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Record W2734569334 · doi:10.1021/acsami.7b06673

Oppositely Charged Polyurethane Microspheres with Tunable Zeta Potentials as an Injectable Dual-Loaded System for Bone Repair

2017· article· en· W2734569334 on OpenAlexaff
Yi Hou, Nan Jiang, Li Zhang, Yubao Li, Yuezhong Meng, Dongmei Han, Chen Chen, Yuan Yang, Songsong Zhu

Bibliographic record

VenueACS Applied Materials & Interfaces · 2017
Typearticle
Languageen
FieldEngineering
TopicBone Tissue Engineering Materials
Canadian institutionsUniversity of Toronto
FundersMinistry of Science and Technology of the People's Republic of ChinaNational Natural Science Foundation of China
KeywordsMaterials sciencePolyurethaneZeta potentialMicrosphereBiomedical engineeringIn vivoChemical engineeringComposite materialNanotechnologyNanoparticle

Abstract

fetched live from OpenAlex

To effectively repair irregular shaped bone defects by a minimally invasive procedure, the exploration of an injectable gel to fill the defect is desirable. Herein, positively and negatively charged polyurethane microspheres (PU-A and PU-B) with adjustable zeta potentials as well as the hydroxyapatite-loaded PU microsphere (PU-A/HA) and the dexamethasone-loaded PU microsphere (PU-B/Dex) were successfully prepared, and the oppositely charged microspheres could self-assemble into injectable gels with 3D structures by a mutually electrostatic attraction. The self-assembly PU-A/HA+PU-B/Dex gel exhibited a much higher elastic modulus (about 0.20 MPa) and excellent shear-thinning and self-recovery behaviors, which would allow the gel to be injected through a fine syringe to fill the irregular defect. The in vitro and in vivo experiments demonstrated that the coexistence of HA and Dex in PU-A/HA+PU-B/Dex gel had a synergistic effect on cell differentiation and accelerating new bone formation, displaying a good prospect as an injectable gel for bone repair in minimally invasive surgery.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
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 score0.004
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.011
GPT teacher head0.224
Teacher spread0.213 · 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.

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

Citations36
Published2017
Admission routes1
Has abstractyes

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