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Record W4400515101 · doi:10.1093/mmy/myae068

Growth on Douglas fir media facilitates<i>Cryptococcus</i>virulence factor production and enhances fungal survival against environmental and immune stressors

2024· article· en· W4400515101 on OpenAlexaboutno aff
Piotr R. Stempinski, Seth Greengo, Arturo Casadevall

Bibliographic record

VenueMedical Mycology · 2024
Typearticle
Languageen
FieldMedicine
TopicFungal Infections and Studies
Canadian institutionsnot available
Fundersnot available
KeywordsCryptococcus gattiiCryptococcosisBiologyCryptococcus neoformansCryptococcusVirulenceDouglas firMicrobiologyImmune systemOutbreakVirulence factorBotanyImmunologyVirology

Abstract

fetched live from OpenAlex

The yeasts Cryptococcus neoformans and Cryptococcus gattii are fungal pathogens that can be isolated from the environment, including the surfaces of many plants. Cryptococcus gattii caused an outbreak on Vancouver Island, British Columbia beginning in 1999 that has since spread to the Pacific Northwest of the United States. Coastal Douglas fir (Pseudotsuga menziesii) is an important lumber species and a major component of the ecosystems in this area. Previous research has explored Cryptococcus survival and mating on Douglas fir plants and plant-derived material, but no studies have been done on the production of cryptococcal virulence factors by cells grown on those media. Here, we investigated the effects of growth on Douglas fir-derived media on the production of the polysaccharide capsule and melanin, two of the most important cryptococcal virulence factors. We found that while the capsule was mostly unchanged by growth in Douglas fir media compared to cells grown in defined minimal media, Cryptococcus spp. can use substrates present in Douglas fir to synthesize functional and protective melanin. These results suggest mechanisms by which Cryptococcus species may survive in the environment and emphasize the need to explore how association with Douglas fir trees could affect its epidemiology for human cryptococcosis.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.650
Threshold uncertainty score0.651

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0000.001
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 head0.013
GPT teacher head0.258
Teacher spread0.245 · 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 designObservational
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

Citations0
Published2024
Admission routes1
Has abstractyes

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