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Record W1994360507 · doi:10.3389/neuro.16.006.2009

Adhesion molecule-modified biomaterials for neural tissue engineering

2009· article· en· W1994360507 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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueFrontiers in Neuroengineering · 2009
Typearticle
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsnot available
FundersUniversity of PittsburghOhio State UniversityGeorgia Institute of TechnologyUniversity of TorontoYale University
KeywordsRegeneration (biology)Tissue engineeringMechanism (biology)Neural cellAdhesionNeural tissue engineeringCell adhesionBiomoleculeCell adhesion moleculeNeuroscienceNanotechnologyChemistryMaterials scienceCellBiomedical engineeringCell biologyBiologyMedicineBiochemistry

Abstract

fetched live from OpenAlex

Adhesion molecules (AMs) represent one class of biomolecules that promote central nervous system regeneration. These tethered molecules provide cues to regenerating neurons that recapitulate the native brain environment. Improving cell adhesive potential of non-adhesive biomaterials is therefore a common goal in neural tissue engineering. This review discusses common AMs used in neural biomaterials and the mechanism of cell attachment to these AMs. Methods to modify materials with AMs are discussed and compared. Additionally, patterning of AMs for achieving specific neuronal responses is explored.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.436
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.012
GPT teacher head0.252
Teacher spread0.240 · 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