Facile one-pot synthesis of CuO nanospheres: Sensitive electrochemical determination of hydrazine in water effluents

Hydrazine (HZ) is massively used in several industrial applications. Adsorption of HZ through human skin creates carcinogenicity by disturbing the human organ system and thus, the quantification of HZ levels in environmental water samples is highly needed. The present work describes the short-term development of copper oxide nanospheres (CuO NS) by one-step wet chemical approach and their implementation on glassy carbon electrode (GCE) for the sensitive and selective quantification of the environmentally hazardous HZ. The CuO NS formation was identified by X-ray diffraction (XRD), field-emission scanning electron microscopy (FE-SEM), transmission electron microscopy (TEM) and UV-visible spectroscopy. SEM images exhibited the uniform CuO NS with an average size of 85 nm. The linker-free CuO NS modified GCE offered high electrocatalytic activity against HZ determination by showing the linear range determination in the range of 0.5 to 500 µM, with the detection limit of 63 nM (S/N=3), and sensitivity of 894.28 µA mM-1 cm-2. Further, the developed HZ sensor displayed excellent repeatability and reproducibility and was successfully exploited for the determination of HZ in real environmental samples, implying that GCE/CuO-NS is a confident and low-cost electrochemical platform for HZ determination

UV photodecomposition of zinc acetate for the growth of ZnO nanowires

The thermal annealing of zinc precursors to form suitable seed layers for the growth of ZnO nanowires is common. However, the process is relatively long and involves high temperatures which limit substrate choice. In this study the use of a low temperature, ultra-violet (UV) exposure is demonstrated for photodecomposition of zinc acetate precursors to form suitable seed layers. Comparisons are made between ZnO nanowire growth performed on seed layers produced through thermal annealing and exposure to UV. The dependence of growth density and nanowire diameter on UV exposure time is investigated. Growth quality is confirmed with energy dispersive x-ray (EDX) and x-ray diffraction analyses. The chemical composition of the exposed layers is investigated with EDX and x-ray photoelectron spectroscopy (XPS). Atomic force microscopy (AFM) is utilized to investigate morphological changes with respect to UV exposure. The diameter and density of the resultant growth was found to be strongly dependent on the UV exposure time. UV exposure times of only 25–30 s led to maximum density of growth and minimum diameter, significantly faster than thermal annealing. EDX, XPS and AFM analyses of the seed layers confirmed decomposition of the zinc precursor and morphological changes which influenced the growth

CyclinPred: A SVM-Based Method for Predicting Cyclin Protein Sequences

Functional annotation of protein sequences with low similarity to well characterized protein sequences is a major challenge of computational biology in the post genomic era. The cyclin protein family is once such important family of proteins which consists of sequences with low sequence similarity making discovery of novel cyclins and establishing orthologous relationships amongst the cyclins, a difficult task. The currently identified cyclin motifs and cyclin associated domains do not represent all of the identified and characterized cyclin sequences. We describe a Support Vector Machine (SVM) based classifier, CyclinPred, which can predict cyclin sequences with high efficiency. The SVM classifier was trained with features of selected cyclin and non cyclin protein sequences. The training features of the protein sequences include amino acid composition, dipeptide composition, secondary structure composition and PSI-BLAST generated Position Specific Scoring Matrix (PSSM) profiles. Results obtained from Leave-One-Out cross validation or jackknife test, self consistency and holdout tests prove that the SVM classifier trained with features of PSSM profile was more accurate than the classifiers based on either of the other features alone or hybrids of these features. A cyclin prediction server- CyclinPred has been setup based on SVM model trained with PSSM profiles. CyclinPred prediction results prove that the method may be used as a cyclin prediction tool, complementing conventional cyclin prediction methods

Manipulation of Costimulatory Molecules by Intracellular Pathogens: Veni, Vidi, Vici!!

Some of the most successful pathogens of human, such as Mycobacterium tuberculosis (Mtb), HIV, and Leishmania donovani not only establish chronic infections but also remain a grave global threat. These pathogens have developed innovative strategies to evade immune responses such as antigenic shift and drift, interference with antigen processing/presentation, subversion of phagocytosis, induction of immune regulatory pathways, and manipulation of the costimulatory molecules. Costimulatory molecules expressed on the surface of various cells play a decisive role in the initiation and sustenance of immunity. Exploitation of the “code of conduct” of costimulation pathways provides evolutionary incentive to the pathogens and thereby abates the functioning of the immune system. Here we review how Mtb, HIV, Leishmania sp., and other pathogens manipulate costimulatory molecules to establish chronic infection. Impairment by pathogens in the signaling events delivered by costimulatory molecules may be responsible for defective T-cell responses; consequently organisms grow unhindered in the host cells. This review summarizes the convergent devices that pathogens employ to tune and tame the immune system using costimulatory molecules. Studying host-pathogen interaction in context with costimulatory signals may unveil the molecular mechanism that will help in understanding the survival/death of the pathogens. We emphasize that the very same pathways can potentially be exploited to develop immunotherapeutic strategies to eliminate intracellular pathogens

