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Record W2266389823 · doi:10.1039/c5nr08175k

Progesterone binding nano-carriers based on hydrophobically modified hyperbranched polyglycerols

2016· article· en· W2266389823 on OpenAlexafffund
Mahsa Alizadeh Noghani, Donald E. Brooks

Bibliographic record

VenueNanoscale · 2016
Typearticle
Languageen
FieldMaterials Science
TopicDendrimers and Hyperbranched Polymers
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsKineticsNano-Receptor–ligand kineticsChemistryPolymer chemistryBiophysicsMaterials scienceChemical engineeringComposite materialBiology

Abstract

fetched live from OpenAlex

Progesterone (Pro) is a potent neurosteroid and promotes recovery from moderate Traumatic Brain Injury but its clinical application is severely impeded by its poor water solubility. Here we demonstrate that reversibly binding Pro within hydrophobically modified hyperbranched polyglycerol (HPG-Cn-MPEG) enhances its solubility, stability and bioavailability. Synthesis, characterization and Pro loading into HPG-Cn-MPEG is described. The release kinetics are correlated with structural properties and the results of Differential Scanning Calorimetry studies of a family of HPG-Cn-MPEGs of varying molecular weight and alkylation. While the maximum amount of Pro bound correlates well with the amount of alkyl carbon per molecule contributing to its hydrophobicity, the dominant first order rate constant for Pro release correlates strongly with the amount of structured or bound water in the dendritic domain of the polymer. The results provide evidence to justify more detailed studies of interactions with biological systems, both single cells and in animal models.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.008
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.002

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 head0.015
GPT teacher head0.235
Teacher spread0.220 · 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; both teacher heads agree on what is shown here.

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

Citations9
Published2016
Admission routes2
Has abstractyes

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