MétaCan
Menu
Back to cohort
Record W2112750959 · doi:10.1088/1748-6041/10/4/045016

An <i>in vitro</i> study of peptide-loaded alginate nanospheres for antagonizing the inhibitory effect of Nogo-A protein on axonal growth

2015· article· en· W2112750959 on OpenAlexafffund
Peng Zhai, Daniel Chen, David J. Schreyer

Bibliographic record

VenueBiomedical Materials · 2015
Typearticle
Languageen
FieldNeuroscience
TopicNerve injury and regeneration
Canadian institutionsUniversity of Saskatchewan
FundersSaskatchewan Health Research Foundation
KeywordsNeuritePeptideAxonCell biologyInhibitory postsynaptic potentialDorsal root ganglionReceptorIn vitroGrowth coneBiophysicsChemistryBiologyBiochemistryNeuroscience

Abstract

fetched live from OpenAlex

The adult mammalian central nervous system has limited ability to regenerate after injury. This is due, in part, to the presence of myelin-associated axon growth inhibitory proteins such as Nogo-A that bind and activate the Nogo receptor, leading to profound inhibition of actin-based motility within the growing axon tip. This paper presents an in vitro study of the use of a Nogo receptor-blocking peptide to antagonize the inhibitory effect of Nogo-A on axon growth. Alginate nanospheres were fabricated using an emulsion technique and loaded with Nogo receptor-blocking peptide, or with other model proteins. Protein release profiles were studied, and retention of the bioactivity of released proteins was verified. Primary dorsal root ganglion neurons were cultured and their ability to grow neurites was challenged with Nogo-A chimeric protein in the absence or presence of Nogo receptor antagonist peptide-loaded alginate nanospheres. Our results demonstrate that peptide released from alginate nanospheres could overcome the growth inhibitory effect of Nogo-A, suggesting that a similar peptide delivery strategy using alginate nanospheres might be used to improve axon regeneration within the injured central nervous system.

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)0.001
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 score0.005
Threshold uncertainty score0.327

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.034
GPT teacher head0.297
Teacher spread0.264 · 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

Citations8
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueBiomedical MaterialsSame topicNerve injury and regenerationFrench-language works237,207