Next-generation biosensing tools based on CRISPR/Cas have revolutionized the molecular detection. A number of CRISPR/Cas-based biosensors have been reported for the detection of nucleic acid targets. The establishment of efficient methods for non-nucleic acid target detection would further broaden the scope of this technique, but up to now, the concerning research is limited. In the current study, we reported a versatile biosensing platform for non-nucleic acid small-molecule detection called SMART-Cas12a (small-molecule aptamer regulated test using CRISPR/Cas12a). Simply, hybridization chain reaction cascade signal amplification was first trigged by functional nucleic acid (aptamer) through target binding. Then, the CRISPR/Cas system was integrated to recognize the amplified products followed by activation of the trans-cleavage. As such, the target can be ingeniously converted to nucleic acid signals and then fluorescent signals that can be readily visualized and analyzed by a customized 3D-printed visualizer with the help of a home-made App-enabled smartphone. Adenosine triphosphate was selected as a model target, and under the optimized conditions, we achieved fine analytical performance with a linear range from 0.1 to 750 μM and a detection limit of 1.0 nM. The satisfactory selectivity and recoveries that we have obtained further demonstrated this method to be suitable for a complex sample environment. The sample-to-answer time was less than 100 min. Our work not only expanded the reach of the CRISPR-Cas system in biosensing but also provided a prototype method that can be generalized for detecting a wider range of analytes with desirable adaptability, sensitivity, specificity, and on-site capability.

In recent years, fires have become more and more frequent, which has a great impact on people's production and life and even their lives. This paper designs a fire detection device based on YOLOv5, which is mainly composed of Raspberry Pi, Openmv and buzzer, which can be widely used in narrow corridors, parking lots, shopping malls, forests and other scenarios. The device has the characteristics of high recognition rate, fast recognition speed and strong sensitivity, and has excellent recognition effect in fire detection.

As one of the most common cancers in women, breast cancer has the highest incidence in the world. Nearly 600,000 people die from breast cancer each year, and early detection is essential for breast cancer treatment. In recent years, the rapid development of artificial intelligence has provided unprecedented ideas for the precise diagnosis and treatment of breast cancer. In this paper, the practical application of artificial intelligence convolutional neural network in breast cancer recognition is studied, which greatly improves the detection speed and saves a lot of time for doctors to further judge the condition.

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