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Record W4382927864 · doi:10.1101/2023.06.30.547242

Plant Cellulose as a Substrate for 3D Neural Stem Cell Culture

2023· preprint· en· W4382927864 on OpenAlexafffund
Lauren Couvrette, Krystal L. A. Walker, Tuan V. Bui, Andrew E. Pelling

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2023
Typepreprint
Languageen
FieldMedicine
TopicTissue Engineering and Regenerative Medicine
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaLi Ka Shing Foundation
KeywordsNeural stem cellNeural tissue engineeringScaffoldCelluloseStem cellCell biologyDecellularizationMesenchymal stem cellSpinal cord injuryTissue engineering3D bioprintingGlial fibrillary acidic proteinNeurosphereChemistryBiologyIn vitroNeuroscienceBiomedical engineeringAdult stem cellMedicineSpinal cordBiochemistryImmunologyImmunohistochemistryEndothelial stem cell

Abstract

fetched live from OpenAlex

Abstract Neural stem cell (NSC) based therapies are at the forefront of regenerative medicine strategies for various neural defects and injuries such as stroke, traumatic brain injury and spinal cord injury. For several clinical applications, NSC therapies require biocompatible scaffolds to support cell survival and to direct differentiation. Here, we investigate decellularized plant tissue as a novel scaffold for three-dimensional (3D) in vitro culture of NSCs. Plant cellulose scaffolds were shown to support attachment and proliferation of adult rat hippocampal neural stem cells (NSCs). Further, NSCs differentiated on the cellulose scaffold had significant increases in their expression of neuron-specific beta-III tubulin and glial fibrillary acidic protein compared to 2D culture on a polystyrene plate, indicating that the scaffold may enhance differentiation of NSCs towards astrocytic and neuronal lineages. Our findings suggest that plant-derived cellulose scaffolds have the potential to be used in neural tissue engineering and can be harnessed to direct differentiation of NSCs.

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 categoriesMeta-epidemiology (narrow)
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 score0.103
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.025
GPT teacher head0.239
Teacher spread0.214 · 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.

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

Citations4
Published2023
Admission routes2
Has abstractyes

Explore more

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