Evidence map›Paper›PMID 41292689›Full record

ArticleFrontiers in microbiology2025

A rapid and visual dual LAMP-LFD assay for on-site simultaneous detection of influenza A virus (H1N1) and respiratory syncytial virus (RSV).

Alang Zhang, Xingyu Lan, Hengxuan Zhou, Ting Zhou, Zhihua Xu, Xinyu Cheng, Chenxi Guo, Mingjie Wei, Jiahui Wu, Feng Shi

Abstract read
In one paragraph

Article in Frontiers in microbiology, 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

10 authors.

Alang Zhang *College of Life Sciences, Shihezi University, Shihezi, China.
Xingyu Lan *The First Division Hospital of Xinjiang Production and Construction Corps, Akesu, China.
Hengxuan ZhouCollege of Life Sciences, Shihezi University, Shihezi, China.
Ting ZhouCollege of Life Sciences, Shihezi University, Shihezi, China.
Zhihua XuCollege of Life Sciences, Shihezi University, Shihezi, China.
Xinyu ChengCollege of Life Sciences, Shihezi University, Shihezi, China.
Chenxi GuoCollege of Life Sciences, Shihezi University, Shihezi, China.
Mingjie WeiCollege of Life Sciences, Shihezi University, Shihezi, China.
Jiahui WuCollege of Life Sciences, Shihezi University, Shihezi, China.
Feng ShiCollege of Life Sciences, Shihezi University, Shihezi, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Respiratory tract infections caused by influenza A virus (IAV) and respiratory syncytial virus (RSV) are a major global health burden. To overcome the limitations of existing diagnostic techniques for timely detection (point-of-care testing), this study developed a dual rapid visual detection method based on loop-mediated isothermal amplification (LAMP) and lateral flow device (LFD) technologies for the simultaneous detection of H1N1 influenza virus and RSV. This method uses a dual-labeled probe system (H1N1: digoxigenin/biotin; RSV: 6-carboxyfluorescein/biotin) combined with a two-color latex microsphere signal system that enables the intuitive visual interpretation of multiple detection results. Compared with traditional nucleic acid detection, the entire detection process was completed within 40 min at a constant temperature of 63 °C and the operation was simple. After optimization, the method showed good sensitivity and specificity. The limit of detection for H1N1 IAV and RSV was as low as 7.78 × 10

Indexed as

double detectionlateral flow deviceloop-mediated isothermal amplificationrespiratory virusesvisual detection

Identifiers

PMID41292689
PMCPMC12640905

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.