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Record W2412500515 · doi:10.1038/srep27693

Biomineralization Guided by Paper Templates

2016· article· en· W2412500515 on OpenAlexaff
Gulden Camci‐Unal, Anna Laromaine, Estrella Hong, Ratmir Derda, George M. Whitesides

Bibliographic record

VenueScientific Reports · 2016
Typearticle
Languageen
FieldEngineering
TopicBone Tissue Engineering Materials
Canadian institutionsUniversity of Alberta
FundersHansjörg Wyss Institute for Biologically Inspired Engineering, Harvard UniversityDefense Advanced Research Projects AgencyUniversity of Texas Health Science Center at HoustonHarvard UniversityNational Science Foundation
KeywordsBiomineralizationScaffoldTemplateMineralization (soil science)PorosityFabricationMaterials scienceNanotechnologyCalciumMineralized tissuesTissue engineeringBiomedical engineeringComputer scienceChemistryChemical engineeringComposite materialDatabase

Abstract

fetched live from OpenAlex

This work demonstrates the fabrication of partially mineralized scaffolds fabricated in 3D shapes using paper by folding, and by supporting deposition of calcium phosphate by osteoblasts cultured in these scaffolds. This process generates centimeter-scale free-standing structures composed of paper supporting regions of calcium phosphate deposited by osteoblasts. This work is the first demonstration that paper can be used as a scaffold to induce template-guided mineralization by osteoblasts. Because paper has a porous structure, it allows transport of O2 and nutrients across its entire thickness. Paper supports a uniform distribution of cells upon seeding in hydrogel matrices, and allows growth, remodelling, and proliferation of cells. Scaffolds made of paper make it possible to construct 3D tissue models easily by tuning material properties such as thickness, porosity, and density of chemical functional groups. Paper offers a new approach to study mechanisms of biomineralization, and perhaps ultimately new techniques to guide or accelerate the repair of bone.

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 score0.090
Threshold uncertainty score0.754

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)0.0010.000

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 head0.010
GPT teacher head0.212
Teacher spread0.202 · 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

Citations49
Published2016
Admission routes1
Has abstractyes

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