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Record W4405009265 · doi:10.1016/j.dib.2024.111182

Data characterizing a panel of biodegradable cross-linked polyester implants for sustained delivery of an anti-viral drug

2024· article· en· W4405009265 on OpenAlexafffund
Sungmin Jung, Jack Bufton, Zeqing Bao, W-S. Cho, Dean J. Aguiar, Christine Allen

Bibliographic record

VenueData in Brief · 2024
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPolyesterDrug deliveryResearch articleDrugMedicineChemistryPharmacologyComputer scienceMaterials scienceNanotechnologyComposite materialLibrary science

Abstract

fetched live from OpenAlex

Tenofovir alafenamide (TAF) is currently administered orally to patients for treatment of chronic hepatitis B virus infection and as a part of a combination therapy for human immunodeficiency virus (HIV) infection. A long-acting delivery system could provide several advantages as a formulation strategy for this drug including improved patient adherence, convenience, more consistent drug levels and potentially fewer side effects. To date, the vast majority of polymer-based long-acting delivery systems have been prepared from poly(lactide- co -glycolide) [ 1 ]. To expand the range of polymers available for use, cross-linkable allyl functionalized, polyester copolymers were considered for preparation of disc-shaped, implantable delivery systems for TAF. The physico-chemical properties of the implants were evaluated including thermal and spectral properties as well as in vitro stability. Subsequently the discs were loaded with TAF via a swelling-equilibrium approach and the discs were further characterized including, TAF loading and in vitro drug release. This dataset shows the potential of using these polymeric materials as a sustained delivery platform for TAF. The dataset is freely available on Mendeley Data.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.466
Threshold uncertainty score0.411

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.107
GPT teacher head0.402
Teacher spread0.294 · 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

Citations1
Published2024
Admission routes2
Has abstractyes

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