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Record W4323031711 · doi:10.1126/sciadv.ade7689

The catheterized bladder environment promotes Efg1- and Als1-dependent <i>Candida albicans</i> infection

2023· article· en· W4323031711 on OpenAlexaff
Alyssa Ann La Bella, Marissa J. Andersen, Nicholas C Gervais, Jonathan J. Molina, Alex Molesan, Peter V. Stuckey, Lauren Wensing, Clarissa J. Nobile, Rebecca S. Shapiro, Felipe H. Santiago‐Tirado, Ana L. Flores‐Mireles

Bibliographic record

VenueScience Advances · 2023
Typearticle
Languageen
FieldMedicine
TopicUrinary Tract Infections Management
Canadian institutionsUniversity of Guelph
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesNational Institute of Allergy and Infectious DiseasesNational Institute of General Medical Sciences
KeywordsCandida albicansBiofilmMicrobiologyCorpus albicansBacterial adhesinUrinary systemAntifungalBiologyMedicineFilamentationImmunologyInternal medicineBacteriaVirulence

Abstract

fetched live from OpenAlex

Catheter-associated urinary tract infections (CAUTIs) account for 40% of hospital-acquired infections (HAIs). As 20 to 50% of hospitalized patients receive catheters, CAUTIs are one of the most common HAIs, resulting in increased morbidity, mortality, and health care costs. Candida albicans is the second most common CAUTI uropathogen, yet relative to its bacterial counterparts, little is known about how fungal CAUTIs are established. Here, we show that the catheterized bladder environment induces Efg1- and fibrinogen (Fg)–dependent biofilm formation that results in CAUTI. In addition, we identify the adhesin Als1 as the critical fungal factor for C. albicans Fg-urine biofilm formation. Furthermore, we show that in the catheterized bladder, a dynamic and open system, both filamentation and attachment are required, but each by themselves are not sufficient for infection. Our study unveils the mechanisms required for fungal CAUTI establishment, which may aid in the development of future therapies to prevent these infections.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.610
Threshold uncertainty score0.554

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
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 head0.011
GPT teacher head0.278
Teacher spread0.267 · 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 designNot applicable
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

Citations32
Published2023
Admission routes1
Has abstractyes

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