Evidence map›Paper›PMID 39760074›Full record

ArticleComputational and structural biotechnology journal2025

An ecological and stochastic perspective on persisters resuscitation.

Tania Alonso-Vásquez, Michele Giovannini, Gian Luigi Garbini, Mikolaj Dziurzynski, Giovanni Bacci, Ester Coppini, Donatella Fibbi, Marco Fondi

Abstract read
In one paragraph

Article in Computational and structural biotechnology journal, 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
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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

8 authors.

Tania Alonso-VásquezDepartment of Biology, University of Florence, Via Madonna del Piano 6, Sesto Fiorentino, 50019, Italy.
Michele GiovanniniDepartment of Biology, University of Florence, Via Madonna del Piano 6, Sesto Fiorentino, 50019, Italy.
Gian Luigi GarbiniDepartment of Biology, University of Florence, Via Madonna del Piano 6, Sesto Fiorentino, 50019, Italy.
Mikolaj DziurzynskiDepartment of Biology, University of Florence, Via Madonna del Piano 6, Sesto Fiorentino, 50019, Italy.
Giovanni BacciDepartment of Biology, University of Florence, Via Madonna del Piano 6, Sesto Fiorentino, 50019, Italy.
Ester CoppiniG.I.D.A. SpA, Via Baciacavallo 36, Prato, 59100, Italy.
Donatella FibbiG.I.D.A. SpA, Via Baciacavallo 36, Prato, 59100, Italy.
Marco FondiDepartment of Biology, University of Florence, Via Madonna del Piano 6, Sesto Fiorentino, 50019, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Resistance, tolerance, and persistence to antibiotics have mainly been studied at the level of a single microbial isolate. However, in recent years it has become evident that microbial interactions play a role in determining the success of antibiotic treatments, in particular by influencing the occurrence of persistence and tolerance within a population. Additionally, the challenge of resuscitation (the capability of a population to revive after antibiotic exposure) and pathogen clearance are strongly linked to the small size of the surviving population and to the presence of fluctuations in cell counts. Indeed, while large population dynamics can be considered deterministic, small populations are influenced by stochastic processes, making their behaviour less predictable. Our study argues that microbe-microbe interactions within a community affect the mode, tempo, and success of persister resuscitation and that these are further influenced by noise. To this aim, we developed a theoretical model of a three-member microbial community and analysed the role of cell-to-cell interactions on pathogen clearance, using both deterministic and stochastic simulations. Our findings highlight the importance of ecological interactions and population size fluctuations (and hence the underlying cellular mechanisms) in determining the resilience of microbial populations following antibiotic treatment.

Indexed as

37N2546N60Microbial communitiesMicrobial interactionsPersisters

Identifiers

PMID39760074
PMCPMC11697298

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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.