ReviewApplied and environmental microbiology2026
Describing the persistence landscape for introducing microbes into complex communities.
Review in Applied and environmental microbiology, 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
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Authors and funding
12 authors.
Funding
Abstract
Introducing non-native organisms into natural or designed microbial communities holds enormous potential but faces challenges in establishment, persistence, and containment. We propose the "persistence landscape"-the functional composition of a target microbiome that promotes or discourages engineered organism persistence. Building on existing ecological concepts, this framework predicts the fitness of introduced organisms. Application of this concept would enable controlled introduction of desired taxa and phenotypes, addressing pressing challenges in agronomy, biomanufacturing, and human health.
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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.