Evidence map›Paper›PMID 42382682›Full record

ArticleExploration (Beijing, China)2026

Three-Dimensional Magnetic Multi-Metallic Nanozymes With Colorimetric and Catalytic Amplification for Rapid and Highly-Sensitive Detection of Influenza A.

Zhenzhen Liu, Pengyou Zhou, Xiaofei Jia, Xiaoxian Liu, Yong Yang, Yansong Sun, Rui Xiao

Abstract read
In one paragraph

Article in Exploration (Beijing, China), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Zhenzhen LiuState Key Laboratory of Pathogen and Biosecurity Academy of Military Medical Sciences Beijing China.
Pengyou ZhouState Key Laboratory of Pathogen and Biosecurity Academy of Military Medical Sciences Beijing China.
Xiaofei JiaState Key Laboratory of Pathogen and Biosecurity Academy of Military Medical Sciences Beijing China.
Xiaoxian LiuState Key Laboratory of Pathogen and Biosecurity Academy of Military Medical Sciences Beijing China.
Yong YangState Key Laboratory of High-Performance Ceramics and Superfine Microstructures Shanghai Institute of Ceramics Chinese Academy of Sciences Shanghai China.ORCID https://orcid.org/0000-0001-8996-2182
Yansong SunState Key Laboratory of Pathogen and Biosecurity Academy of Military Medical Sciences Beijing China.
Rui XiaoState Key Laboratory of Pathogen and Biosecurity Academy of Military Medical Sciences Beijing China.ORCID https://orcid.org/0000-0002-6313-4757

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Lateral flow immunochromatographic assay (LFIA) has been widely used in the point-of-care testing field with fast results readout and portability. Nanozymes-based LFIA strengthened the colorimetric signals of LFIA strips by catalytic oxidation of the colorless substrates into colored substrates, without additional measuring equipment. But the limited specific surface area and functionalized sites of the zero-dimensional and two-dimensional nanozymes restricted their peroxidase-like activity, and the detection sensitivity cannot meet the demand for early diagnosis. Herein, a novel three-dimensional (3D) magnetic multi-metallic nanozyme with excellent superparamagnetism and peroxidase-like activity was developed as a catalytic amplification sensor for the detection of Influenza A (Flu A) viruses. Notably, two-dimensional MoS

Indexed as

3D magnetic multi‐metallic nanozymescatalytic amplificationinfluenza A viruseslocalized surface plasmon resonancetrifunctional LFIA strips

Identifiers

PMID42382682
PMCPMC13317654

What OpenQuestion holds

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Registered trials

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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.