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Cationic Antimicrobial Peptides and Their Multifunctional Role in the Immune System

2000· article· en· W2079375923 on OpenAlexaff
Monisha G. Scott, Robert E. W. Hancock

Bibliographic record

VenueCritical Reviews in Immunology · 2000
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAntimicrobial Peptides and Activities
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAntimicrobial peptidesAntimicrobialInnate immune systemMicrobiologyProinflammatory cytokineLipopolysaccharideImmune systemBacteriaTumor necrosis factor alphaAntibioticsBiologyGram-negative bacteriaChemistrySepsisLysozymeInflammationImmunologyBiochemistryEscherichia coli

Abstract

fetched live from OpenAlex

Many species of life contain cationic antimicrobial peptides as components of their immune systems. The antimicrobial activity of these peptides has been studied extensively, and many peptides have a broad spectrum of activity not only against gram-negative and gram-positive bacteria but also against antibiotic-resistant bacteria, fungi, viruses, and parasites. Such cationic antimicrobial peptides can also act in synergy with host molecules, such as other cationic peptides and proteins, lysozyme, and also conventional antibiotics, to kill microbes. It has been found that certain peptides are produced in large quantities at sites of infection/inflammation, and their expression can be induced by bacterial products such as endotoxic lipopolysaccharide (LPS) and proinflammatory cytokines, such as tumor necrosis factor-alpha (TNF-alpha). These peptides often have a high affinity for bacterial products, such as LPS, allowing them to modulate the host response and reduce the inflammatory response in sepsis. More recently, they have been found to interact directly with host cells to modulate the inflammatory process and innate defenses.

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 studies0.0000.001
Scholarly communication0.0010.001
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 head0.015
GPT teacher head0.253
Teacher spread0.238 · 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

Citations289
Published2000
Admission routes1
Has abstractyes

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