Celiac disease detection using a transglutaminase electrochemical immunosensor fabricated on nanohybrid screen-printed carbon electrodes

Celiac disease is a gluten-induced autoimmune enteropathy characterized by the presence of tissue tranglutaminase (tTG) autoantibodies. A disposable electrochemical immunosensor (EI) for the detection of IgA and IgG type anti-tTG autoantibodies in real patient’s samples is presented. Screen-printed carbon electrodes (SPCE) nanostructurized with carbon nanotubes and gold nanoparticles were used as the transducer surface. This transducer exhibits the excellent characteristics of carbon–metal nanoparticle hybrid conjugation and led to the amplification of the immunological interaction. The immunosensing strategy consisted of the immobilization of tTG on the nanostructured electrode surface followed by the electrochemical detection of the autoantibodies present in the samples using an alkaline phosphatase (AP) labelled anti-human IgA or IgG antibody. The analytical signal was based on the anodic redissolution of enzymatically generated silver by cyclic voltammetry. The results obtained were corroborated with a commercial ELISA kit indicating that the electrochemical immunosensor is a trustful analytical screening tool.

Detection of ZnS phases in CZTS thin-films by EXAFS

Copper zinc tin sulfide (CZTS) is a promising Earthabundant thin-film solar cell material; it has an appropriate band gap of ~1.45 eV and a high absorption coefficient. The most efficient CZTS cells tend to be slightly Zn-rich and Cu-poor. However, growing Zn-rich CZTS films can sometimes result in phase decomposition of CZTS into ZnS and Cu2SnS3, which is generally deleterious to solar cell performance. Cubic ZnS is difficult to detect by XRD, due to a similar diffraction pattern. We hypothesize that synchrotron-based extended X-ray absorption fine structure (EXAFS), which is sensitive to local chemical environment, may be able to determine the quantity of ZnS phase in CZTS films by detecting differences in the second-nearest neighbor shell of the Zn atoms. Films of varying stoichiometries, from Zn-rich to Cu-rich (Zn-poor) were examined using the EXAFS technique. Differences in the spectra as a function of Cu/Zn ratio are detected. Linear combination analysis suggests increasing ZnS signal as the CZTS films become more Zn-rich. We demonstrate that the sensitive technique of EXAFS could be used to quantify the amount of ZnS present and provide a guide to crystal growth of highly phase pure films.

An intelligent clustering scheme for distributed intrusion detection in vehicular cloud computing

In recent years, vehicular cloud computing (VCC) has emerged as a new technology which is being used in wide range of applications in the area of multimedia-based healthcare applications. In VCC, vehicles act as the intelligent machines which can be used to collect and transfer the healthcare data to the local, or global sites for storage, and computation purposes, as vehicles are having comparatively limited storage and computation power for handling the multimedia files. However, due to the dynamic changes in topology, and lack of centralized monitoring points, this information can be altered, or misused. These security breaches can result in disastrous consequences such as-loss of life or financial frauds. Therefore, to address these issues, a learning automata-assisted distributive intrusion detection system is designed based on clustering. Although there exist a number of applications where the proposed scheme can be applied but, we have taken multimedia-based healthcare application for illustration of the proposed scheme. In the proposed scheme, learning automata (LA) are assumed to be stationed on the vehicles which take clustering decisions intelligently and select one of the members of the group as a cluster-head. The cluster-heads then assist in efficient storage and dissemination of information through a cloud-based infrastructure. To secure the proposed scheme from malicious activities, standard cryptographic technique is used in which the auotmaton learns from the environment and takes adaptive decisions for identification of any malicious activity in the network. A reward and penalty is given by the stochastic environment where an automaton performs its actions so that it updates its action probability vector after getting the reinforcement signal from the environment. The proposed scheme was evaluated using extensive simulations on ns-2 with SUMO. The results obtained indicate that the proposed scheme yields an improvement of 10 % in detection rate of malicious nodes when compared with the existing schemes.