Lateral Flow Immunoassay for Naked Eye Detection of Mycobacterium tuberculosis Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis. Detection and control of infectious diseases is a major problem, especially in developing countries. Lateral flow immunoassay (LFIA) has been introduced as a handheld immunoassaybased pointofcare platform for an automated detection of TB. The CFP10ESAT6 antigen of M. tuberculosis was used as the target in early detection of TB using LFIA stripbased POC strategy. An interesting platform based on optical signals is implemented as a colour change in the detection area that is visible to the naked eye. The gold nanoparticles (AuNPs) were used as the colour probe for the detection of a target of interest. The highresolution transmission electron microscopy (HRTEM) image and ultravioletvisible spectrophotometer (UVVis) analysis confirmed that the synthesized AuNPs were appropriate for the immunoassay designed. The platform consists of AuNPs conjugated with specific antibodies (Ab) to capture the antigen of M. tuberculosis. Under the capillary effect, sandwich immunoreactions of AuNPAbantigen were performed on the test pad of the immunostrip, which can be observed by the colour signal on the test line of the strip with a short assay time. Furthermore, the newly developed biosensor was utilized in CFP10ESAT6 antigen detection in human sputum specimens with satisfactory results. The characteristic coloured bands enable visual detection (naked eye) of target analyte without instrumentation. This noninvasive diagnose system which is sputumbased detection could provide userfriendly and affordable diagnostic tests in developing countries.

Assay Type Detection Using Advanced Machine Learning Algorithms The colourimetric analysis has been used in diversified fields for years. This paper provides a unique overview of colourimetric tests from the perspective of computer vision by describing different aspects of a colourimetric test in the context of image processing, followed by an investigation into the development of a colorimetric assay type detection system using advanced machine learning algorithms. To the best of our knowledge, this is the first attempt to define colourimetric assay types from the eyes of a machine and perform any colorimetric test using deep learning. This investigation utilizes the stateoftheart pretrained models of Convolutional Neural Network (CNN) to perform the assay type detection of an enzymelinked immunosorbent assay (ELISA) and lateral flow assay (LFA). The ELISA dataset contains images of both positive and negative samples, prepared for the plasmonic ELISA based TBantigen specific antibody detection. The LFA dataset contains images of the universal pH indicator paper of eight pH levels. It is noted that the pretrained models offered 100% accurate visual recognition for the assay type detection. Such detection can assist novice users to initiate a colorimetric test using hisher personal digital devices. The assay type detection can also aid in calibrating an imagebased colorimetric classification.