Evidence map›Paper›PMID 40529185›Full record

ArticleComputational and structural biotechnology journal2025

Direct feature identification from Raman spectra and precise data-driven classification of phytopathogens at single conidium-species level.

Xinze Xu, Zhang Cheng, Wenbo Liu, Chunhua Lin, Weibo Xia, Hsiang-Yang Shyu, Weiguo Miao, Heyang Yuan

Abstract read
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In one paragraph

Article in Computational and structural biotechnology journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

8 authors.

Xinze XuSchool of Tropical Agriculture and Forestry/Key Laboratory of Green Prevention and Control of Tropical Plant Diseases and Pests, Ministry of Education, Hainan University, Haikou 570228, China.
Zhang ChengDepartment of Civil & Environmental Engineering, Temple University, 1947N. 12th Street, Philadelphia, PA 19122, United States.
Wenbo LiuSchool of Tropical Agriculture and Forestry/Key Laboratory of Green Prevention and Control of Tropical Plant Diseases and Pests, Ministry of Education, Hainan University, Haikou 570228, China.
Chunhua LinSchool of Tropical Agriculture and Forestry/Key Laboratory of Green Prevention and Control of Tropical Plant Diseases and Pests, Ministry of Education, Hainan University, Haikou 570228, China.
Weibo XiaDepartment of Civil & Environmental Engineering, Temple University, 1947N. 12th Street, Philadelphia, PA 19122, United States.
Hsiang-Yang ShyuCivil & Environmental Engineering, University of South Florida, 4202 E. Fowler Avenue, Tampa, FL 33620, United States.
Weiguo MiaoSchool of Tropical Agriculture and Forestry/Key Laboratory of Green Prevention and Control of Tropical Plant Diseases and Pests, Ministry of Education, Hainan University, Haikou 570228, China.
Heyang YuanDepartment of Civil & Environmental Engineering, Temple University, 1947N. 12th Street, Philadelphia, PA 19122, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Early and accurate identification of phytopathogenic conidia, which cause substantial agricultural and economic losses, is critical for preventing disease and prescribing management. However, traditional methods based on morphology and molecular biology are time-consuming, labor-intensive, and ineffective at the species level. Here, we aimed to develop a new classification approach through Raman spectroscopy and data-driven modeling. Among the seven selected fungal species, three characteristic Raman wavenumbers at 1003-1005 cm

Indexed as

Data-driven modelsFungal conidiaPhytopathogenicRaman spectroscopy

Identifiers

PMID40529185
PMCPMC12172984

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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.