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Nanocellulose-based porous materials: Regulation and pathway to commercialization in regenerative medicine

2023· review· en· W4384010472 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueBioactive Materials · 2023
Typereview
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsUniversity of British Columbia
FundersHorizon 2020European Research CouncilHorizon 2020 Framework ProgrammeCanada Foundation for InnovationConselho Nacional de Desenvolvimento Científico e TecnológicoEuropean CommissionCanada Excellence Research Chairs, Government of CanadaFundação de Amparo à Pesquisa do Estado de São Paulo
KeywordsNanocelluloseNanotechnologyCommercializationRegenerative medicineMaterials scienceBacterial cellulosePorosityNanofiberCelluloseBusinessEngineeringChemical engineeringChemistryComposite material

Abstract

fetched live from OpenAlex

We review the recent progress that have led to the development of porous materials based on cellulose nanostructures found in plants and other resources. In light of the properties that emerge from the chemistry, shape and structural control, we discuss some of the most promising uses of a plant-based material, nanocellulose, in regenerative medicine. Following a brief discussion about the fundamental aspects of self-assembly of nanocellulose precursors, we review the key strategies needed for material synthesis and to adjust the architecture of the materials (using three-dimensional printing, freeze-casted porous materials, and electrospinning) according to their uses in tissue engineering, artificial organs, controlled drug delivery and wound healing systems, among others. For this purpose, we map the structure-property-function relationships of nanocellulose-based porous materials and examine the course of actions that are required to translate innovation from the laboratory to industry. Such efforts require attention to regulatory aspects and market pull. Finally, the key challenges and opportunities in this nascent field are critically reviewed.

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)0.002
metaresearch head score (Gemma)0.001
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: Review · Consensus signal: Review
Teacher disagreement score0.192
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.130
GPT teacher head0.388
Teacher spread0.259 · 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