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Record W2605945747 · doi:10.1039/c7bm00146k

The scaffold microenvironment for stem cell based bone tissue engineering

2017· review· en· W2605945747 on OpenAlexaff
Zhichao Hao, Zhenhua Song, Jun Huang, Keqing Huang, Amanda Panetta, Zhipeng Gu, Jun Wu

Bibliographic record

VenueBiomaterials Science · 2017
Typereview
Languageen
FieldEngineering
TopicBone Tissue Engineering Materials
Canadian institutionsUniversity of Windsor
FundersNational Natural Science Foundation of China
KeywordsScaffoldTissue engineeringMechanism (biology)Stem cellBone tissueFunction (biology)Biomedical engineeringCell biologyChemistryBiologyEngineeringPhysics

Abstract

fetched live from OpenAlex

Bone tissue engineering uses the principles and methods of engineering and life sciences to study bone structure, function and growth mechanism for the purposes of repairing, maintaining and improving damaged bone tissue. Scaffolds not only provide structural support for stem cells in cell adhesion and proliferation and bone formation, but also serve as a microenvironment for guiding stem cell differentiation and tissue regeneration and for controlling tissue structure. This review presents the research status of the scaffold microenvironment for bone-related stem cells based on bone tissue engineering. Scaffold materials and the stem cell microenvironment are described in this review, and the existing shortcomings are also simply mentioned.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.041
GPT teacher head0.278
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations153
Published2017
Admission routes1
Has abstractyes

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