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The Resilience of <i>Pseudomonas aeruginosa</i> to Antibiotics and theDesigning of Antimicrobial Peptides to Overcome Microbial Resistance

2022· review· en· W4295078867 on OpenAlexaff
Daniel Juárez-López, Estefanía Morales-Ruiz, Leonardo David Herrera-Zúñiga, Zuriel González-Carrera, Elizabeth Cuevas-Reyes, Gerardo Corzo, Alejandro Schcolnik‐Cabrera, Elba Villegas

Bibliographic record

VenueCurrent Medicinal Chemistry · 2022
Typereview
Languageen
FieldImmunology and Microbiology
TopicAntimicrobial Peptides and Activities
Canadian institutionsUniversité de MontréalHôpital Maisonneuve-Rosemont
Fundersnot available
KeywordsPseudomonas aeruginosaMicrobiologyAntimicrobialAntibioticsBiologyAntibiotic resistanceAntimicrobial peptidesIn silicoBacteriaComputational biologyGeneGenetics

Abstract

fetched live from OpenAlex

Pseudomonas aeruginosa (P. aeruginosa) is a bacterium of medical concern known for its potential to persist in diverse environments due to its metabolic capacity. Its survival ability is linked to its relatively large genome of 5.5-7 Mbp, from which several genes are employed in overcoming conventional antibiotic treatments and promoting resistance. The worldwide prevalence of antibiotic-resistant clones of P. aeruginosa necessitates novel approaches to researching their multiple resistance mechanisms, such as the use of antimicrobial peptides (AMPs). In this review, we briefly discuss the epidemiology of the resistant strains of P. aeruginosa and then describe their resistance mechanisms. Next, we explain the biology of AMPs, enlist the present database platforms that describe AMPs, and discuss their usefulness and limitations in treating P. aeruginosa strains. Finally, we present 13 AMPs with theoretical action against P. aeruginosa, all of which we evaluated in silico in this work. Our results suggest that the AMPs we evaluated have a carpet-like mode of action with a membranolytic function in Gram-positive and Gramnegative bacteria, with a clear potential of synthesis for in vitro evaluation.

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)0.001
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 score0.831
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.030
GPT teacher head0.299
Teacher spread0.269 · 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.

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

Citations5
Published2022
Admission routes1
Has abstractyes

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