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Record W2279126740 · doi:10.1002/adhm.201500517

Textile Technologies and Tissue Engineering: A Path Toward Organ Weaving

2016· review· en· W2279126740 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

VenueAdvanced Healthcare Materials · 2016
Typereview
Languageen
FieldMaterials Science
TopicElectrospun Nanofibers in Biomedical Applications
Canadian institutionsUniversity of Victoria
FundersNational Institute of Biomedical Imaging and BioengineeringNational Institute of Arthritis and Musculoskeletal and Skin DiseasesNational Institute of Dental and Craniofacial ResearchOffice of Naval ResearchNational Heart, Lung, and Blood InstitutePolitecnico di MilanoDivision of Emerging Frontiers in Research and InnovationNational Institutes of HealthNational Science Foundation
KeywordsBiofabricationWeavingTextileTissue engineeringComputer scienceEngineeringMaterials scienceManufacturing engineeringNanotechnologyMechanical engineeringBiomedical engineeringComposite material

Abstract

fetched live from OpenAlex

Textile technologies have recently attracted great attention as potential biofabrication tools for engineering tissue constructs. Using current textile technologies, fibrous structures can be designed and engineered to attain the required properties that are demanded by different tissue engineering applications. Several key parameters such as physiochemical characteristics of fibers, microarchitecture, and mechanical properties of the fabrics play important roles in the effective use of textile technologies in tissue engineering. This review summarizes the current advances in the manufacturing of biofunctional fibers. Different textile methods such as knitting, weaving, and braiding are discussed and their current applications in tissue engineering are highlighted.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.913
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.021
GPT teacher head0.334
Teacher spread0.313 · 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