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Record W2054102780 · doi:10.1089/153036603322662165

Cationic Antimicrobial Peptide Killing of African Trypanosomes and <i>Sodalis glossinidius</i> , a Bacterial Symbiont of the Insect Vector of Sleeping Sickness

2003· article· en· W2054102780 on OpenAlexafffund
Lee R. Haines, Robert E. W. Hancock, Terry W. Pearson

Bibliographic record

VenueVector-Borne and Zoonotic Diseases · 2003
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAntimicrobial Peptides and Activities
Canadian institutionsUniversity of VictoriaUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of British ColumbiaCanada Research ChairsUniversity of Victoria
KeywordsBiologyVector (molecular biology)African trypanosomiasisAntimicrobial peptidesInsectMicrobiologyTrypanosomiasisAntimicrobialPeptideTrypanosoma bruceiVirologyGeneBiochemistryBotanyRecombinant DNA

Abstract

fetched live from OpenAlex

Nine biochemically distinct cationic antimicrobial peptides were tested in vitro for their effects on bloodstream forms and procyclic (insect) forms of African trypanosomes, the protozoan parasites that cause African sleeping sickness in humans and trypanosomiasis in domestic animals. At low concentrations, one peptide completely inhibited growth of bloodstream forms, one inhibited procyclic forms, and five inhibited both trypanosome life cycle stages. The peptides were also tested on Sodalis glossinidius, a bacterial symbiont of tsetse flies. S. glossinidius was highly resistant to seven of the nine peptides, including both that specifically inhibited either bloodstream or procyclic forms and three of the five that inhibited both trypanosome life cycle stages. The results indicate that several of these peptides may be ideal candidates for therapy of trypanosome infected mammals or for transgenic expression in S. glossinidius as a strategy for inhibiting trypanosome survival, development, and maturation in tsetse and interference with transmission of African sleeping sickness.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.234
Threshold uncertainty score0.664

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.010
GPT teacher head0.202
Teacher spread0.193 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations56
Published2003
Admission routes2
Has abstractyes

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