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Effect of molecular weight of thermoreversible polymer onin vivo retention of rhBMP-2

2001· article· en· W2037207224 on OpenAlexaff
Tiejun Gao, Hasan Uludağ

Bibliographic record

VenueJournal of Biomedical Materials Research · 2001
Typearticle
Languageen
FieldEngineering
TopicBone Tissue Engineering Materials
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPolymerMethacrylatePolymer chemistryCopolymerMaterials scienceLower critical solution temperatureIn vivoMicelleChemical engineeringChemistryOrganic chemistryComposite materialAqueous solution

Abstract

fetched live from OpenAlex

To investigate the effect of polymer molecular weight (MW) on rhBMP-2 delivery by thermoreversible polymers, four polymers with similar lower critical solution temperatures (20 degrees -22 degrees C) but different MWs were studied. Thermoreversible polymers were based on N-isopropylacrylamide (NiPAM), ethyl methacrylate (EMA), and N-acryloxysuccinimide (NASI), and had MWs of either approximately 49 kDa or approximately 400 kDa. The NASI content was either 0 or 1-1.6%. High MW polymers, irrespective of their NASI content, formed a stable gel with significantly lower water uptake and exhibited a dense micelle with average pore size smaller than the low MW polymers. NiPAM/EMA polymers without NASI did not conjugate with recombinant human bone morphogenetic protein-2 (rhBMP-2). NiPAM/EMA polymers containing NASI, however, gave conjugation with rhBMP-2. For polymers without NASI, a high MW was essential for rhBMP-2 retention when injected intramuscularly in Sprague-Dawley rats. For NASI-containing polymers, the MW of the polymer did not make a significant difference because rhBMP-2 retention was equivalent for different size polymers. We conclude that polymer MW affects rhBMP-2 retention in vivo in polymers designed for physical entrapment of rhBMP-2, but not in polymers designed for chemical conjugation with rhBMP-2.

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 categoriesInsufficient payload (model declined to judge)
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 score0.004
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.015
GPT teacher head0.296
Teacher spread0.281 · 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.

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

Citations27
Published2001
Admission routes1
Has abstractyes

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