Evidence map›Paper›PMID 36338091›Full record

ArticleFrontiers in microbiology2022

Degradation potential of alkanes by diverse oil-degrading bacteria from deep-sea sediments of Haima cold seep areas, South China Sea.

Lina Lyu, Jie Li, Yu Chen, Zhimao Mai, Lin Wang, Qiqi Li, Si Zhang

Open access · goldAbstract read
In one paragraph

Article in Frontiers in microbiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
1.1field-weighted citation impact, top 27% 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

7 citing papers in PubMed, 22 citations in OpenAlex.

  1. Article
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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

7 authors at 3 institutions in 1 country.

Lina LyuCAS Key Laboratory of Tropical Marine Bio-resources and Ecology, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou, China.
Jie LiCAS Key Laboratory of Tropical Marine Bio-resources and Ecology, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou, China.
Yu ChenSouthern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou, China.
Zhimao MaiCAS Key Laboratory of Tropical Marine Bio-resources and Ecology, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou, China.
Lin WangCAS Key Laboratory of Tropical Marine Bio-resources and Ecology, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou, China.
Qiqi LiCAS Key Laboratory of Tropical Marine Bio-resources and Ecology, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou, China.
Si ZhangCAS Key Laboratory of Tropical Marine Bio-resources and Ecology, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou, China.
Chinese Academy of Sciences · CNInstitute of Oceanology · CNSouthern Marine Science and Engineering Guangdong Laboratory (Guangzhou) · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Marine oil spills are a significant concern worldwide, destroying the ecological environment and threatening the survival of marine life. Various oil-degrading bacteria have been widely reported in marine environments in response to marine oil pollution. However, little information is known about culturable oil-degrading bacteria in cold seep of the deep-sea environments, which are rich in hydrocarbons. This study enriched five oil-degrading consortia from sediments collected from the Haima cold seep areas of the South China Sea.

Indexed as

alkanebiodiversitydegrading bacteriaHaima cold seepoil

Identifiers

PMID36338091
PMCPMC9626528
OpenAlexW4306755528

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.