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Record W4405394727 · doi:10.1016/j.jobab.2024.11.006

Cellulose-based suture: State of art, challenge, and future outlook

2024· article· en· W4405394727 on OpenAlexvenueno aff
Meiyan Wu, Lei Ding, Xiaoying Bai, Mehdi Rahmaninia, Yiran Li, Bin Li

Bibliographic record

VenueJournal of Bioresources and Bioproducts · 2024
Typearticle
Languageen
FieldMedicine
TopicSurgical Sutures and Adhesives
Canadian institutionsnot available
FundersIran National Science FoundationQingdao Municipal Science and Technology BureauChinese Academy of SciencesNational Natural Science Foundation of China
KeywordsCelluloseState (computer science)Fibrous jointState of artPolymer scienceMaterials scienceChemical engineeringComputer scienceEngineeringMedicineData scienceSurgeryAlgorithm

Abstract

fetched live from OpenAlex

Surgical sutures as the most widely used and high-value implanted materials are of vital importance in wound closure and healing. Among them, cellulose-based sutures with multifunctionality have been developed in recent decades, and are very promising to replace the fossil-based synthetic sutures. Therefore, this paper aims at covering the history and recent advances of cellulose-based suture, mainly including the materials used (e.g., natural cellulose, nanocellulose, and regenerated cellulose), fabrication methods and mechanism of wet spinning and the recently developed interfacial polyelectrolyte complexation spinning, as well as suture application performance (such as mechanical properties, cytocompatibility, biodegradability, absorbable properties, and antibacterial properties). More importantly, it summarizes all cellulose-based sutures, and then delves deep into the challenges and future outlook. Thus, this review provides an important reference for the development of high-end cellulose-based medical sutures.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.889
Threshold uncertainty score0.337

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.009
GPT teacher head0.232
Teacher spread0.224 · 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 designNot applicable
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

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