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Record W4388677903 · doi:10.1039/d3tb01490h

Different drug loading methods and antibiotic structure modulate the efficacy of polydopamine nanoparticles as drug nanocarriers

2023· article· en· W4388677903 on OpenAlexafffund
Meera Patel, Nesha May Andoy, Susannah Megan Tran, Keuna Jeon, Ruby May A. Sullan

Bibliographic record

VenueJournal of Materials Chemistry B · 2023
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Surface Interaction Studies
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaOntario Ministry of Research, Innovation and ScienceCanada Foundation for InnovationResearch Corporation for Science Advancement
KeywordsNanocarriersDrugMaterials scienceNanoparticleNanotechnologyDrug deliveryPharmacologyMedicine

Abstract

fetched live from OpenAlex

The inefficient delivery of antimicrobials to their target is a significant factor contributing to antibiotic resistance. As such, smart nanomaterials that respond to external stimuli are extensively explored for precise drug delivery. Here, we investigate how drug loading methods and the structure of antibiotics impact the effectiveness of photothermally active polydopamine nanoparticles (PDNPs) as a laser-responsive drug delivery system. We examine two loading methods: in-synthesis and post-synthesis, and evaluate how laser irradiation affects drug release. Density functional theory calculations are also performed to gain deeper insights into the drug-PDNP interactions. Our findings point to the critical role of antibiotic structure and drug loading method in the laser-responsive capabilities of PDNPs as drug nanocarriers. Our study offers valuable insights for optimizing the design and efficiency of PDNP-based drug delivery systems.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.528

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.014
GPT teacher head0.309
Teacher spread0.295 · 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

Citations6
Published2023
Admission routes2
Has abstractyes

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