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Targeting Matrix Exopolysaccharides to Disrupt <em>Pseudomonas aeruginosa</em> Biofilms in Cystic Fibrosis

2023· preprint· en· W4319161518 on OpenAlexaff
Jonathan Chung, Shafinaz Eisha, Su‐Bin Park, Amanda Sheffield Morris, Isaac William Martin

Bibliographic record

VenuePreprints.org · 2023
Typepreprint
Languageen
FieldMedicine
TopicCystic Fibrosis Research Advances
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsPseudomonas aeruginosaCystic fibrosisBiofilmAntibioticsMicrobiologyExtracellular matrixChronic infectionAntimicrobialBiologyMedicineBacteriaImmunologyInternal medicineImmune systemCell biology

Abstract

fetched live from OpenAlex

In cystic fibrosis (CF), pulmonary infection with Pseudomonas aeruginosa is a cause of increased morbidity and mortality, especially in patients for whom infection becomes chronic and there is reliance on long-term suppressive therapies. Current antimicrobials, though varied mechanistically and by mode of delivery, are inadequate not only due to eradication failure in many cases, but also because they do not halt the progression of lung function decline over time. One of the reasons for this failure is thought to be the biofilm mode of growth of P. aeruginosa, wherein self-secreted exopolysaccharides (EPSs) provide physical protection against antibiotics and an array of niches with resulting metabolic and phenotypic heterogeneity. Targeting the three EPSs secreted by P. aeruginosa (alginate, Psl and Pel) is currently under investigation as a way of disrupting the biofilm extracellular matrix to potentiate the action of antibiotics. In this review, we look at each EPS as a potential therapeutic target for combatting pulmonary infection with P. aeruginosa in CF, with a particular focus on the current evidence for these emerging therapies and barriers to bringing these therapies into clinic.

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)0.003
metaresearch head score (Gemma)0.014
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.488
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.014
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.008
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0020.010

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 head0.065
GPT teacher head0.365
Teacher spread0.300 · 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; both teacher heads agree on what is shown here.

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

Citations6
Published2023
Admission routes1
Has abstractyes

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Same venuePreprints.orgSame topicCystic Fibrosis Research AdvancesFrench-language works237,207