A highly sensitive fluorescence biosensor for aflatoxins B1 detection based on polydiacetylene liposomes combined with exonuclease III-assisted recycling amplification.

A fluorescence biosensor for determination of aflatoxin B1 (AFB1) based on polydiacetylene (PDA) liposomes and exonuclease III (EXO III)-assisted recycling amplification was developed. The AFB1 aptamer partially hybridizes with complementary DNA (cDNA), which is released upon recognition of AFB1 by the aptamer. Subsequently, the cDNA hybridizes with hairpin H to form double-stranded DNA that undergoes digestion by EXO III, resulting in the cyclic release of cDNA and generation of capture DNA for further reaction. The capture DNA then hybridizes with probe modified on PDA liposomes, leading to aggregation of liposomes and subsequent fluorescence production. This strategy exhibited a limit of detection of 0.18 ng/mL within the linear range 1-100 ng/mL with a determination coefficient > 0.99. The recovery ranged from 92.81 to 106.45%, with relative standard deviations (RSD) between 1.73 and 4.26%, for corn, brown rice, peanut butter, and wheat samples. The stability, accuracy, and specificity of the method demonstrated the applicability for real sample analysis.

Artificial intelligence-assisted smartphone-based sensing for bioanalytical applications: A review.

Artificial intelligence (AI) has received great attention since the concept was proposed, and it has developed rapidly in recent years with applications in many fields. Meanwhile, newer iterations of smartphone hardware technologies which have excellent data processing capabilities have leveraged on AI capabilities. Based on the desirability for portable detection, researchers have been investigating intelligent analysis by combining smartphones with AI algorithms. Various examples of the application of AI algorithm-based smartphone detection and analysis have been developed. In this review, we give an overview of this field, with a particular focus on bioanalytical detection applications. The applications are presented in terms of hardware design, software algorithms, and specific application areas. We also discuss the existing limitations of AI-based smartphone detection and analytical approaches, and their future prospects. The take-home message of our review is that the application of AI in the field of detection analysis is restricted by the limitations of the smartphone's hardware as well as the model building of AI for detection targets with insufficient data. Nevertheless, at this juncture, while bioanalytical diagnostics and health monitoring have set the pace for AI-based smartphone applicability, the future should see the technology making greater inroads into other fields. In relation to the latter, it is likely that the ordinary or average person will play a greater participatory role.