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<i>In vitro</i> activity of a monoclonal killer anti‐idiotypic antibody and a synthetic killer peptide against oral isolates of <i>Candida</i> spp. differently susceptible to conventional antifungals

2005· article· en· W2064714505 on OpenAlexaff
Maddalena Manfredi, Michael McCullough, Stefania Conti, Luciano Polonelli, Paolo Vescovi, Z. M. Al‐Karaawi, Stephen Porter

Bibliographic record

VenueOral Microbiology and Immunology · 2005
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune Cell Function and Interaction
Canadian institutionsInstitute of Infection and Immunity
Fundersnot available
KeywordsCandida albicansBiologyMonoclonal antibodyMicrobiologyIn vitroYeastPeptideAntibodyCorpus albicansBiological activityImmunologyBiochemistry

Abstract

fetched live from OpenAlex

BACKGROUND/AIMS: A monoclonal killer anti-idiotypic antibody (mAbK10) and a synthetic killer peptide, acting as internal images of a microbicidal, wide-spectrum yeast killer toxin (KT) have been recently shown to express candidacidal in vitro and an in vivo therapeutic activity against experimental mucosal and systemic candidosis models caused by a reference strain of Candida albicans (10S). MATERIAL AND METHODS: The in vitro candidacidal activity of mAbK10 and synthetic killer peptide was compared using a colony forming unit assay against a large number of isolates of different Candida spp., obtained from oral saliva of adult diabetic (type 1 and 2) and nondiabetic subjects from Parma (Italy) and London (UK). RESULTS: Both the KT-mimics exerted a strong dose-dependent candidacidal activity, probably mediated by the interaction with beta-glucan KT receptors on target yeast cells, against all the tested strains, regardless of their species and pattern of resistance to conventional antifungal agents. CONCLUSIONS: These observations open new perspectives in the design and production of candidacidal compounds whose mechanism reflects that exerted in nature by killer yeasts.

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 categoriesMeta-epidemiology (narrow)
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 score0.061
Threshold uncertainty score1.000

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 integrity0.0010.001
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 head0.008
GPT teacher head0.240
Teacher spread0.232 · 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.

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

Citations19
Published2005
Admission routes1
Has abstractyes

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