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Record W2264861695 · doi:10.1080/09205063.2015.1128248

Bone regeneration and infiltration of an anisotropic composite scaffold: an experimental study of rabbit cranial defect repair

2015· article· en· W2264861695 on OpenAlexaff
Jidong Li, Fu You, Yubao Li, Yi Zuo, Limei Li, Jia‐Xing Jiang, Yili Qu, Minpeng Lu, Yi Man, Qin Zou

Bibliographic record

VenueJournal of Biomaterials Science Polymer Edition · 2015
Typearticle
Languageen
FieldEngineering
TopicBone Tissue Engineering Materials
Canadian institutionsUniversity of Saskatchewan
FundersSpecialized Research Fund for the Doctoral Program of Higher Education of China
KeywordsScaffoldInterconnectivityMaterials scienceBiomedical engineeringRegeneration (biology)IsotropyMedicineCell biology

Abstract

fetched live from OpenAlex

Tissue formation on scaffold outer edges after implantation may restrict cell infiltration and mass transfer to/from the scaffold center due to insufficient interconnectivity, leading to incidence of a necrotic core. Herein, a nano-hydroxyapatite/polyamide66 (n-HA/PA66) anisotropic scaffold with axially aligned channels was prepared with the aim to enhance pore interconnectivity. Bone tissue regeneration and infiltration inside of scaffold were assessed by rabbit cranial defect repair experiments. The amount of newly formed bone inside of anisotropic scaffold was much higher than isotropic scaffold, e.g., after 12 weeks, the new bone volume in the inner pores was greater in the anisotropic scaffolds (>50%) than the isotropic scaffolds (<30%). The results suggested that anisotropic scaffolds could accelerate the inducement of bone ingrowth into the inner pores in the non-load-bearing bone defects compared to isotropic scaffolds. Thus, anisotropic scaffolds hold promise for the application in bone tissue engineering.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.017
GPT teacher head0.262
Teacher spread0.245 · 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 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

Citations10
Published2015
Admission routes1
Has abstractyes

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