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Record W3006923993 · doi:10.3390/microorganisms8020280

Antimicrobial Peptides: Virulence and Resistance Modulation in Gram-Negative Bacteria

2020· review· en· W3006923993 on OpenAlexafffund
Marylise Duperthuy

Bibliographic record

VenueMicroorganisms · 2020
Typereview
Languageen
FieldImmunology and Microbiology
TopicAntimicrobial Peptides and Activities
Canadian institutionsUniversité de Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAntimicrobial peptidesBacteriaMicrobiologyVirulenceGram-negative bacteriaAntibiotic resistanceBiologyAntimicrobialAntibioticsPathogenic bacteriaVirulence factorBiochemistryEscherichia coliGenetics

Abstract

fetched live from OpenAlex

Growing resistance to antibiotics is one of the biggest threats to human health. One of the possibilities to overcome this resistance is to use and develop alternative molecules such as antimicrobial peptides (AMPs). However, an increasing number of studies have shown that bacterial resistance to AMPs does exist. Since AMPs are immunity molecules, it is important to ensure that their potential therapeutic use is not harmful in the long term. Recently, several studies have focused on the adaptation of Gram-negative bacteria to subinhibitory concentrations of AMPs. Such concentrations are commonly found in vivo and in the environment. It is therefore necessary to understand how bacteria detect and respond to low concentrations of AMPs. This review focuses on recent findings regarding the impact of subinhibitory concentrations of AMPs on the modulation of virulence and resistance in Gram-negative bacteria.

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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.019
GPT teacher head0.255
Teacher spread0.236 · 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

Citations57
Published2020
Admission routes2
Has abstractyes

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