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Record W2117626607 · doi:10.3390/biology4040607

Antimicrobial Peptides as Mediators of Innate Immunity in Teleosts

2015· review· en· W2117626607 on OpenAlexaff
Barbara A. Katzenback

Bibliographic record

VenueBiology · 2015
Typereview
Languageen
FieldImmunology and Microbiology
TopicAntimicrobial Peptides and Activities
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsAntimicrobial peptidesInnate immune systemBiologyImmunityImmune systemAntimicrobialMicrobiologyImmunology

Abstract

fetched live from OpenAlex

Antimicrobial peptides (AMPs) have been identified throughout the metazoa suggesting their evolutionarily conserved nature and their presence in teleosts is no exception. AMPs are short (18-46 amino acids), usually cationic, amphipathic peptides. While AMPs are diverse in amino acid sequence, with no two AMPs being identical, they collectively appear to have conserved functions in the innate immunity of animals towards the pathogens they encounter in their environment. Fish AMPs are upregulated in response to pathogens and appear to have direct broad-spectrum antimicrobial activity towards both human and fish pathogens. However, an emerging role for AMPs as immunomodulatory molecules has become apparent-the ability of AMPs to activate the innate immune system sheds light onto the multifaceted capacity of these small peptides to combat pathogens through direct and indirect means. Herein, this review focuses on the role of teleost AMPs as modulators of the innate immune system and their regulation in response to pathogens or other exogenous molecules. The capacity to regulate AMP expression by exogenous factors may prove useful in modulating AMP expression in fish to prevent disease, particularly in aquaculture settings where crowded conditions and environmental stress pre-dispose these fish to infection.

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 score0.002
Threshold uncertainty score0.007

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 science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.034
GPT teacher head0.316
Teacher spread0.282 · 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

Citations159
Published2015
Admission routes1
Has abstractyes

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