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Record W4405314836 · doi:10.1016/j.jobab.2024.12.001

Macroporous scaffolds based on biomass polymers and their applications in wound healing

2024· article· en· W4405314836 on OpenAlexvenueno aff
Daqian Gao, William D. Shipman, Ya‐Ping Sun, Joshua Glahn, Leleda Beraki, Henry C. Hsia

Bibliographic record

VenueJournal of Bioresources and Bioproducts · 2024
Typearticle
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsnot available
FundersResearch and DevelopmentOffice of Research and Development
KeywordsWound healingPolymerBiomass (ecology)Biomedical engineeringMaterials scienceMedicineComposite materialBiologySurgeryEcology

Abstract

fetched live from OpenAlex

The rapid advancement of biomedical polymers has raised significant concerns about the disposal of medical polymer waste. Sustainable biomass materials derived from renewable sources in nature have emerged as promising alternatives to petroleum-based polymers for medical applications and tissue engineering due to their abundance, biodegradability, and environmental friendliness. In tissue engineering, interconnected macropores within biomaterials are crucial as they provide space and interfaces for cells, enhancing permeability for nutrient and waste transport. In this review, we summarize recent developments in the use of biomass materials to engineer macroporous tissue engineering scaffolds. We highlight key techniques, such as microparticles assembly, leaching template, and bioprinting that can create macropores within scaffolds composed of biomass materials and their composites. In addition, we investigate the applications of the macroporous scaffolds in wound healing, with a focus on cell behaviors within macroporous constructs and their role in treating chronic wounds. We envision that the combination of the bicontinuous macropores and biomass-based materials can create an ideal cellular environment and provide a powerful platform for wound healing and tissue regeneration.

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.629
Threshold uncertainty score0.320

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.010
GPT teacher head0.247
Teacher spread0.237 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations7
Published2024
Admission routes1
Has abstractyes

Explore more

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