Evidence map›Paper›PMID 33425992›Full record

ArticleFrontiers in molecular biosciences2020

Prediction Correction Topic Evolution Research for Metabolic Pathways of the Gut Microbiota.

Li Ning, Peng Lifang, He Huixin

Abstract read
In one paragraph

Article in Frontiers in molecular biosciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Review
  3. Topic Evolution Analysis for Omics Data Integration in Cancers.Frontiers in cell and developmental biology · 2021
    Article
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

3 authors.

Li NingManagement Science and Engineering Department, Management School, Xiamen University, Xiamen, China.
Peng LifangManagement Science and Engineering Department, Management School, Xiamen University, Xiamen, China.
He HuixinComputer Science and Engineering Department, Computer Science and Engineering School, Huaqiao University, Quanzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The gut microbiota is composed of a large number of different bacteria, that play a key role in the construction of a metabolic signaling network. Deepening the link between metabolic pathways of the gut microbiota and human health, it seems increasingly essential to evolutionarily define the principal technologies applied in the field and their future trends. We use a topic analysis tool, Latent Dirichlet Allocation, to extract themes as a probabilistic distribution of latent topics from literature dataset. We also use the Prophet neural network prediction tool to predict future trend of this area of study. A total of 1,271 abstracts (from 2006 to 2020) were retrieved from MEDLINE with the query on "gut microbiota" and "metabolic pathway." Our study found 10 topics covering current research types: dietary health, inflammation and liver cancer, fatty and diabetes, microbiota community, hepatic metabolism, metabolomics-based approach and SFCAs, allergic and immune disorders, gut dysbiosis, obesity, brain reaction, and cardiovascular disease. The analysis indicates that, with the rapid development of gut microbiota research, the metabolomics-based approach and SCFAs (topic 6) and dietary health (topic 1) have more studies being reported in the last 15 years. We also conclude from the data that, three other topics could be heavily focused in the future: metabolomics-based approach and SCFAs (topic 6), obesity (topic 8) and brain reaction and cardiovascular disease (topic 10), to unravel microbial affecting human health.

Indexed as

gut microbiotaLDAmetabolic pathwaytime-series featuretopic prediction

Identifiers

PMID33425992
PMCPMC7793741

What OpenQuestion holds

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Registered trials

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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.