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Record W3203053206 · doi:10.25011/cim.v44i3.36657

Antimicrobial Peptide, LL-37, And Its Potential As An Anti-HIV Agent

2021· review· en· W3203053206 on OpenAlexaffvenue
Ana Vera-Cruz, Nongnuj Tanphaichitr, Jonathan B. Angel

Bibliographic record

VenueClinical and investigative medicine · 2021
Typereview
Languageen
FieldImmunology and Microbiology
TopicAntimicrobial Peptides and Activities
Canadian institutionsOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsAntimicrobialHuman immunodeficiency virus (HIV)MedicineAntimicrobial peptidesImmunologyVirusPeptideVirologyBiologyMicrobiologyBiochemistry

Abstract

fetched live from OpenAlex

Human immunodeficiency virus (HIV) continues to have a profound global health impact. New infections continue at a high rate despite the development of prophylactic therapies, prompting the need for development of novel preventative approaches. Antimicrobial peptides (AMPs), such as LL-37, display broad microbicidal properties and have potential as anti-HIV agents. LL-37 has been studied for its anti-HIV activity and the limited data available suggest it can inhibit HIV infection in primary T cells as well as exert inhibitory effects on key HIV enzymes. Its immunomodulatory properties may both enhance and inhibit HIV replication. In addition, LL-37 has both 1) the ability to kill other sexually-transmitted pathogens and 2) spermicidal activity; thus, it is a good candidate for multipurpose prevention technology. Further investigation of its anti-HIV activity is warranted.

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 score0.003
Threshold uncertainty score0.010

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 science0.0010.001
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 head0.151
GPT teacher head0.367
Teacher spread0.216 · 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

Citations15
Published2021
Admission routes2
Has abstractyes

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