ReviewJournal of food and drug analysis2020
Noble metal nanomaterial-based aptasensors for microbial toxin detection.
Review in Journal of food and drug analysis, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed, 31 citations in OpenAlex.
- SELEX-Based Aptamer Technologies for Toxin Analysis: Screening, Optimization, and Computational Assisted Design.Toxins · 2026Review
- Metallic nanostructure-based aptasensors for robust detection of proteins.Nanoscale advances · 2024Review
- The Potential of Single-Chain Variable Fragment Antibody: Role in Future Therapeutic and Diagnostic Biologics.Journal of immunology research · 2024Review
- A Label-Free Aptasensor for Turn-On Fluorescent Detection of Aflatoxin B1 Based on an Aggregation-Induced-Emission-Active Probe and Single-Walled Carbon Nanohorns.Foods (Basel, Switzerland) · 2023Article
- Single-Walled Carbon Nanohorn-Based Fluorescence Energy Resonance Transfer Aptasensor Platform for the Detection of Aflatoxin B1.Foods (Basel, Switzerland) · 2023Article
- A colorimetric aptasensor based on gold nanoparticles for detection of microbial toxins: an alternative approach to conventional methods.Analytical and bioanalytical chemistry · 2022Review
- Transcription Factor-Based Biosensors for Detecting Pathogens.Biosensors · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Microbial toxins generated by bacteria, fungi and algae cause serious food-safety problems due to the frequent contamination of foodstuffs and their poisonous nature. Becoming acquainted with the contamination condition of foodstuffs is highly dependent on developing sensitive, specific, and accurate methods for targeting microbial toxins. Aptamers, obtained from systematic evolution of ligands by exponential enrichment (SELEX), have significant advantages for microbial toxin analysis, such as small size, reproducible chemical synthesis, and modification, as well as high binding affinity, specificity, and stability. Besides, aptamers have a predictable structure and can be tailored using biomolecular tools (e.g., ligase, endonuclease, exonuclease, polymerase, and so on), which is conducive to the development of flexible and variable amplification methods. Recent studies revealed that the combination of aptamers and noble metal nanomaterials offers unprecedented opportunities for microbial toxin detection. Noble metal nanomaterials with outstanding physical and chemical properties facilitate the detection process and improve the sensitivity and specificity. In this review, we discuss current progress in the development of various noble metal nanomaterial-based aptasensors for microbial toxin detection. These noble metal nanomaterials include gold nanoparticles, gold nanorods, gold nanoclusters, silver nanoparticles, silver nanoclusters, and bimetallic nanomaterials. Aptasensors based on noble metal nanomaterials exhibiting high selectivity and sensitivity represent a promising tool for microbial toxin detection.
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.