours of incubation of nanoparticles while the drug per se ( < 4μg) showed 93% toxicity within 24 hours. Phase contrast microscopy of nanoparticle-cell interaction, clearly indicated aggregation along the lipid cell-membrane. Electron Microscopy (TEM, SEM) studies revealed its size and spherical shape. The stability of the particle, the slow and controlled release of drug from the gelatin nanoparticles indicate that it is a good candidate to deliver bio-pharmaceuticals. These behave as “;intelligent” carriers for drug delivery, and can be exploited to empty their drug load in acidic medium. The paper focuses on the release kinetics of the gelatin nanoparticles that can be successfully exploited to treat solid tumors.

S-nitrosylation of proteins occurs as a consequence of the derivatization of cysteine thiols with nitric oxide (NO) and is often associated with diseases and protein malfunction. Aberrant S-nitrosylation, in addition to other genetic and epigenetic factors, has gained rapid importance as a prime cause of various metabolic, respiratory, and cardiac disorders, with a major emphasis on cancer and neurodegeneration. The S-nitrosoproteome, a term used to collectively refer to the diverse and dynamic repertoire of S-nitrosylated proteins, is relatively less explored in the field of redox biochemistry, in contrast to other covalently modified versions of the same set of proteins. Advancing research is gradually unveiling the enormous clinical importance of S-nitrosylation in the etiology of diseases and is opening up new avenues of prompt diagnosis that harness this phenomenon. Ever since the discovery of the two robust and highly conserved S-nitrosoglutathione reductase and thioredoxin systems as candidate denitrosylases, years of rampant speculation centered around the identification of specific substrates and other candidate denitrosylases, subcellular localization of both substrates and denitrosylases, the position of susceptible thiols, mechanisms of S-denitrosylation under basal and stimulus-dependent conditions, impact on protein conformation and function, and extrapolating these findings towards the understanding of diseases, aging and the development of novel therapeutic strategies. However, newer insights in the ever-expanding field of redox biology reveal distinct gaps in exploring the crucial crosstalk between the redoxins/major denitrosylase systems. Clarifying the importance of the functional overlap of the glutaredoxin, glutathione, and thioredoxin systems and examining their complementary functions as denitrosylases and antioxidant enzymatic defense systems are essential prerequisites for devising a rationale that could aid in predicting the extent of cell survival under high oxidative/nitrosative stress while taking into account the existence of the alternative and compensatory regulatory mechanisms. This review thus attempts to highlight major gaps in our understanding of the robust cellular redox regulation system, which is upheld by the concerted efforts of various denitrosylases and antioxidants.

Type 2 diabetes (T2D) is a common multi-factorial disease that is primarily ac-counted to ineffective insulin action in lowering blood glucose level and later escalates to impaired insu-lin secretion by pancreatic β cells. Deregulation in insulin signaling to its target organs is attributed to this disease phenotype. Various genome-wide microarray studies from multiple insulin responsive tis-sues have been conducted in past but due to inherent noise in microarray data and heterogeneity in dis-ease etiology; reproduction of prioritized pathways/genes is very low across various studies. In this study, we aim to identify consensus signaling and metabolic pathways through system level meta-analysis of multiple expression-sets to elucidate T2D pathobiology. We used 'R', an open source statistical environment, which is routinely used for Microarray data analysis particularly using special sets of packages available at Bioconductor. We primarily focused on gene-set analysis methods to elucidate various aspects of T2D. Literature-based evidences have shown the success of our approach in exploring various known aspects of diabetes pathophysiology. Our study stressed the need to develop novel bioinformatics workflows to advance our understanding further in insulin signaling.