Evidence map›Paper›PMID 42877225›Full record

ArticleFrontiers in bioinformatics2026

Towards a holistic epidemiology of

Linda Fenske, Oliver Schwengers, Alexander Goesmann

Abstract read
In one paragraph

Article in Frontiers in bioinformatics, 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

3 authors.

Linda FenskeBioinformatics and Systems Biology, Justus Liebig University Giessen, Giessen, Germany.
Oliver SchwengersBioinformatics and Systems Biology, Justus Liebig University Giessen, Giessen, Germany.
Alexander GoesmannBioinformatics and Systems Biology, Justus Liebig University Giessen, Giessen, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Methods: To move beyond region- or outbreak-limited studies, this work has analyzed 37970 Results: The dataset largely matched the known population structure with serotype III, Ia, and V most common and stable serotype/clonal complex lineages (e.g., III-2/CC17, Ia/CC23, CC1/V), while also increasing serotype diversity. Lineages differed in their accessory-gene profiles, with III-2/CC17 being enriched for virulence and adhesion genes, while other groups showed either greater genomic plasticity (mobile/phage genes) or niche specialization. AMR was widespread with very high tetracycline resistance (>80%), frequent MLSB resistance determinants, and emerging aminoglycoside resistance in some genomes. Conclusions: Overall, it became evident that the associated metadata contained substantial gaps. Missing or incomplete information limits biological interpretation, pointing out the importance of rigorously curated, structured metadata for maximizing the value of ongoing sequencing efforts.

Indexed as

antimicrobial resistancecapsular serotypesclonal complexesGWASmetadatamultilocus sequence typingpopulation structureStreptococcus agalactiae

Identifiers

PMID42877225
PMCPMC13662052

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.