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Record W2166290770 · doi:10.2174/1568005024605855

Clinical Development of Cationic Antimicrobial Peptides: From Natural to Novel Antibiotics

2002· review· en· W2166290770 on OpenAlexafffund
Rebecca Hancock, Aleksander Patrzykat

Bibliographic record

VenueCurrent Drug Targets - Infectious Disorders · 2002
Typereview
Languageen
FieldImmunology and Microbiology
TopicAntimicrobial Peptides and Activities
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of British ColumbiaCanada Research ChairsCystic Fibrosis Foundation
KeywordsAntimicrobialAntibioticsAntimicrobial peptidesCationic polymerizationBiologyMicrobiologyComputational biologyChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Over the past decade, levels of bacterial resistance to antibiotics have risen dramatically and ”superbugs“ resistant to most or all available agents have appeared in the clinic. Thus there is a growing need to discover and introduce new drugs. One potential source of novel antibiotics is the cationic antimicrobial peptides, which have been isolated from most living entities as components of their non-specific defenses against infectious organisms. Based on these natural templates, scores of structurally diverse antimicrobial cationic peptides have been designed, manufactured both chemically and biologically, and tested for activity against specific pathogens. A few of these peptide antibiotics have entered clinical trials to date, with mixed success. However, their diverse portfolio of structures, activity spectra, biological activities, and modes of action, provide substantial potential. Keywords: Cationic Antimicrobial Peptides, Cationic peptides, Antibiotics, Burkholderia, COMBINATORIAL LIBRARIES, Lipopolysaccharide, Methicillin resistant Staphylococcus aureus, Vancomycin resistant Entrococcus

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)0.001
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
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.037
GPT teacher head0.327
Teacher spread0.290 · 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

Citations368
Published2002
Admission routes2
Has abstractyes

Explore more

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