Evidence mapPaperPMID 41201733Full record

ReviewInfection2026

The infectome framework: linking polymicrobial ecology and biofilm dynamics to precision diagnostic approaches.

Reem Mahdi Saleh, Omar Mohammed Hassan

Abstract readReview
PubMed Publisher
In one paragraph

Review in Infection, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

2 authors.

Reem Mahdi SalehBiology Department, College of Science, University of Anbar, Ramadi, 31001, Iraq.
Omar Mohammed HassanBiology Department, College of Science, University of Anbar, Ramadi, 31001, Iraq. sc.omerhasan@uoanbar.edu.iq.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Chronic infections are a persistent global health problem and are frequently sustained by polymicrobial communities rather than by a single pathogen. This review brings together current evidence for the infectome concept, defined as the dynamic set of pathogenic or pathobiont taxa in the host, their shared functional capacities, and the interactions that connect them. We analyze how community-level processes promote persistence, cause diagnostic failure, and drive therapeutic resistance, with emphasis on multispecies biofilms, quorum sensing, horizontal gene transfer, metabolic cooperation, and immune modulation. We also highlight advances in multi-omics and computational integration that now permit high-resolution infectome profiling and reveal taxa and interspecies networks that are not captured by routine culture. Clinical examples such as periodontitis, bacterial vaginosis, chronic rhinosinusitis, device-associated infections, and recurrent urinary tract infections show the translational value of this shift. On the therapeutic side, we discuss infectome-informed options including antivirulence agents, biofilm-disrupting enzymes, bacteriophages and lysins, community-wide susceptibility-guided regimens, and microbiome-restoration strategies. Finally, we identify the main requirements for the field: standardized sampling and analytic workflows, reproducible infectome signatures linked to clinical outcomes, and trial designs able to capture ecological dynamics and meet regulatory expectations for community-targeted interventions. Adopting an infectome perspective can enable precision infectiology and reshape the management of chronic and recurrent infections.

Indexed as

BiofilmsCoinfectionMicrobiotaPrecision MedicineHumansBiofilmInfectomeMetagenomicsPolymicrobial infectionsPrecision infectiology

Identifiers

PMID41201733

What Socratic 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 Socratic graph.