ArticleNanomaterials (Basel, Switzerland)2022
The Voltammetric Detection of Cadaverine Using a Diamine Oxidase and Multi-Walled Carbon Nanotube Functionalised Electrochemical Biosensor.
Article in Nanomaterials (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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.
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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.
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Who cites it
2 citing papers in PubMed, 7 citations in OpenAlex.
- Non-Invasive Biosensing for Healthcare Using Artificial Intelligence: A Semi-Systematic Review.Biosensors · 2024Review
- Novel Approaches to Enzyme-Based Electrochemical Nanobiosensors.Biosensors · 2023Review
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
Cadaverine is a biomolecule of major healthcare importance in periodontal disease; however, current detection methods remain inefficient. The development of an enzyme biosensor for the detection of cadaverine may provide a cheap, rapid, point-of-care alternative to traditional measurement techniques. This work developed a screen-printed biosensor (SPE) with a diamine oxidase (DAO) and multi-walled carbon nanotube (MWCNT) functionalised electrode which enabled the detection of cadaverine via cyclic voltammetry and differential pulse voltammetry. The MWCNTs were functionalised with DAO using carbodiimide crosslinking with 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide hydrochloride (EDC) and
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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.