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Record W2889179725 · doi:10.3390/ma11091556

Minor Review: An Overview of a Synthetic Nanophase Bone Substitute

2018· review· en· W2889179725 on OpenAlexaff
Steven J. Eppell, Weidong Tong, James McMasters, Yohannes Soenjaya, Anca M. Barbu, Alvin B. Ko, Jonathan Z. Baskin

Bibliographic record

VenueMaterials · 2018
Typereview
Languageen
FieldEngineering
TopicBone Tissue Engineering Materials
Canadian institutionsSunnybrook Health Science Centre
FundersU.S. ArmyNational Institutes of HealthWallace H. Coulter Foundation
KeywordsBiocompatible materialFibrilMaterials scienceCollagen fibrilBiomedical engineeringNanotechnologyBiomaterialBiophysicsChemistryBiochemistry

Abstract

fetched live from OpenAlex

Material is reviewed that consists of reconstituted collagen fibril gel mineralized in a manner that produces biomimetically sized nanoapatites intimately associated with the fibrils. This gel is formed into usable shapes with a modulus and strength that allow it to be surgically press fitted into bony defects. The design paradigm for the material is that the nanoapatites will dissolve into soluble Ca2+ as the collagen is degraded into RGD-containing peptide fragments due to osteoclastic action. This is intended to signal to the osteoclasts to continue removing the material in a biomimetic fashion similar to bony remodeling. Preliminary experiments in a subcutaneous rat model show that the material is biocompatible with respect to inflammatory and immunogenic responses, and that it supports cellular invasion. Preliminary experiments in a critical-sized mandibular defect in rats show that the material is resorbable and functions well as a bone morphogenetic 2 (BMP-2) carrier. We have produced a range of mechanical and biological responses by varying mechanical and chemical processing of the 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.

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)0.001
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 score0.008
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.006

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 head0.081
GPT teacher head0.331
Teacher spread0.251 · 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

Citations5
Published2018
Admission routes1
Has abstractyes

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