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Record W3165790409 · doi:10.1016/j.celrep.2021.109172

Temperate phage-antibiotic synergy eradicates bacteria through depletion of lysogens

2021· article· en· W3165790409 on OpenAlexafffund
Amany M. Al-Anany, Rabia Fatima, Alexander P. Hynes

Bibliographic record

VenueCell Reports · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicBacteriophages and microbial interactions
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsLysogenTemperatenessMicrobiologyAntibioticsBiologyBacteriaPhage therapyVirulenceBacteriophageCiprofloxacinTemperate climateAntibiotic resistanceGeneticsEscherichia coliEcologyGene

Abstract

fetched live from OpenAlex

There is renewed interest in bacterial viruses (phages) as alternatives to antibiotics. All phage treatments to date have used virulent phages rather than temperate ones, as these can integrate into the genome of the bacterial host and lie dormant. However, temperate phages are abundant and easier to isolate. To make use of these entities, we leverage stressors known to awaken these dormant, integrated phages. Co-administration of the temperate phage HK97 with sub-inhibitory concentrations of the antibiotic ciprofloxacin results in bacterial eradication (≥8 log reduction) in vitro. This synergy is mechanistically distinct from phage-antibiotic-synergy described for virulent phages. Instead, the antibiotic specifically selects against bacteria in which the phage has integrated. As the interaction between temperate phages and stressors such as ciprofloxacin are known to be widespread, this approach may be broadly applicable and enable the use of temperate phages to combat bacterial infections.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.009
GPT teacher head0.223
Teacher spread0.214 · 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 source (direct Gemma or distilled Codex), 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

Citations107
Published2021
Admission routes2
Has abstractyes

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