Editorial: Advances in Nucleic Acid-Based Biosensors and Imaging
Bibliographic record
Abstract
Advances in Nucleic Acid-based Biosensors and ImagingNucleic acids are key biomolecules that regulate the expression of hereditary information within living organisms (Jani et al., 2019).The predictable and specific Watson-Crick hybridization of complementary bases of nucleic acids renders them extremely useful for biomedical applications including biosensing and bioimaging (Jiang et al., 2020;Ma and Liu, 2020).In recent years, functional nucleic acids, including molecular beacons, aptamers and DNAzymes have been synthesized in such a way that they can specifically bind to various analytes including metal ions, organic dyes, amino acids, oligosaccharides, toxins, enzymes, and cells (Choi, 2020).This key property has prompted the use of functional nucleic acid for the detection of targets based upon numerous detection approaches, including fluorescent, colorimetric and electrochemical detection (Hwang et al., 2020).This Research Topic highlights the use of functional nucleic acids for fundamental research and applications.A number of comprehensive review articles have highlighted the recent advances of functional nucleic acids and their biomedical applications.For instance, Yang et al. reviewed the advances and biological applications of DNA-templated silver nanoclusters (DNA-AgNCs).DNA-AgNCs, an emerging fluorophore, possesses unique features including high fluorescence quantum yields and stability, good biocompatibility, and low toxicity, making them highly suitable to be used as fluorescent probes.Their synthesis methods and biomedical applications, such as fluorescent sensing and imaging, were comprehensively reviewed.Dyussembayev et al. reviewed the advances in biosensors for detection and quantification of plant pathogens.The conventional methods used in plant disease diagnostics were compared with new nucleic acid-based biosensing technologies, especially electrochemical and optical biosensors for pathogen detection.The remaining challenges and future perspectives were briefly discussed.In addition, Huang et al. reviewed the applications of nucleic acid probe-based fluorescent sensing and imaging for cancer diagnosis and therapy.The characteristics of nucleic acid probes and their latest advances in fluorescent sensing and imaging were summarized, particularly in cancer diagnosis and therapy.Some challenges and perspectives in the field were also elaborated.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.012 | 0.016 |
| Insufficient payload (model declined to judge) | 0.015 | 0.016 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".