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Record W2104553045 · doi:10.5539/ijb.v5n2p85

Investigating the Effects of Hydrophobicity and Charge on the Therapeutic Ability of the Antimicrobial Histatin 8 Peptide for Potential Use in Oral Applications

2013· article· en· W2104553045 on OpenAlexafffundvenue
Scott M. G. Matheson, Naowarat Cheeptham, Heidi E. K. Huttunen‐Hennelly

Bibliographic record

VenueInternational Journal of Biology · 2013
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAntimicrobial Peptides and Activities
Canadian institutionsThompson Rivers University
FundersDirectorate for Biological SciencesThompson Rivers University
KeywordsAntimicrobialAntimicrobial peptidesCandida tropicalisPeptideYeastAntibioticsMinimum inhibitory concentrationChemistryMicrobiologyAmino acidBiochemistryBiology

Abstract

fetched live from OpenAlex

Advances in technology allow for the construction of synthetic antibiotics, which includes the development of de novo antimicrobial peptides (AMPs). The contents of AMPs including amino acids, chain length, hydrophobicity, ring structure/rigidity, terminus and charge when modified can alter the antimicrobial properties. A special family of peptides called histatins is naturally excreted by oral glands as an immune response, and previous research shows their potential for treating thrush. Histatin 8 is known to have antimicrobial activity against yeast strains and the goal of this study was to synthesize histatin 8 and two novel derivatives (delt 1 and delt 4) that fall at extremes of each other with regard to charge and hydrophobicity in order to investigate the properties that could optimize antimicrobial properties. The derivatives were characterized using various chemical and biological assays to investigate the effects of charge and hydrophobicity on bioactivity. Compared to histatin 8, delt 4’s minimum inhibitory concentration (MIC) was decreased more than tenfold against Candida tropicalis indicating increased antimicrobial activity. Re-inoculation confirmed fungicidal properties.

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 score0.174
Threshold uncertainty score0.282

Codex and Gemma teacher scores by category

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 communication0.0000.000
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 head0.014
GPT teacher head0.249
Teacher spread0.236 · 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

Citations4
Published2013
Admission routes3
Has abstractyes

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