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Record W2030495029 · doi:10.1089/ten.teb.2012.0006

Strategic Design and Recent Fabrication Techniques for Bioengineered Tissue Scaffolds to Improve Peripheral Nerve Regeneration

2012· review· en· W2030495029 on OpenAlex

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.

Bibliographic record

VenueTissue Engineering Part B Reviews · 2012
Typereview
Languageen
FieldNeuroscience
TopicNerve injury and regeneration
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsRegeneration (biology)ScaffoldTissue engineeringPeripheral nerve injuryRegenerative medicinePeripheral nerveNerve guidance conduitBiomedical engineeringMaterials scienceMedicineStem cellAnatomyCell biologyBiology

Abstract

fetched live from OpenAlex

Bioengineered tissue scaffolds are a potential tool for improving regenerative repair of damaged peripheral nerves. Novel modes of fabrication coupled with scaffold design strategies that are based on an understanding of the biology of nerve injury offer the prospect of intervention at a more sophisticated level. We review the etiology and incidence of peripheral nerve injury and the biological events that unfold during nerve regeneration after an injury. Newly available tissue scaffold fabrication technologies using bioplotting and laser-based techniques are described. Scaffold design strategies are also discussed, including the incorporation of living cells during scaffold fabrication, inclusion of neurotrophic gradients, use of electric stimulation, inclusion of antioxidant compounds to counteract neural apotosis, and promotion of angiogenesis. Use of these advanced fabrication techniques and incorporation of one or more of these active biological strategies may eventually lead to a greater success in peripheral nerve 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.

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.910
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.150
GPT teacher head0.356
Teacher spread0.206 · 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