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.
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.
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.
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