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Record W4414663578 · doi:10.1186/s13104-025-07484-w

Effect of an antibiotic and a probiotic on phage communities in the swine gut microbiota

2025· article· en· W4414663578 on OpenAlexafffund
Éléonore Lemieux, Xavier C. Monger, Linda Saucier, Steve J. Charette, Frédéric Guay, Éric Pouliot, Sylvain Fournaise, Antony T. Vincent

Bibliographic record

VenueBMC Research Notes · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicBacteriophages and microbial interactions
Canadian institutionsCegep de Saint HyacintheUniversité de MontréalUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsProbioticAntibioticsFecesMicrobiomeGut floraAbundance (ecology)Bacteria

Abstract

fetched live from OpenAlex

OBJECTIVE: The impact of dietary treatments on the phage community of porcine intestinal microbiota is not well understood. An antibiotic (tylvalosin), a probiotic (Pediococcus acidilactici), and a combination of these were given to six cannulated pigs in a double crossover design study. Samples of ileal digesta and feces were collected and whole genome shotgun sequencing was performed. The variations in phage and bacterial communities were compared for each treatment and sample type. RESULTS: The bacteriophages present in the gut microbiome exhibited greater variations in both α- and β-diversity between sample types (digesta, feces) than between treatments. β-diversity and differential abundance showed that the effect of the combined antibiotic and probiotic treatment was the same as with the antibiotic alone. However, the effects of the probiotic and antibiotic treatments were statistically significantly different in the fecal samples. β-diversity was different in those two treatments, and differential abundance analysis identified multiple phages as markers for each treatment. No significant variations in relative abundance were found in phage lifestyle (i.e., virulent, temperate) between treatments.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.735
Threshold uncertainty score0.750

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.052
GPT teacher head0.393
Teacher spread0.341 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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