Evidence map›Paper›PMID 29375588›Full record

ReviewFrontiers in plant science2017

Advanced DNA-Based Point-of-Care Diagnostic Methods for Plant Diseases Detection.

Han Yih Lau, Jose R Botella

Open access · goldAbstract readReview
In one paragraph

Review in Frontiers in plant science, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 69 papers.

0numbers the graph read from it
0cells of the map it votes in
69citing papers in PubMed
6.7field-weighted citation impact, top 2% of its field
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

69 citing papers in PubMed, 218 citations in OpenAlex.

  1. Rapid On-Site Detection ofPlants (Basel, Switzerland) · 2026
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  2. Review
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  8. Exploring the diagnostic landscape ofFrontiers in microbiology · 2025
    Review
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9 more citing papers are in PubMed but not listed here.

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

2 authors at 2 institutions in 2 countries.

Han Yih LauBiotechnology and Nanotechnology Research Centre, Malaysian Agricultural Research and Development Institute, Serdang, Malaysia.
Jose R BotellaPlant Genetic Engineering Laboratory, School of Agriculture and Food Sciences, University of Queensland, Brisbane, QLD, Australia.
Agriculture and Food · AUMalaysian Agricultural Research and Development Institute · MY

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Diagnostic technologies for the detection of plant pathogens with point-of-care capability and high multiplexing ability are an essential tool in the fight to reduce the large agricultural production losses caused by plant diseases. The main desirable characteristics for such diagnostic assays are high specificity, sensitivity, reproducibility, quickness, cost efficiency and high-throughput multiplex detection capability. This article describes and discusses various DNA-based point-of care diagnostic methods for applications in plant disease detection. Polymerase chain reaction (PCR) is the most common DNA amplification technology used for detecting various plant and animal pathogens. However, subsequent to PCR based assays, several types of nucleic acid amplification technologies have been developed to achieve higher sensitivity, rapid detection as well as suitable for field applications such as loop-mediated isothermal amplification, helicase-dependent amplification, rolling circle amplification, recombinase polymerase amplification, and molecular inversion probe. The principle behind these technologies has been thoroughly discussed in several review papers; herein we emphasize the application of these technologies to detect plant pathogens by outlining the advantages and disadvantages of each technology in detail.

Indexed as

HDAisothermal amplificationLAMPpoint-of-care diagnosticRCARPA

Identifiers

PMID29375588
PMCPMC5770625
OpenAlexW2771595483

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.