Evidence map›Paper›PMID 42840285›Full record

ArticleFrontiers in immunology2026

Inflammatory landscape of

Xiao-Feng Ruan, Xiao-Meng Xue, Gao-Pi Deng, Hao-Meng Wu, Si Chen, Xiang-Dan Hu, Fang-Fang Zhu, Yun-Yun Luo, Dan-Ting Wen

Abstract read
In one paragraph

Article in Frontiers in immunology, 2026. 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

9 authors.

Xiao-Feng Ruan *Guangzhou University of Chinese Medicine, Guangzhou, China.
Xiao-Meng Xue *The First Clinical Medical School of Guangzhou University of Chinese Medicine, Guangzhou, China.
Gao-Pi DengThe First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.
Hao-Meng WuThe Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.
Si ChenThe First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.
Xiang-Dan HuThe Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.
Fang-Fang ZhuThe Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.
Yun-Yun LuoThe Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.
Dan-Ting WenThe Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Bacterial vaginosis (BV) is a prevalent vaginal dysbiosis primarily associated with Methods: Female Sprague-Dawley rats received antibiotic pretreatment followed by intravaginal inoculation with Results: The BV-like phenotype was characterized by clue cell-like epithelial cells, histopathological injury, and an altered serum cytokine profile. 16S sequencing revealed nonsignificant trends toward increased diversity and reduced evenness in the model group. Although overall microbial community composition did not differ significantly between groups, within-group dispersion was significantly lower in the model group. The genera Conclusions: These findings suggest that the

Indexed as

Gardnerella vaginalisVaginosis, BacterialAnimalsCytokinesDisease Models, AnimalFemaleInflammationMetabolomeMetabolomicsMicrobiotaMultiomicsNF-kappa BRatsRats, Sprague-DawleyRNA, Ribosomal, 16SVaginaCytokinesNF-kappa BRNA, Ribosomal, 16Sarachidonic acid metabolismbacterial vaginosisGardnerella vaginalisimmunometabolismmetabolomicstryptophan metabolismvaginal microbiome

Identifiers

PMID42840285
PMCPMC13638667

What OpenQuestion holds

Textmetadata
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.