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Record W4389508869 · doi:10.1002/bmm2.12059

Magnetic hydroxyapatite nanobelt‐stem cell hybrid spheroids for remotely patterning bone tissues

2023· article· en· W4389508869 on OpenAlexaff
Min Hao, Wenhan Wang, Anil Kumar, Wan Hairul Anuar Kamaruddin, Syafiqah Saidin, Nik Ahmad Nizam Nik Malek, Jérôme P. Claverie, Hong Liu

Bibliographic record

VenueBMEMat · 2023
Typearticle
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsUniversité de Sherbrooke
FundersFundamental Research Funds for the Central UniversitiesNatural Science Foundation of Shandong Province
KeywordsMesenchymal stem cellStem cellSpheroidCell biologyMaterials scienceBone marrowAdipose tissueCellBiomedical engineeringChemistryNanotechnologyBiologyCell cultureImmunologyMedicineBiochemistry

Abstract

fetched live from OpenAlex

Abstract The low survival rate and poor differentiation efficiency of stem cells, as well as the insufficient integration of implanted stem cells, limit the regeneration of bone defects. Here, we have developed magnetic ferroferric oxide‐hydroxyapatite‐polydopamine (Fe 3 O 4 ‐HAp‐PDA) nanobelts to assemble mesenchymal stem cells (MSCs) into a three‐dimensional hybrid spheroid for patterning bone tissue. These nanobelts, which are featured by their high‐aspect ratio and contain Fe 3 O 4 nanospheres with a PDA coating, can be manipulated by a magnetic field and foster enhanced cell‐nanobelt interactions. This strategy has been demonstrated to be effective for both bone marrow mesenchymal stem cells and adipose‐derived mesenchymal stem cells, enabling remote manipulation of stem cell spheroids and efficient spheroid fusion, which in turn accelerates osteogenic differentiation. Consequently, this methodology serves as an efficient and general tool for bone tissue printing and can potentially overcome the low survival rate and poor differentiation efficiency of stem cells, as well as mismatched interface fusion issues.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.604
Threshold uncertainty score0.999

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)0.0000.002

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 head0.021
GPT teacher head0.258
Teacher spread0.237 · 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 designOther design
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

Citations32
Published2023
Admission routes1
Has abstractyes

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