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Record W1964312400 · doi:10.1080/08977190410001682854

The Osteoinductive Activity of Bone Morphogenetic Protein (BMP) Purified by Repeated Extracts of Bovine Bone

2004· article· en· W1964312400 on OpenAlexaff
Zhen Ming Hu, Sean Peel, George K.B. Sándor, Cameron M. L. Clokie

Bibliographic record

VenueGrowth Factors · 2004
Typearticle
Languageen
FieldEngineering
TopicBone Tissue Engineering Materials
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBone morphogenetic proteinChemistryBone morphogenetic protein 2Matrix (chemical analysis)In vitroExtraction (chemistry)In vivoBone formationYield (engineering)ChromatographyBone morphogenetic protein 7BiochemistryBiologyBiotechnologyMaterials scienceEndocrinology

Abstract

fetched live from OpenAlex

Native bone morphogenetic proteins (BMPs) extracted from bone have been used clinically to stimulate bone regeneration and repair. However, preparation of purified BMP is a laborious process. This study investigated the yield, activity and cost effectiveness of repeatedly extracting the same bone matrix to produce purified BMP. While repeated extraction was able to increase the yield 62% the activity of the partially purified BMP in later extracts decreased both in vitro and in vivo. This decline in activity appears to be due to an increase in non-BMP contaminants, such as collagen, in the extracts. When the first three extracts were combined and processed together activity was equivalent to that of the first extract. A simple analysis based on the cost of reagents used and the time required for purification indicates that separate processing of the extracts is inefficient while combining the first and second extracts and processing them together would result in a small cost saving. Based on this study we would recommend that the demineralized bone matrix be extracted no more than twice and that the extracts be combined for further processing.

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 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 score0.006
Threshold uncertainty score0.954

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)0.0000.000

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 head0.009
GPT teacher head0.190
Teacher spread0.181 · 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

Citations24
Published2004
Admission routes1
Has abstractyes

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