ReviewThe Analyst2015
Nano-enabled bioanalytical approaches to ultrasensitive detection of low abundance single nucleotide polymorphisms.
Review in The Analyst, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed, 18 citations in OpenAlex.
- Recent Progress in Single-Nucleotide Polymorphism Biosensors.Biosensors · 2023Review
- The Promise of Nanotechnology in Personalized Medicine.Journal of personalized medicine · 2022Review
- Application of magnetic nanoparticles in nucleic acid detection.Journal of nanobiotechnology · 2020Review
- An aptamer based fluorometric assay for amyloid-β oligomers using a metal-organic framework of type Ru@MIL-101(Al) and enzyme-assisted recycling.Mikrochimica acta · 2020Article
- A silver(I) doped bud-like DNA nanostructure as a dual-functional nanolabel for voltammetric discrimination of methylated from unmethylated genes.Mikrochimica acta · 2018Article
- Multiplex detection of extensively drug resistant tuberculosis using binary deoxyribozyme sensors.Biosensors & bioelectronics · 2017Article
- Sequence-Specific Covalent Capture Coupled with High-Contrast Nanopore Detection of a Disease-Derived Nucleic Acid Sequence.Chembiochem : a European journal of chemical biology · 2017Article
- DNA nanotechnology for nucleic acid analysis: multifunctional molecular DNA machine for RNA detection.Chemical communications (Cambridge, England) · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
Abstract
Single nucleotide polymorphisms (SNPs) constitute the most common types of genetic variations in the human genome. A number of SNPs have been linked to the development of life threatening diseases including cancer, cardiovascular diseases and neurodegenerative diseases. The ability for ultrasensitive and accurate detection of low abundant disease-related SNPs in bodily fluids (e.g. blood, serum, etc.) holds a significant value in the development of non-invasive future biodiagnostic tools. Over the past two decades, nanomaterials have been utilized in a myriad of biosensing applications due to their ability of detecting extremely low quantities of biologically important biomarkers with high sensitivity and accuracy. Of particular interest is the application of such technologies in the detection of SNPs. The use of various nanomaterials, coupled with different powerful signal amplification strategies, has paved the way for a new generation of ultrasensitive SNP biodiagnostic assays. Over the past few years, several ultrasensitive SNP biosensors capable of detecting specific targets down to the ultra-low regimes (ca. aM and below) and therefore holding great promises for early clinical diagnosis of diseases have been developed. This mini review will highlight some of the most recent, significant advances in nanomaterial-based ultrasensitive SNP sensing technologies capable of detecting specific targets on the attomolar (10(-18) M) regime or below. In particular, the design of novel, powerful signal amplification strategies that hold the key to the ultrasensitivity is highlighted.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.