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Record W2604367237

Reduced Burst Release of Bioactive rhBMP-2 from a Three-Phase Composite Scaffold

2010· dissertation· en· W2604367237 on OpenAlexfundno aff
D. W. Grant

Bibliographic record

VenueTSpace (University of Toronto) · 2010
Typedissertation
Languageen
FieldEngineering
TopicBone Tissue Engineering Materials
Canadian institutionsnot available
FundersUniversity of TorontoMcMaster University
KeywordsScaffoldKineticsChemistryComposite numberBone morphogenetic proteinControlled releaseBiomedical engineeringBiophysicsMaterials scienceBiochemistryNanotechnologyMedicineBiologyComposite material
DOInot available

Abstract

fetched live from OpenAlex

Recombinant human bone morphogenic proteins (rhBMPs) are extensively studied and employed clinically for treatment of various bone defects. Current clinical delivery vehicles suffer wasteful burst releases that mandate supra-physiological dosing driving concerns over safety and cost. It was therefore investigated whether a unique drug delivery vehicle sequestered within a composite scaffold could lower the burst release of rhBMP-2. PLGA-calcium phosphate tri-phasic composite scaffolds delivered model protein BSA with burst release of ~13% and sustained kinetics of 0.5-1.5% BSA/day up to 45 days. rhBMP-2 was delivered with zero burst release however at much lower levels, totaling 0.09% to 0.9 % release over 10 days, but had up to 6.3-fold greater bioactivity than fresh rhBMP-2 (p<0.05). In conclusion, the three-phase composite scaffold can deliver bioactive proteins with a reduced burst release and sustained secondary kinetics.

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 score0.001
Threshold uncertainty score0.002

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 head0.008
GPT teacher head0.226
Teacher spread0.217 · 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

Citations0
Published2010
Admission routes1
Has abstractyes

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