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Record W2079584071 · doi:10.1089/ten.tea.2006.0436

Enhanced Osteoblast Adhesion on Self-Assembled Nanostructured Hydrogel Scaffolds

2008· article· en· W2079584071 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueTissue Engineering Part A · 2008
Typearticle
Languageen
FieldEngineering
TopicBone Tissue Engineering Materials
Canadian institutionsNational Institute for NanotechnologyUniversity of Alberta
FundersNational Institute on Aging
KeywordsSelf-healing hydrogelsAdhesionOsteoblastBiomedical engineeringMaterials scienceChemistryNanomaterialsGelatinPolymerizationNanotechnologyPolymer chemistryIn vitroComposite materialPolymerOrganic chemistryBiochemistry

Abstract

fetched live from OpenAlex

The objective of the current in vitro study was to improve properties of a commonly used hydrogel for implant applications by incorporating novel self-assembled helical rosette nanotubes (HRNs). Since traditional methods (such as autografts and allografts) used to treat bone defects present various disadvantages (such as donor tissue shortage, extensive inflammation, possible disease transmission, and poor new bone growth), which may lead to implant failure, much effort has been devoted to creating a novel bone substitute that biomimics the nanoscale features of natural bone in order to improve bone growth. HRNs (formed by chemically immobilizing two DNA base pairs) are a novel type of soft nanomaterial that biomimics natural nanostructured components of bone (such as collagen) since they are 3.5 nm in diameter and self-assemble into a helical structure in aqueous solutions. Because HRNs undergo a phase transition from a liquid to a viscous gel when heated to slightly above body temperatures or when added directly to serum-supplemented or serum-free media at body temperatures, they may provide an exciting therapy to heal bone fractures in situ. In this study, HRN-K1 (HRNs functionalized with lysine amino acids) was embedded in and coated on a model hydrogel [specifically, poly(2-hydroxyethyl methacrylate) or pHEMA]. The results of this study showed, for the first time, enhanced osteoblast (bone-forming cell) adhesion on HRN-K1 embedded in and coated on hydrogels compared to hydrogels without HRN-K1. Moreover, the results showed that embedding HRN-K1 into hydrogels can greatly decrease the polymerization time of pHEMA (especially at low temperatures). The presence of lysine in HRN-K1/hydrogels was shown to be one, but not only, property of HRN-K1 that enhanced osteoblast adhesion. In summary, the present results demonstrated that HRNs can improve properties of one particular hydrogel (pHEMA) and, thus, should be further investigated as a bone-healing material.

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.

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 categoriesMeta-epidemiology (narrow), Insufficient 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 score0.189
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
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)0.0000.001

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 head0.196
Teacher spread0.188 · 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