Sequence and Structure Signatures of Cancer Mutation Hotspots in Protein Kinases

Protein kinases are the most common protein domains implicated in cancer, where somatically acquired mutations are known to be functionally linked to a variety of cancers. Resequencing studies of protein kinase coding regions have emphasized the importance of sequence and structure determinants of cancer-causing kinase mutations in understanding of the mutation-dependent activation process. We have developed an integrated bioinformatics resource, which consolidated and mapped all currently available information on genetic modifications in protein kinase genes with sequence, structure and functional data. The integration of diverse data types provided a convenient framework for kinome-wide study of sequence-based and structure-based signatures of cancer mutations. The database-driven analysis has revealed a differential enrichment of SNPs categories in functional regions of the kinase domain, demonstrating that a significant number of cancer mutations could fall at structurally equivalent positions (mutational hotspots) within the catalytic core. We have also found that structurally conserved mutational hotspots can be shared by multiple kinase genes and are often enriched by cancer driver mutations with high oncogenic activity. Structural modeling and energetic analysis of the mutational hotspots have suggested a common molecular mechanism of kinase activation by cancer mutations, and have allowed to reconcile the experimental data. According to a proposed mechanism, structural effect of kinase mutations with a high oncogenic potential may manifest in a significant destabilization of the autoinhibited kinase form, which is likely to drive tumorigenesis at some level. Structure-based functional annotation and prediction of cancer mutation effects in protein kinases can facilitate an understanding of the mutation-dependent activation process and inform experimental studies exploring molecular pathology of tumorigenesis

Negative potential induced growth of surfactant-free CuO nanostructures on Al-C substrate: A dual in-line sensor for biomarkers of diabetes and oxidative stress

Identifying a non-enzymatic sensor electrocatalyst for the accurate determination of biomolecules is a paramount task. The weird morphology of the nanomaterial may exhibit the remarkable activity to enhance the sensitivity of the electrode, and hence, tuning the shape of the nanomaterials is one of the important criteria for electrochemical sensors. Thus, this study sought to fabricate a non-enzymatic sensor with different CuO morphologies by an eco-friendly and facile approach. Different surfactant-free CuO nanostructures were fabricated on the highly conductive flexible aluminum–carbide substrate for the electrochemical assessment of glucose and H2O2 in human fluids. The CuO nanostructures were initially electrodeposited at −0.1, −0.3, −0.5, and −0.7 V and then calcined in air at 120 °C for 2 h. Different CuO nanostructures with a polygonal and cactus-like morphology were observed in scanning electron microscopic images. The as-fabricated electrodes were also characterized by X-ray diffraction (XRD), X-ray photoelectron spectroscopy, and electrochemical impedance spectroscopy analysis. Texture coefficients (TC) of different CuO nanostructures were calculated from XRD analysis, and the CuO nanostructures grown at −0.5 V exhibited the highest TC with their texture along the (022) plane. The electrodes grown with different CuO nanostructures exhibited shape-dependent electrocatalytic activity against glucose and H2O2, and the cactus-like CuO nanostructures grown at −0.5 V showed superior electrochemical behavior compared to that of the other nanostructures by showing superior sensitivities of 3892.6 and 2015.7 μA mM–1 cm–2, respectively, against glucose and H2O2. In addition, the sensor grown with a cactus-like CuO nanostructure exhibited high selectivity, storage capability, and good practicability. This sensor proved to be practical by determining glucose and H2O2 levels in human fluids. It is believed that the proposed electrochemical sensor will have a strong impact in the dual in-line sensing of biomarkers in both biological and clinical fields in the near future

Aqueous two-phase extraction coupled with ultrafiltration for purification of amyloglucosidase

Aqueous two phase extraction (ATPE) in combination with ultrafiltration was employed for concentration and purification of amyloglucosidase produced by solid state fermentation. After extraction (with water) from dry moldy bran the dilute enzyme extract was concentrated by ATPE in a polyethylene glycol (PEG)/maltodextrin (MDX) system. The enzyme in the top PEG rich phase was then extracted into a Na2HPO4 rich bottom phase and further concentrated by ultrafiltration. The partitioning behavior of amyloglucosidase was examined in PEG/MDX, PEG/Na2SO4, PEG/Na2HPO4, PEG/KH2PO4 aqueous two phase systems. Effect of buffering salts such as NaCl, Na2HPO4, KH2PO4 and Na2SO4 on the partitioning behavior of enzyme was studied in PEG/MDX system. Maximum partitioning of amyloglucosidase was seen with KH2PO4 (m = 18.1). A two stage ATPE employing PEG/MDX (buffered with KH2PO4) and PEG/Na2HPO4 systems, followed by ultrafiltration has resulted in an overall recovery of 78.4% with 3.1 fold purification and 9.4 fold concentration of the enzyme

Interaction of transport resistances with biochemical reaction in packed bed solid state fermenters: the effect of gaseous concentration gradients

Mass transfer plays an important role in solid state fermentation (SSF) systems. Earlier work on SSF in tray bioreactors indicated that steep gaseous concentration gradients developed within the substrate bed, owing to mass transfer resistances, which may adversely affect the bioreactor performance. For all practical purposes these gradients have been eliminated using a packed bed column bioreactor with forced aeration. Gaseous concentrations (oxygen and carbon dioxide) and enzyme activities were measured at various bed heights for various air flow rates during the course of fermentation. The results indicated that concentration gradients were decreased effectively by increasing air flow rate. For example, the actual oxygen and carbon dioxide concentration gradients reduced from 0.07% (v/v) cm−1 and 0.023% (v/v) cm−1 to 0.007% (v/v) cm−1 and 0.0032% (v/v) cm−1 respectively when the air flow rate was increased from 5 dm3 min−1 to 25 dm3 min−1. This resulted in an overall improvement in the performance of the bioreactor in terms of enzyme production

Estimation of K_La in solid-state fermentation using a packed-bed bioreactor

The direct method based on inlet- and outlet-gas concentrations in submerged fermentation (SmF) was adopted for estimating overall oxygen-transfer coefficients (K_La) in a packed-bed solid-state fermenter. The variation in K_La during fermentation at different air-flow rates was studied. The values of K_La were higher than those found in SmF. The moisture content was found to affect oxygen-transfer rates and coefficients. The value of K_La increased with an increase in air-flow rate in the range 0–15 litre/min but decreased at higher air-flow rates (15–25 litre/min). This could be attributed to the decrease in the moisture content of the substrate at high flow rates