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Record W3006966574 · doi:10.1002/jbm.a.36909

Production and evaluation of biosynthesized cellulose tubes as promising nerve guides for spinal cord injury treatment

2020· article· en· W3006966574 on OpenAlexafffund
Taisa Regina Stumpf, Linda Tang, Kathlyn M. Kirkwood, Xiuying Yang, Jingchang Zhang, Xudong Cao

Bibliographic record

VenueJournal of Biomedical Materials Research Part A · 2020
Typearticle
Languageen
FieldEngineering
TopicGraphene and Nanomaterials Applications
Canadian institutionsCarleton UniversityUniversity of Ottawa
FundersStem Cell Network
KeywordsSpinal cord injuryNerve growth factorRegeneration (biology)Spinal cordDrug deliveryCelluloseMedicineBiomedical engineeringChemistryMaterials scienceBiologyCell biologyInternal medicineBiochemistryNanotechnology

Abstract

fetched live from OpenAlex

Spinal cord injury (SCI) is a central nervous disorder that can result in permanent motor and sensory damage due to a severed communication pathway. Although there is currently no effective treatment, nerve guide tubes have been used to bridge the injured stumps and act as drug delivery systems. In this study, biosynthesized cellulose (BC) nerve guides were prepared, and nerve growth factor (NGF)-a model growth factor-was incorporated into the tubular nerve guide in order to obtain a nerve guide/drug delivery system to assist the regeneration. To achieve this, Gluconacetobacter hansenii was cultivated in a special bioreactor to produce biosynthesized cellulose tubes (BCTs) in situ, and the physical and mechanical properties of the BCTs obtained from different cultivation time points were evaluated. Our results showed that the properties of the BCTs were comparable to those of the native human neural tissues, and that the NGF released from the BCTs was bioactive for at least 7 days as evaluated by PC12 cell cultures in vitro. In summary, this study evaluated the use of BCT as a drug releasing nerve guide, and our results showed that the BCT is an attractive strategy to enhance nerve regeneration after the SCI.

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)0.003
metaresearch head score (Gemma)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.317

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
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 head0.149
GPT teacher head0.414
Teacher spread0.265 · 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

Citations16
Published2020
Admission routes2
Has abstractyes

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