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Record W1508310914 · doi:10.1371/journal.pntd.0003996

Selamectin Is the Avermectin with the Best Potential for Buruli Ulcer Treatment

2015· article· en· W1508310914 on OpenAlexafffund
Nicole Scherr, Gerd Pluschke, Charles J. Thompson, Santiago Ramón‐García

Bibliographic record

VenuePLoS neglected tropical diseases · 2015
Typearticle
Languageen
FieldMedicine
TopicMycobacterium research and diagnosis
Canadian institutionsUniversity of British Columbia
FundersCanadian Institutes of Health ResearchBritish Columbia Lung Association
KeywordsMoxidectinMycobacterium ulceransBuruli ulcerAvermectinIvermectinPharmacologyPharmacodynamicsMedicineAntiparasitic agentBiologyPharmacokineticsVeterinary medicineInternal medicine

Abstract

fetched live from OpenAlex

A comprehensive analysis was done to evaluate the potential use of anti-parasitic macrocyclic lactones (including avermectins and milbemycins) for Buruli ulcer (BU) therapy. A panel containing nearly all macrocyclic lactones used in human or in veterinary medicine was analyzed for activity in vitro against clinical isolates of Mycobacterium ulcerans. Milbemycin oxime and selamectin were the most active drugs against M. ulcerans with MIC values from 2 to 8 μg/mL and 2 to 4 μg/mL, respectively. In contrast, ivermectin and moxidectin, which are both in clinical use, showed no significant activity (MIC> 32 μg/mL). Time-kill kinetic assays showed bactericidal activity of selamectin and in vitro pharmacodynamic studies demonstrated exposure-dependent activity. These data together with analyses of published pharmacokinetic information strongly suggest that selamectin is the most promising macrocyclic lactone for BU treatment.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.036
GPT teacher head0.289
Teacher spread0.252 · 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 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

Citations27
Published2015
Admission routes2
Has abstractyes

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