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Record W3202541944 · doi:10.1128/mbio.02446-21

Preclinical Evaluation of Recombinant Microbial Glycoside Hydrolases in the Prevention of Experimental Invasive Aspergillosis

2021· article· en· W3202541944 on OpenAlexafffund
Hanna Ostapska, Deepa Raju, Mélanie Lehoux, Ira Lacdao, Stephanie Gilbert, Piyanka Sivarajah, Natalie C. Bamford, Perrin Baker, Thi Tuyet Mai Nguyen, Caitlin Zacharias, Fabrice N. Gravelat, P. Lynne Howell, Donald C. Sheppard

Bibliographic record

VenuemBio · 2021
Typearticle
Languageen
FieldMedicine
TopicAntifungal resistance and susceptibility
Canadian institutionsUniversity of TorontoMcGill UniversityHospital for Sick ChildrenInstitute of Infection and ImmunityMcGill University Health Centre
FundersCanadian Glycomics NetworkU.S. ArmyCystic Fibrosis Canada
KeywordsAspergillus fumigatusAspergillosisMicrobiologyBiofilmAspergillusImmune systemMedicineBiologyImmunologyBacteria

Abstract

fetched live from OpenAlex

The biofilm-forming mold Aspergillus fumigatus is a common causative agent of invasive fungal airway disease in patients with a compromised immune system or chronic airway disease. Treatment of A. fumigatus infection is limited by the few available antifungals to which fungal resistance is becoming increasingly common. The high mortality rate of A. fumigatus-related infection reflects a need for the development of novel therapeutic strategies. The fungal biofilm matrix is in part composed of the adhesive exopolysaccharide galactosaminogalactan, against which antifungals are less effective. Previously, we demonstrated antibiofilm activity with recombinant forms of the glycoside hydrolase enzymes that are involved in galactosaminogalactan biosynthesis. In this study, prophylaxis with glycoside hydrolases alone or in combination with the antifungal posaconazole in a mouse model of experimental aspergillosis improved outcomes. This study offers insight into the therapeutic potential of combining biofilm disruptive agents to leverage the activity of currently available antifungals.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.124
Threshold uncertainty score0.281

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.093
GPT teacher head0.390
Teacher spread0.297 · 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

Citations14
Published2021
Admission routes2
Has abstractyes

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