Evidence map›Paper›PMID 36832044›Full record

ArticleBiosensors2023

Detection of Rice Fungal Spores Based on Micro- Hyperspectral and Microfluidic Techniques.

Xiaodong Zhang, Houjian Song, Yafei Wang, Lian Hu, Pei Wang, Hanping Mao

Open access · goldAbstract read
In one paragraph

Article in Biosensors, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
2.1field-weighted citation impact, top 14% 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

8 citing papers in PubMed, 19 citations in OpenAlex.

  1. Article
  2. Frontiers in plant science · 2026
    Review
  3. Article
  4. Article
  5. Review
  6. Review
  7. Article
  8. Review
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

6 authors at 2 institutions in 1 country.

Xiaodong ZhangSchool of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China.
Houjian SongSchool of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China.
Yafei WangSchool of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China.ORCID 0000-0002-9575-781X
Lian HuKey Laboratory of Key Technology on Agricultural Machine and Equipment, Ministry of Education, South China Agricultural University, Guangzhou 510642, China.ORCID 0000-0002-4875-7868
Pei WangKey Laboratory of Key Technology on Agricultural Machine and Equipment, Ministry of Education, South China Agricultural University, Guangzhou 510642, China.
Hanping MaoSchool of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China.
Jiangsu University · CNSouth China Agricultural University · CN

Funding

NZXB20210106 NZXB20210106
6 · The paper itself

Abstract

As rice is one of the world's most important food crops, protecting it from fungal diseases is very important for agricultural production. At present, it is difficult to diagnose rice fungal diseases at an early stage using relevant technologies, and there are a lack of rapid detection methods. This study proposes a microfluidic chip-based method combined with microscopic hyperspectral detection of rice fungal disease spores. First, a microfluidic chip with a dual inlet and three-stage structure was designed to separate and enrich

Indexed as

HypocrealesOryzaMicrofluidicsPlant DiseasesSpores, Fungalclassification methodmicrofluidic chipmicro-hyperspectralrice fungal spores

Identifiers

PMID36832044
PMCPMC9954447
OpenAlexW4321106852

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.