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Record W1844435770 · doi:10.3109/02652048.2015.1046517

Chitosan nanoparticles as adenosine carriers

2015· article· en· W1844435770 on OpenAlexaff
Mehdi Kazemzadeh‐Narbat, Marla Reid, Marianne Su‐Ling Brooks, Amyl Ghanem

Bibliographic record

VenueJournal of Microencapsulation · 2015
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicAdvanced Drug Delivery Systems
Canadian institutionsDalhousie University
Fundersnot available
KeywordsAdenosineChitosanNanoparticleDrug carrierIn vivoMaterials scienceDrug deliveryIonic bondingNanotechnologyNuclear chemistryBiophysicsChemical engineeringPharmacologyChemistryBiochemistryOrganic chemistryMedicineIon

Abstract

fetched live from OpenAlex

The objective of this research project was to evaluate the potential use of chitosan (CS) nanoparticles (NPs) as a drug delivery system for the molecule adenosine. Adenosine is an essential drug used for treating several health issues especially irregular heart rhythm. However, due to its extremely short half-life in vivo (<10 s), the effective delivery of adenosine in clinical applications is a significant challenge. In this research, adenosine was encapsulated into NPs formed by ionic gelation of CS. The encapsulation efficiency and loading capacity of 20% and 3% were obtained, respectively, by forming a complex between CS NPs and adenosine. The obtained CS NPs had a spherical shape in the size range of 260.6 ± 20.1 nm. Spectrophotometry analysis of the adenosine released in vitro showed an initial burst release phase, a plateau phase, followed by a steady release over a week.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.134
GPT teacher head0.439
Teacher spread0.304 · 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 source (direct Gemma or distilled Codex), 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

Citations21
Published2015
Admission routes1
Has abstractyes

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