Aptamers are in vitro selected oligonucleotides (DNA, RNA, oligos with modified nucleotides) that can have high affinity and specificity for a broad range of potential targets with high affinity and specificity. Here we focus on their applications as biosensors in the diagnostic field, although they can also be used as therapeutic agents. A small number of peptide aptamers have also been identified. In analytical settings, aptamers have the potential to extend the limit of current techniques as they offer many advantages over antibodies and can be used for real-time biomarker detection, cancer clinical testing, and detection of infectious microorganisms and viruses. Once optimized and validated, aptasensor technologies are expected to be highly beneficial to clinicians by providing a larger range and more rapid output of diagnostic readings than current technologies and support personalized medicine and faster implementation of optimal treatments.
Being the predominant cause of disability, neurological diseases have received much attention from the global health community. Over a billion people suffer from one of the following neurological disorders: dementia, epilepsy, stroke, migraine, meningitis, Alzheimer’s disease, Parkinson’s disease, multiple sclerosis, amyotrophic lateral sclerosis, Huntington’s disease, prion disease, or brain tumors. The diagnosis and treatment options are limited for many of these diseases. Aptamers, being small and non-immunogenic nucleic acid molecules that are easy to chemically modify, offer potential diagnostic and theragnostic applications to meet these needs. This review covers pioneering studies in applying aptamers, which shows promise for future diagnostics and treatments of neurological disorders that pose increasingly dire worldwide health challenges.
Since the discovery of coronavirus disease 2019 (COVID-19) in December 2019, it has been mainly diagnosed with quantitative reverse transcription polymerase chain reaction (PCR) of nasal swabs in clinics. A very sensitive and rapid detection technique using easily collected fluids such as saliva is needed for safer and more practical, precise mass testing. Here, we introduce a computationally screened gold-nanopatterned metasurface platform out of a pattern space of 2 100 combinations for strongly enhanced light–virus interaction using a genetic algorithm and apply them to investigate the presence and concentration of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In our approach, the gold metasurface with the nanopattern that provides the highest plasmonic enhancement is modified with the primary DNA aptamer for COVID-19 sensing from unprocessed saliva. A fluorescently tagged secondary aptamer was used to bind the virus that was then captured on the surface with the primary aptamer. By incorporating machine learning to identify the virus from Raman spectra, we achieved 95.2% sensitivity and specificity on 36 SARS-CoV-2 PCR-positive and 33 SARS-CoV-2 PCR-negative samples collected in the clinics. In addition, we demonstrated that our nanoplasmonic aptasensor could distinguish wild-type, Alpha, and Beta variants through the machine learning analysis of their spectra. Our results may help pave the way for effective, safe, and quantitative preventive screening and identification of variants.
There has been keen interest in measuring in vivo GABA. However, GABA signal is low and typically measured using techniques vulnerable to the confounding effects of in-scanner head movement. This issue is particularly problematic for clinical studies since it may lead to Type I or II error in testing for group differences. While solutions to mitigate the effects of movement have been proposed, fundamental and largely unexamined issues are the nature and scale of this effect. We developed a method to quantify and characterize head movement during GABA spectroscopy and found that two parameters of movement, displacement and instantaneous movement, were inversely correlated with and accounted for 12.1% and 20.2% variance of GABA estimates respectively. We conclude that head movement can significantly affect GABA measurements and the application of methods to account for movement may improve of GABA measurement accuracy and the detection of true group differences in clinical studies.
COVID-19 is detected using reverse transcription polymerase chain reaction (RT-PCR) of nasal swabs. A very sensitive and rapid detection technique using easily-collected fluids like saliva must be developed for safe and precise mass testing. Here, we introduce a metasurface platform for direct sensing of COVID-19 from unprocessed saliva. We computationally screen gold metasurfaces out of a pattern space of 2100 combinations for strongly-enhanced light-virus interaction with machine learning and use it to investigate the presence and concentration of the SARS-CoV-2. We use machine learning to identify the virus from Raman spectra with 95.2% sensitivity and specificity on 36 PCR positive and 33 negative clinical samples and to distinguish wild-type, alpha, and beta variants. Our results could pave the way for effective, safe and quantitative preventive screening and identification of variants.
Being the predominant cause of disability, neurological diseases have received much attention from the global health community. Over a billion people suffer from one of the following neurological disorders: dementia, epilepsy, stroke, migraine, meningitis, Alzheimer's disease, Parkinson’s disease, multiple sclerosis, amyotrophic lateral sclerosis, Huntington’s disease, prion dis-ease, or brain tumors. Diagnosis and treatment options are limited for many of these diseases. Aptamers, being small and non-immunogenic nucleic acid molecules that are easy to chemically modify, offer potential diagnostic and theranostic applications to meet these needs. This review covers pioneer studies to apply aptamers, which show promise for future diagnostics and treatments of neurological disorders that pose increasingly dire worldwide health challenges.
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