MétaCan
Menu
Back to cohort
Record W2557351924 · doi:10.1002/cbic.201600538

Fungal Glycolipid Hydrolase Inhibitors and Their Effect on <i>Cryptococcus neoformans</i>

2016· article· en· W2557351924 on OpenAlexafffund
Andrés G. Santana, Christina Tysoe, Guanggan Hu, James W. Kronstad, Ethan D. Goddard‐Borger, Stephen G. Withers

Bibliographic record

VenueChemBioChem · 2016
Typearticle
Languageen
FieldMedicine
TopicFungal Infections and Studies
Canadian institutionsVancouver Hospital and Health Sciences CentreCanada's Michael Smith Genome Sciences CentreUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsIminosugarCryptococcus neoformansGlycolipidMicrobiologyEchinocandinsBiologyAntifungal drugDrugEnzymeBiochemistryChemistryCandida albicansPharmacologyAntifungalAmphotericin B

Abstract

fetched live from OpenAlex

Pathogenic fungi kill an estimated 1.3 million people each year. This number is predicted to rise as drug resistance spreads, thus antifungal drugs with novel modes of action are urgently required. Fungal endoglycoceramidase-related proteins 1 and 2 (EGCrP-1 and -2), which hydrolyse glucosylceramide and ergosteryl β-glucoside, respectively, are important for fungal cell growth and have been identified as potential targets for drug development. A library of iminosugar derivatives was screened against EGCrP-1 and -2, and a number of competitive inhibitors with nanomolar affinities were identified. In addition, a mechanism-based inhibitor was shown to form a covalent derivative with EGCrP-2. Nine of the inhibitors were evaluated against Cryptococcus neoformans. Several showed growth inhibitory activity, but only against a C. neoformans strain lacking the outer fungal polysaccharide capsule; this implies that penetration into the cell is a significant handicap for these inhibitors. Pro-drug versions of these inhibitors could address this issue.

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 score0.049
Threshold uncertainty score0.466

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 studies0.0000.000
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 head0.007
GPT teacher head0.226
Teacher spread0.219 · 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

Citations6
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueChemBioChemSame topicFungal Infections and StudiesFrench-language works237,207