Nucleic acid is the main material for storing, copying, and transmitting genetic information. Gene sequencing is of great significance in DNA damage research, gene therapy, mutation analysis, bacterial infection, drug development, and clinical diagnosis. Gene detection has a wide range of applications, such as environmental, biomedical, pharmaceutical, agriculture and forensic medicine to name a few. Compared with Sanger sequencing, high-throughput sequencing technology has the advantages of larger output, high resolution, and low cost which greatly promotes the application of sequencing technology in life science research. Magnetic nanoparticles, as an important part of nanomaterials, have been widely used in various applications because of their good dispersion, high surface area, low cost, easy separation in buffer systems and signal detection. Based on the above, the application of magnetic nanoparticles in nucleic acid detection was reviewed.
A series of electron micrographs showing the presence of different molecular forms representing various replication stages of plasmid deoxyribonucleic acid from Streptomyces griseus was obtained. Based upon an analysis of these electron micrographs, a tentative model for plasmid deoxyribonucleic acid replication in S. griseus is proposed.
Using magnetic nanobeads (MNBs) to extract nucleic acids is an efficient, inexpensive, easy automation, high throughput and good repeatability, instead of the traditional nucleic acid extraction (NAE) methods. Advances in fully automated MNBs-based nucleic acid extraction systems (MNAES)
can push the frontiers of point-of-care testing (POCT) devices towards low-cost, automation, and enhanced accuracy molecular-level diagnostics. So, this paper introduces the pipettingbased MNAES with position of magnetic separation kit or tip, and based on magnetic bar MNAES with blending
manner is shock or rotation. Further, advantages and disadvantages of various MNAES are compared. We envisage that the future trend in molecular diagnosis and monitoring will be facilitation, intelligent, miniaturization, and high throughput MNAES with sample-in-answer-out capability.
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