Search

Background: The 16 kDa heat shock protein (HSP) is an immuno-dominant antigen, used in diagnosis of infectious Mycobacterium tuberculosis (M.tb.) causing tuberculosis (TB). Its use in serum-based diagnostics is limited, but for the direct identification of M.tb. bacteria in sputum or cultures it may represent a useful tool. Recently, a broad set of twelve 16 kDa specific heavy chain llama antibodies (VHH) has been isolated, and their utility for diagnostic applications was explored. Methodology/Principal Findings: To identify the epitopes recognized by the nine (randomly selected from a set of twelve 16 kDa specific VHH antibodies) distinct VHH antibodies, 14 overlapping linear epitopes (each 20 amino acid long) were characterized using direct and sandwich ELISA techniques. Seven out of 14 epitopes were recognized by 8 out of 9 VHH antibodies. The two highest affinity binders B-F10 and A-23 were found to bind distinct epitopes. Sandwich ELISA and SPR experiments showed that only B-F10 was suitable as secondary antibody with both B-F10 and A-23 as anchoring antibodies. To explain this behavior, the epitopes were matched to the putative 3D structure model. Electrospray ionization time-of-flight mass spectrometry and size exclusion chromatography were used to determine the higher order conformation. A homodimer model best explained the differential immunological reactivity of A-23 and B-F10 against heat-treated M.tb. lysates. Conclusions/Significance: The concentrations of secreted antigens of M.tb. in sputum are too low for immunological detection and existing kits are only used for identifying M.tb. in cultures. Here we describe how specific combinations of VHH domains could be used to detect the intracellular HSP antigen. Linked to methods of pre-concentrating M.tb. cells prior to lysis, HSP detection may enable the development of protein-based diagnostics of sputum samples and earlier diagnosis of diseases. [ABSTRACT FROM AUTHOR]

This study presents the classification of malaria-prone zones based on (a) meteorological factors, (b) demographics and (c) patient information. Observations are performed on extended features in dataset over the spiking and non-spiking classifiers including Quadratic Integrate and Fire neuron (QIFN) model as a benchmark. As per research studies, parasite transmission is highly dependent on the (i) stagnant water, (ii) population of area and the (iii) greenery of the locality. Considering these factors, three more attributes were added to the existing novel dataset and comparison on the results is presented. For four feature dataset, QIFN exhibited an accuracy of 97.08% in K10 protocol, and with extended dataset; QIFN yields an accuracy of 99.58% in K10 protocol. The benchmarking results showed reliability and stability. There is 12.47% improvement against multilayer perceptron (MLP) and 5.39% against integrate-and-fire neuron (IFN) model. The QIFN model performed the best over the conventional classifiers for deciphering the risk of acquiring malaria in different geographical regions worldwide.

Malaria is an infectious disease caused by parasitic protozoans of the Plasmodium family. These parasites are transmitted by mosquitos which are common in certain parts of the world. Based on their specific climates, these regions have been classified  as low and high risk regions using a backpropagation neural network (BPNN). However, this approach yielded low performance and stability necessitating development of a more robust model. We hypothesized that by spiking neuron models in simulating the characteristics of a neuron, which when embedded with a BPNN, could improve the performance for the assessment of malaria prone regions. To this end, we created an inter-spike interval (ISI)-based BPNN (ISI-BPNN) architecture that uses a single-pass spiking learning strategy and has a parallel structure that is useful for non-linear regression tasks. Existing malaria dataset comprised of 1296 records, that met these attributes, were used. ISI-BPNN showed superior performance, and a high accuracy. The benchmarking results showed reliability and stability and an improvement of 11.9% against a multilayer perceptron and 9.19% against integrate-and-fire neuron models. The ISI-BPNN model is well suited for deciphering the risk of acquiring malaria as well as other diseases in prone regions of the world.