Development and validation of a new PCR optimization method by combining experimental design and artificial neural network

Generation of ssDNA aptamers as diagnostic tool for Newcastle avian virus

Aptamers are short single-stranded DNA (ssDNA), RNA or synthetic XNA molecules, which are used as a class of affinity binders recognizing target molecules with a very high affinity and specificity. The aim of this study was to generate and characterize ssDNA aptamers for the detection of Newcastle disease virus (NDV). These aptamers were selected using systematic evolution of ligands by exponential enrichment (SELEX) in combination with quantitative high-throughput DNA sequencing. After three rounds of selections, a highly enriched ssDNA pool was sequenced, and the results were analyzed using FASTAptamer Toolkit. Sequencing reads were sorted by copy numbers and clustered into groups, according to their sequence homology. Top aptameric sequences were used to develop a sandwich enzymatic linked aptamer assay (ELAA) for rapid and sensitive detection of NDV in farm samples. The selected aptamers have an affinity within the nanomolar range, and a high specificity with no cross-reactivity towards other avian viruses. Following optimization of the sandwich ELAA method, the results demonstrated that both selected aptamers Apt_NDV01 and Apt_NDV03 with dissociation constant values of 31 nM and 78.1 nM, respectively, showed the highest specificity and affinity for NDV detection. The ELAA results were verified by quantitative real-time PCR, demonstrating strong concordance, and showing outstanding accuracy for detection of NDV in field sample. In summary, combination of SELEX with high-throughput DNA sequencing allowed rapid screening and selection of aptamers. The selected aptamers allowed recognition of NDV with high affinities. This is the first report that uses a validated sandwich ELAA for rapid and specific detection of NDV in poultry samples.

Optimization of multiplex quantitative polymerase chain reaction based on response surface methodology and an artificial neural network-genetic algorithm approach

Multiplex quantitative polymerase chain reaction (qPCR) has found an increasing range of applications. The construction of a reliable and dynamic mathematical model for multiplex qPCR that analyzes the effects of interactions between variables is therefore especially important. This work aimed to analyze the effects of interactions between variables through response surface method (RSM) for uni- and multiplex qPCR, and further optimize the parameters by constructing two mathematical models via RSM and back-propagation neural network-genetic algorithm (BPNN-GA) respectively. The statistical analysis showed that Mg2+ was the most important factor for both uni- and multiplex qPCR. Dynamic models of uni- and multiplex qPCR could be constructed using both RSM and BPNN-GA methods. But RSM was better than BPNN-GA on prediction performance in terms of the mean absolute error (MAE), the mean square error (MSE) and the Coefficient of Determination (R2). Ultimately, optimal parameters of uni- and multiplex qPCR were determined by RSM.

Metal nanoparticle assisted polymerase chain reaction for strain typing of Salmonella Typhi

Salmonella enterica serotype Typhi (S. Typhi) is the causative agent of typhoid fever and remains a major health threat in most of the developing countries. The prompt diagnosis of typhoid directly from the patient's blood requires high level of sensitivity and specificity. Some of us were the first to report PCR based diagnosis of typhoid. This approach has since then been reported by many scientists using different genomic targets. Since the number of bacteria circulating in the blood of a patient can be as low as 0.3 cfu ml(-1), there is always a room for improvement in diagnostic PCR. In the present study, the role of different types of nanoparticles was investigated to improve the existing PCR based methods for diagnosis and strain typing of S. Typhi (targeting Variable Number of Tandem Repeats [VNTR]) by using optimized PCR systems. Three different types of nanoparticles were used i.e., citrate stabilized gold nanoparticles, rhamnolipid stabilized gold and silver nanoparticles, and magnetic iron oxide nanoparticles. The non-specific amplification was significantly reduced in VNTR typing when gold and silver nanoparticles were used in an appropriate concentration. More importantly, the addition of nanoparticles decreased the non-specificity to a significant level in the case of multiplex PCR thus further validating the reliability of PCR for the diagnosis of typhoid.

Artificial intelligence techniques to optimize the EDC/NHS-mediated immobilization of cellulase on Eudragit L-100

Two artificial intelligence techniques, namely artificial neural network (ANN) and genetic algorithm (GA) were combined to be used as a tool for optimizing the covalent immobilization of cellulase on a smart polymer, Eudragit L-100. 1-Ethyl-3-(3-dimethyllaminopropyl) carbodiimide (EDC) concentration, N-hydroxysuccinimide (NHS) concentration and coupling time were taken as independent variables, and immobilization efficiency was taken as the response. The data of the central composite design were used to train ANN by back-propagation algorithm, and the result showed that the trained ANN fitted the data accurately (correlation coefficient R² = 0.99). Then a maximum immobilization efficiency of 88.76% was searched by genetic algorithm at a EDC concentration of 0.44%, NHS concentration of 0.37% and a coupling time of 2.22 h, where the experimental value was 87.97 ± 6.45%. The application of ANN based optimization by GA is quite successful.