Evidence map›Paper›PMID 40077556›Full record

ArticleFoods (Basel, Switzerland)2025

Establishing Detection Methods for Okadaic Acid Aptamer-Target Interactions: Insights from Computational and Experimental Approaches.

Wenchong Shan, Jiefang Sun, Runqing Liu, Jing Wang, Bing Shao

Abstract read
In one paragraph

Article in Foods (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Wenchong ShanNational Nanfan Research Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya 572024, China.ORCID 0000-0002-9732-9018
Jiefang SunBeijing Key Laboratory of Diagnostic and Traceability Technologies for Food Poisoning, Beijing Center for Disease Prevention and Control, Beijing 100013, China.
Runqing LiuBeijing Key Laboratory of Diagnostic and Traceability Technologies for Food Poisoning, Beijing Center for Disease Prevention and Control, Beijing 100013, China.
Jing WangNational Nanfan Research Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya 572024, China.
Bing ShaoBeijing Key Laboratory of Diagnostic and Traceability Technologies for Food Poisoning, Beijing Center for Disease Prevention and Control, Beijing 100013, China.

Funding

National Key Research and Development Special Plan 2023YFD1400902National Natural Science Foundation of China U1736201
6 · The paper itself

Abstract

The binding interactions between okadaic acid (OA) aptamers and OA molecules are crucial for developing effective detection methods. This study aims to identify the recognition site and establish a reliable detection protocol through computational simulations and experimental validations. After determining the target sequence (OA-2), molecular docking simulations using Sybyl-X and H-dock were conducted to predict the binding affinity and interaction sites of OA aptamers with their targets. These predictions were subsequently validated through experiments based on the Förster resonance energy transfer (FRET) principle. The combined approach not only confirmed the computational predictions, identifying the "major region" as the recognition basis of OA-2, but also provided deeper insights into the binding mechanisms. Subsequently, a classical AuNPs-aptamer colorimetric detection method was established based on the OA-2 sequence and applied to the detection of real shellfish samples, achieving a limit of quantification (LOQ) of 5.0 μg kg

Indexed as

aptamerbinding interactionsfood safetymarine toxinsmolecular docking simulationsokadaic acid

Identifiers

PMID40077556
PMCPMC11898563

What OpenQuestion holds

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Registered trials

None linked

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.