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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 OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.968
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.019
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