News&Events

Focus

BIT team makes progress in medical and biological detection technology of unknown and fuzzy target based on micro and nanotechnology

1.png

Recently, Associate Professor Geng Lina from the School of Life Science, together with the team of Professor Luo Aiqin and Professor Deng Yulin, joined the team of Professor Zhong Haizheng from the School of Materials Science and Engineering and the team of Associate Professor Li Huanjun from the School of Chemistry and Chemical Engineering, respectively. They have made a series of progress in medical diagnosis and biological detection technology based on micro-nano technology to construct unknown and obscure objects. Since 2022, their three manuscripts have been continuously published or accepted in Q1 journals Biosensors & Bioelectronics (IF 19.40) and Sensors and Actuators B: Chemical (IF 14.00). and one was published in the Q2 journal, Journal of Chromatography A. Associate Professor Geng Lina has received invitations from Biosensors & Bioelectronics to be a member of the editorial or review committee, as well as invitations for manuscripts from journals such as Biosensors & Bioelectronics and Journal of Chromatography A.

With the continuous development of clinical diagnostics and life sciences, the detection and analysis objects are not only limited to known targets, which puts higher demands on the analysis technology of clinical and biological samples. Medical and biological detection objects often have great complexity, and in the face of unknown and biological objects for which complete information is not yet available, it is important to achieve rapid identification and detection of unknown and ambiguous targets before obtaining all information about the target object by in-depth analysis with existing knowledge conditions, but this has not been clearly reported yet.

Cancer cell-derived exosomes are considered as one of the potential biomarkers for non-invasive cancer diagnosis; however, the surface properties of exosomes of different tumor sources are not fully understood, and complex and unknown nature of targets like exosomes limit the use of currently widely used detection techniques such as immunology. Drawing on the idea of artificial intelligence, the group proposes to use the chemical self-assembly property in molecular blotting technology to “train” the target unknown object to be detected using the polymeric system, and self-assemble to build three-dimensional highly selective and high-affinity biological recognition pores with the help of intermolecular forces, and after eluting the template, the blotted pores can be used for unknown or recognition, separation or detection of fuzzy targets. Unlike artificial intelligence, which uses data for autonomous training to extract information for model construction, this group has established a method to prepare new artificial intelligence materials by using physical objects for “autonomous training”. On this basis, two new “Turn-on” type fluorescence sensing detection techniques are developed using aptamer-mediated aggregated luminescent material technology and aptamer/graphene energy transfer resonance technology, respectively. The sensitivity of exosome detection is better than that of current methods reported in the literature, and the method can initially distinguish clinical tumor patient blood samples from healthy human blood samples. At the same time, the established method based on manual “customization” of highly selective recognition materials can be applied to other unknown targets by simply changing the “training” template, which is universally applicable. The method established by the group not only expands the application field of molecular blotting technology, but more importantly, it proposes a new highly selective unknown object recognition technology means, which is simple and efficient, and fills the relevant technology gaps, and the method is also patent pending. (https://doi.org/10.1016/j.snb.2021.131182)(https://doi.org/10.1016/j.bios.2022.114112).

2.png

In recent years, for new and sudden outbreaks of infectious diseases, rapid detection of unknown, partially unknown and constantly mutating pathogens is one of the keys to timely detection and effective control of infectious disease outbreaks. The group has realized multidimensional high-throughput high-resolution electrophoretic separation of microbial cells by using the efficient and integrated advantages of microfluidic chips and with the help of computerized fluid dynamics simulation. Further, image analysis techniques such as scale invariant feature transformation algorithm based image feature extraction, global information entropy and support vector machine are utilized to resolve the fine electrophoretic separation fingerprints and achieve the identification and semi-quantitative analysis of unknown microorganisms in mixed samples, respectively. (https://doi.org/10.1016/j.chroma.2021.462797)

3.png

Flexible wearable intelligent health monitoring devices cannot be separated from the development of biosensor technology. Photoelectrochemical (PEC) sensors have the advantages of low cost, simple instrumentation, easy miniaturization and high sensitivity. New organic/inorganic hybrid and inorganic chalcogenide nanomaterials have attracted much attention in several fields because of their excellent optoelectronic properties such as high carrier mobility, direct band gap structure, and high photoelectric conversion efficiency. By adopting the strategy of titanium dioxide anti-opal/calcitonite quantum dot heterojunction while wrapping a highly selective molecularly imprinted polymer layer, the group has not only established a calixarene biosensor that can be applied to the detection of aqueous samples, but also realized the highly selective detection of cholesterol in blood samples (https://doi.org/10.1016/j.bios.2022.114112) (https://authors.elsevier.com/tracking/article/details.do?aid=132121&jid=SNB&surname=Lina). Based on this work, the team will further develop the detection of unknown targets in sweat in order to establish novel wearable smart health monitoring devices.

4.png