Tuberculosis (TB) remains one of the most devastating infectious diseases and its treatment efficiency is majorly influenced by the stage at which infection with the TB bacterium is diagnosed. The available methods for TB diagnosis are either time consuming, costly or not efficient. This study employs a signal generation mechanism for biosensing, known as Plasmonic ELISA, and computational intelligence to facilitate automatic diagnosis of TB. Plasmonic ELISA enables the detection of a few molecules of analyte by the incorporation of smart nanomaterials for better sensitivity of the developed detection system. The computational system uses k-means clustering and thresholding for image segmentation. This paper presents the results of the classification performance of the Plasmonic ELISA imaging data by using various types of classifiers. The five-fold cross-validation results show high accuracy rate (>97%) in classifying TB images using the entire data set. Future work will focus on developing an intelligent mobile-enabled expert system to diagnose TB in real-time. The intelligent system will be clinically validated and tested in collaboration with healthcare providers in Malaysia.
A portable electrochemical aptamer-antibody based sandwich biosensor has been designed and successfully developed using an aptamer bioreceptor immobilized onto a screen-printed electrode surface for Mycobacterium tuberculosis (M. tuberculosis) detection in clinical sputum samples. In the sensing strategy, a CFP10-ESAT6 binding aptamer was immobilized onto a graphene/polyaniline (GP/PANI)-modified gold working electrode by covalent binding via glutaraldehyde linkage. Upon interaction with the CFP10-ESAT6 antigen target, the aptamer will capture the target where the nano-labelled Fe3O4/Au MNPs conjugated antibody is used to complete the sandwich format and enhance the signal produced from the aptamer-antigen interaction. Using this strategy, the detection of CFP10-ESAT6 antigen was conducted in the concentration range of 5 to 500 ng/mL. From the analysis, the detection limit was found to be 1.5 ng/mL, thereby demonstrating the efficiency of the aptamer as a bioreceptor. The specificity study was carried out using bovine serum albumin (BSA), MPT64, and human serum, and the result demonstrated good specificity that is 7% higher than the antibody-antigen interaction reported in a previous study. The fabricated aptasensor for M. tuberculosis analysis shows good reproducibility with an relative standard deviation (RSD) of 2.5%. Further analysis of M. tuberculosis in sputum samples have shown good correlation with the culture method with 100% specificity and sensitivity, thus making the aptasensor a promising candidate for M. tuberculosis detection considering its high specificity and sensitivity with clinical samples.