ReviewMicrobiologyOpen2026
Post-Microbial Therapeutics for the Next Generation of Medicine.
Review in MicrobiologyOpen, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Post-microbial therapeutics is proposed here as an integrative, function-centered framework rather than a new therapeutic class. It asks whether a defined microbial perturbation can produce a reproducible change in a microbial function, biochemical output, and clinically relevant host phenotype. Bacteriophages provide a perturbation model because their host dependence permits strain-selective intervention, but the framework also distinguishes between whole phages and engineered phages, lysins, depolymerases, delivery systems, and combination products. A central requirement is that a microbial module must be defined by experimentally testable functional contribution rather than by statistical co-occurrence alone. Computationally inferred modules are therefore treated as hypotheses until supported by perturbation, reconstruction, removal/add-back, flux, or rescue experiments. Evidence reviewed across infection, gastrointestinal, biofilm, and cardiometabolic contexts shows a recurring pattern: phage exposure can alter bacterial abundance, resistance phenotypes, community interactions, and metabolism, but the strength of evidence diminishes as claims shift from bacterial killing to ecosystem restoration and host benefit. Engineering and delivery technologies can improve targeting or exposure, yet they introduce additional constraints in genetic stability, resistance, formulation, pharmacology, and regulation. The framework is consequently best viewed as a testable translational architecture that connects intervention to function and outcome while separating established evidence from emerging hypotheses.
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Registered trials
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.