Evidence map›Paper›PMID 41028632›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2026

In Vitro Study of Microbiota Influence on Enteric Glial Cell Physiology.

Elena Lucarini, Alessandra Toti

Abstract read
PubMed Publisher
In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 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

2 authors.

Elena LucariniDepartment of Neuroscience, Psychology, Drug Research and Child Health (NEUROFARBA), University of Florence, Florence, Italy. elena.lucarini@unifi.it.
Alessandra TotiDepartment of Neuroscience, Psychology, Drug Research and Child Health (NEUROFARBA), University of Florence, Florence, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Gut microbiome is an essential partner in the maintenance of gastrointestinal (GI) homeostasis, through its interaction with both epithelium, immune cells, and enteric nervous system (ENS). Under pathological conditions, disturbances of resident bacteria composition and/or metabolism frequently combine with ENS dysfunction. Enteric glial cells (EGCs), accompanying neurons across all the layers of the GI tract, can orchestrate neuronal activity and immune response to microenvironment perturbation, but their interaction with the components of microbiota has been poorly investigated. Indeed, although a reciprocal influence between gut microflora and EGC has been suggested, further research is needed to elucidate the mechanisms governing this crosstalk and its implications in health and disease. To this end, in vitro studies appear necessary as they allow for dissection and selective analysis of microbiota-to-EGCs signaling without the intestinal contour. This chapter has the objective to describe a method to process fecal material from either animals or humans for in vitro studying microbiota-mediated modulation of EGCs activity. It also illustrates the advantage of using whole fecal supernatant (FS) over selected supernatant and/or living bacteria to in vitro mimic microbiota signaling and introduce the possibility to treat EGCs with the conditioned medium of FS pre-treated epithelial cells.

Indexed as

Enteric Nervous SystemGastrointestinal MicrobiomeNeurogliaAnimalsCulture Media, ConditionedFecesHumansCulture Media, ConditionedColonDysbiosisEnteric gliaFecal supernatantGut microbiotaIntestinal epitheliumIntestinal epithelium

Identifiers

PMID41028632

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.