Evidence map›Paper›PMID 42597927›Full record

ArticleNew microbes and new infections2026

PlasmidRiskNet: An explainable machine-learning framework for antimicrobial resistance plasmid risk stratification.

Oluwaseun E Agboola, Samuel S Agboola, Oluwaseun Ruth Olasehinde, Temitope C Aribigbola, Anuoluwapo Bukola Shaleye, I Adefolake Alebiosu, Babatunji E Oyinloye, Idowu O Omotuyi, Abel Kolawole Oyebamiji, Olusola Tolulope Fakoyede

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in New microbes and new infections, 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

10 authors.

Oluwaseun E AgboolaInstitute for Drug Research and Development, Bogoro Research Centre, Afe Babalola University, Ado-Ekiti, 360001, Nigeria.
Samuel S AgboolaDepartment of Pharmacology and Toxicology, College of Pharmacy, Afe Babalola University, Ado-Ekiti, 360001, Nigeria.
Oluwaseun Ruth OlasehindeDepartment of Medical Biochemistry, College of Medicine and Health Sciences, Afe Babalola University, Ado-Ekiti, 360102, Nigeria.
Temitope C AribigbolaFaculty of Medicine and Dentistry, Department of Medical Microbiology and Immunology, University of Alberta, Edmonton, AB, T6G 2E1, Canada.
Anuoluwapo Bukola ShaleyeDepartment of Social and Administrative Pharmacy, College of Pharmacy, Afe Babalola University, Ado-Ekiti, 360001, Nigeria.
I Adefolake AlebiosuDepartment of Human Nutrition and Dietetics, Afe Babalola University, Ado-Ekiti, 360001, Nigeria.
Babatunji E OyinloyeInstitute for Drug Research and Development, Bogoro Research Centre, Afe Babalola University, Ado-Ekiti, 360001, Nigeria.
Idowu O OmotuyiInstitute for Drug Research and Development, Bogoro Research Centre, Afe Babalola University, Ado-Ekiti, 360001, Nigeria.
Abel Kolawole OyebamijiDepartment of Industrial Chemistry, University of Ilesa, Ilesa, Osun State, Nigeria.
Olusola Tolulope FakoyedeCollege of Nursing Sciences, Ekiti State University Teaching Hospital, Ado-Ekiti, Nigeria.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Plasmids are the principal vehicles of horizontal antimicrobial resistance (AMR) gene transfer, yet risk analyses rarely combine gene content, mobility, and network topology. We asked whether explainable machine learning over these dimensions can stratify plasmid dissemination risk, and tested rigorously where it succeeds and fails. Methods: From 72,556 PLSDB 2025 plasmids we integrated 251,138 AMRFinderPlus gene records with CARD v3 ontology and MOBsuite typing, built a co-resistance network, and derived a composite PlasmidRisk score from five features. Three classifiers were evaluated by five-fold cross-validation; external validation used WHO and ECDC 2024 to 2025 carbapenemase designations as a feature-independent reference. We added length- and host-adjusted burden models, phylum-normalized enrichment, and feature-category ablation. Results: AMR genes occurred in 41.0% of plasmids across 85 drug classes. The network (29,758 nodes) was heterogeneous rather than scale-free. Internal cross-validation AUCs exceeded 0.999, but because labels derived from the scored features this reflects internal consistency, not generalization. The feature-independent external AUC was modest (0.607): strong for the metallo-beta-lactamases Conclusions: PlasmidRiskNet offers a useful pre-screening layer for MBL-bearing plasmids but not for the compact serine-carbapenemase backbones (

Indexed as

Antimicrobial resistanceCarbapenemaseExternal validationHorizontal gene transferNetwork analysisPlasmid mobilomePlasmidRiskNetPlasmidRisk scorePLSDBRandom forestWHO priority pathogens

Identifiers

PMID42597927
PMCPMC13470093

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.