MétaCan
Menu
Back to cohort
Record W4317553107 · doi:10.1126/sciadv.ade8039

Codelivery of synergistic antimicrobials with polyelectrolyte nanocomplexes to treat bacterial biofilms and lung infections

2023· article· en· W4317553107 on OpenAlexaff
Joel A. Finbloom, Preethi Raghavan, Michael Kwon, Bhushan N. Kharbikar, Michelle Yu, Tejal A. Desai

Bibliographic record

VenueScience Advances · 2023
Typearticle
Languageen
FieldMedicine
TopicInhalation and Respiratory Drug Delivery
Canadian institutionsUniversity of British Columbia
FundersUniversity of California, San FranciscoNational Institutes of HealthNational Science Foundation
KeywordsAntimicrobialBiofilmTobramycinPseudomonas aeruginosaAntibioticsMicrobiologyCystic fibrosisDrug deliveryPolyelectrolyteDrugChemistryMedicineBacteriaBiologyNanotechnologyMaterials sciencePharmacologyGentamicin

Abstract

fetched live from OpenAlex

(PA), have high rates of antimicrobial tolerance and are commonly found in chronic wound and cystic fibrosis lung infections. Combination therapeutics that act synergistically can overcome antimicrobial tolerance; however, the delivery of multiple therapeutics at relevant dosages remains a challenge. We therefore developed a nanoscale drug carrier for antimicrobial codelivery by combining approaches from polyelectrolyte nanocomplex (NC) formation and layer-by-layer electrostatic self-assembly. This strategy led to NC drug carriers loaded with tobramycin antibiotics and antimicrobial silver nanoparticles (AgTob-NCs). AgTob-NCs displayed synergistic enhancements in antimicrobial activity against both planktonic and biofilm PA cultures, with positively charged NCs outperforming negatively charged formulations. NCs were evaluated in mouse models of lung infection, leading to reduced bacterial burden and improved survival outcomes. This approach therefore shows promise for nanoscale therapeutic codelivery to treat recalcitrant bacterial 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)0.000
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 score0.005
Threshold uncertainty score0.258

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.010
GPT teacher head0.286
Teacher spread0.276 · 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

Citations32
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueScience AdvancesSame topicInhalation and Respiratory Drug DeliveryFrench-language works237,207