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Record W2605921613 · doi:10.1042/etls20160016

The therapeutic potential of bacteriocins as protein antibiotics

2017· article· en· W2605921613 on OpenAlexaff
Hannah Michaela Behrens, Anne Six, Daniel Walker, Colin Kleanthous

Bibliographic record

VenueEmerging Topics in Life Sciences · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicProbiotics and Fermented Foods
Canadian institutionsInstitute of Infection and Immunity
Fundersnot available
KeywordsBacteriocinAntibioticsColicinMicrobiologyBiologyIn vivoImmunogenicityBacteriaPotencyIn vitroImmune systemAntimicrobialImmunologyBiotechnologyEscherichia coliBiochemistry

Abstract

fetched live from OpenAlex

The growing incidence of antibiotic-resistant Gram-negative bacterial infections poses a serious threat to public health. Molecules that have yet to be exploited as antibiotics are potent protein toxins called bacteriocins that are produced by Gram-negative bacteria during competition for ecological niches. This review discusses the state of the art regarding the use for therapeutic purposes of two types of Gram-negative bacteriocins: colicin-like bacteriocins (CLBs) and tailocins. In addition to in vitro data, the potency of eight identified CLBs or tailocins has been demonstrated in diverse animal models of infection with no adverse effects for the host. Although the characteristics of bacteriocins will need further study, results obtained thus far regarding their in vivo potency, immunogenicity and low levels of resistance are encouraging. This leads the way for the development of novel treatments using bacteriocins as protein antibiotics.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.030
GPT teacher head0.281
Teacher spread0.251 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations127
Published2017
Admission routes1
Has abstractyes

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