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Record W2793298189 · doi:10.21037/aes.2018.ab020

AB020. 3D scaffolds for optic nerve regeneration

2018· article· en· W2793298189 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.

Bibliographic record

VenueAnnals of Eye Science · 2018
Typearticle
Languageen
FieldEngineering
TopicGraphene and Nanomaterials Applications
Canadian institutionsHôpital Maisonneuve-Rosemont
Fundersnot available
KeywordsRegeneration (biology)ElectrospinningMaterials scienceScaffoldBiomedical engineeringElectrical conductorPolymerMedicineComposite materialBiologyCell biology

Abstract

fetched live from OpenAlex

Background: Regeneration of nerves or nerve bundles is problematic mainly due to issues of nerves finding their target tissues. A very clear example of this is the lack of treatments for traumatic injury to the optic nerve, something that is associated with surgery or trauma to the skull. Nerve guides have been used to support this for the better part of the last century, unfortunately the clinical improvements in patients receiving this sort of treatment is poor. Large improvements to the type of nerve guides used are needed to make this a viable solution for repair. It has been shown that electrostimulation of cells on conductive polymers can have positive effects on nerve regeneration. There are several material innovations that improve on speed of nerve regeneration; conductive polymer coatings being one example. There are constant improvements on solutions for nerve regeneration in many fields, unfortunately combining these different solutions is often slow. We combine electrospinning, 3D printing and surface modification. Electrospinning allows control over fibrous structures. We tune the surface properties using conductive polymer coatings. The conductive fibrous structure can be integrated in a larger 3D printed scaffold that takes the role of guiding the nerve bundle. Methods: For manufacture of aligned fibers, PCL in chloroform was electrospun on a rotating mandrel. Random fibers were collected on a flat stationary collector. Dip coating was performed by submerging the fibrous scaffold in a solution of PEDOT:PSS in water and isopropanol. An outer layer of PEDOT:tosylate was added using vapor phase polymerization (VPP). 3D printing was performed using an ink consisting of 0.25% alginate and 8.75% gelatin. The ink was cross-linked after printing using 0.4% CaCl 2 . Cell cultures were performed using chick dorsal root ganglia and a mouse neuroblastoma cell line. Ganglia and cells were seeded on the fibrous scaffolds. Electrostimulation was performed using a custom set up at constant DC current and slow pulsed DC current (1 min on off cycle) Materials were imaged using scanning electron microscopy. Cell cultures were stained using ICC and imaged with fluorescence microscopy. Results: All of the materials supported cell growth and neurite extension to some degree. The materials that were coated with PEDOT:tosylate and a combination of PEDOT:tosylate + PEDOT:PSS outperformed the PSS only group. Stimulation with a slow pulsed or constant DC current increased neurite extension on the negative pole, while there was inhibition of neurite growth on the opposite pole. The 3D printed outer layer serves as a biocompatible, bioactive support and guide for the bundle of neurites. Conclusions: The nerve guides can guide nerve growth. The 3D printed scaffold is cell friendly. The construct allows electrostimulation to increase speed of 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.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.211

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 head0.040
GPT teacher head0.315
Teacher spread0.274 · 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