Evidence map›Paper›PMID 40746317›Full record

ArticleFrontiers in microbiology2025

Comparison of

Jianwen Wu, Yizhao Li, Mi Qiu, Jihua Guan

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. 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

4 authors.

Jianwen WuGuangxi Laboratory of Forestry, Guangxi Forestry Research Institute, Nanning, China.
Yizhao LiFaculty of Agricultural Engineering, Guangxi Vocational and Technical College, Nanning, China.
Mi QiuGuangxi Laboratory of Forestry, Guangxi Forestry Research Institute, Nanning, China.
Jihua GuanGuangxi Laboratory of Forestry, Guangxi Forestry Research Institute, Nanning, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: This study aimed to analyze the flavor profile and microbial community structure of 54 Methods: The flavor components and microbial community structure were analyzed using SBSE-GC-MS, electronic nose, and 16S rRNA sequencing. SVM machine learning was employed to classify the samples based on their characteristics. Results: A total of 114 common aroma components were identified, including esters, alcohols, hydrocarbons, ketones, acids, aldehydes, heterocyclic compounds, phenols, halogenated hydrocarbons, amides, and others. Using a Discussion: The study highlights that natural water-sealed fermentation is the most effective method for enhancing flavor profiles, and that

Indexed as

16sRNAmicrobial communitySBSEsuansunSVM

Identifiers

PMID40746317
PMCPMC12312013

What Socratic 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 Socratic graph.