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Record W2032516709 · doi:10.1002/jbm.b.30749

Fibroblastic interactions with high‐porosity Ti‐6Al‐4V metal foam

2007· article· en· W2032516709 on OpenAlexaffabout
Serene Cheung, Maxime Gauthier, Louis‐Philippe Lefebvre, Michael Dunbar, Mark Filiaggi

Bibliographic record

VenueJournal of Biomedical Materials Research Part B Applied Biomaterials · 2007
Typearticle
Languageen
FieldEngineering
TopicBone Tissue Engineering Materials
Canadian institutionsQueen Elizabeth II Health Sciences CentreNational Research Council CanadaDalhousie University
Fundersnot available
KeywordsPorosityMaterials scienceMetal foamFibroblastScaffoldCell growthComposite materialBridging (networking)Cell cultureBiomedical engineeringChemistryMedicine

Abstract

fetched live from OpenAlex

A novel metallic Ti-6Al-4V foam in development at the National Research Council of Canada was investigated for its ability to foster cell attachment and growth using a fibroblast cell culture model. The foam was manufactured via a powder metallurgical process that could produce interconnected porosity greater than 70%. Cell attachment was assessed after 6 and 24 h, while proliferation was examined after 3 and 7 days. Ingrown fibroblasts displayed a number of different morphologies; some fibroblasts were spread thinly in close apposition with the irregular surface, or more often had several anchorage points and extended in three dimensions as they spanned pore space. It was also demonstrated that fibroblasts were actively migrating through the porous scaffold over a 14-day period. In a 60-day extended culture, fibroblasts were bridging and filling macropores and had extensively infiltrated the foams. Overall, it was established that this foam was supportive of cell attachment and proliferation, migration through the porous network, and that it was capable of sustaining a large cell population.

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 score0.003

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)0.0000.000

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 head0.026
GPT teacher head0.300
Teacher spread0.274 · 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

Citations15
Published2007
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Biomedical Materials Research Part B Applied BiomaterialsSame topicBone Tissue Engineering MaterialsFrench-language works237,207