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Record W4404443163 · doi:10.1016/j.xphs.2024.11.006

Controlled self-assembly of macrocyclic peptide into multifunctional photoluminescent nanoparticles

2024· article· en· W4404443163 on OpenAlexafffund
Ranga Dissanayake, Nauman Nazeer, Adnan Murad Bhayo, Marya Ahmed

Bibliographic record

VenueJournal of Pharmaceutical Sciences · 2024
Typearticle
Languageen
FieldMaterials Science
TopicSupramolecular Self-Assembly in Materials
Canadian institutionsUniversity of Prince Edward Island
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Cancer SocietyCanadian Cardiovascular Society
KeywordsNanoparticleNanotechnologyPhotoluminescencePeptideSelf-assemblyChemistryCombinatorial chemistryMaterials scienceBiochemistryOptoelectronics

Abstract

fetched live from OpenAlex

Self-assembled peptide nanoparticles are unique stimuli responsive biodegradable materials with applications in biomedicines as delivery carriers and imaging agents. This study investigates the controlled self-assembly of chicken Angiogenin 4 derived immunomodulatory macrocyclic peptide (mCA4-5) in the presence of an inert amphipathic stabilizing peptide and as a function of pH, temperature and presence of ions to yield optically active, physiologically stable and biodegradable peptide nanoparticles. The photoluminescent peptide nanoparticles (PLPNs) produced were characterized for the size, surface charge, optical properties and crystallinity. The carvacrol loaded nanoparticles prepared by facile encapsulation of the drug during the self-assembly process were evaluated for the drug release efficacies, as a function of pH and in the presence of reducing agent. Carvacrol loaded, physiologically stable PLPNs obtained with high conversion efficacy were highly effective against planktonic bacteria and bacterial biofilms and efficiently eradicated intracellular bacteria in infected macrophages and fibroblast. Furthermore, the drug-loaded nanoparticles exhibited significant antioxidant activities and immunomodulatory effects, highlighting their multifunctional therapeutic potential.

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)0.004
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 score0.010
Threshold uncertainty score0.925

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.026
GPT teacher head0.346
Teacher spread0.319 · 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

Citations4
Published2024
Admission routes2
Has abstractyes

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