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Record W2460527870 · doi:10.3389/fmats.2016.00027

Nano-Engineered Biomaterials for Tissue Regeneration: What Has Been Achieved So Far?

2016· article· en· W2460527870 on OpenAlex
Sarah McLaughlin, James Podrebarac, Marc Ruel, Erik J. Suuronen, Brian McNeill, Emilio I. Alarcón

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueFrontiers in Materials · 2016
Typearticle
Languageen
FieldEngineering
TopicGraphene and Nanomaterials Applications
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Ottawa Heart Institute Foundation
KeywordsNanotechnologyTissue engineeringRegeneration (biology)Regenerative medicineBiomimetic materialsBiocompatible materialNanomaterialsMaterials scienceBiochemical engineeringEngineeringBiomedical engineeringStem cellBiology

Abstract

fetched live from OpenAlex

Nanomaterials have attracted the interest of tissue engineers for the last two decades. Their unique properties make them promising for de-novo fabrication of bio-inspired hybrid/composite materials with improved regenerative properties, including the capacity for electric conductivity and the provision of antimicrobial properties, for example. However, to this day, the use of such materials in medical applications is rather limited and most of the studies have only reached the archetypical proof-of-concept stage. Herein, we present a review on the use of nanomaterials in tissue engineering for regenerative therapies of heart, skin, eye, skeletal muscle, and nervous system. The advantages and limitations of nano-engineering materials are presented in this review alongside with the future challenges and milestones nanotechnology must overcome to make an impact in biomedical applications.

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.

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 categoriesMeta-epidemiology (narrow)
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 score0.090
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
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 head0.013
GPT teacher head0.216
Teacher spread0.202 · 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