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Record W2167926695 · doi:10.1096/fj.03-0043fje

Innervated human corneal equivalents as in vitro models for nerve‐target cell interactions

2003· article· en· W2167926695 on OpenAlexafffund
Erik J. Suuronen, Masatsugu Nakamura, Mitchell A. Watsky, Peter K. Stys, Linda Müller, Réjean Munger, Naoshi Shinozaki, May Griffith

Bibliographic record

VenueThe FASEB Journal · 2003
Typearticle
Languageen
FieldMedicine
TopicCorneal Surgery and Treatments
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaSantenInternational Foundation for Ethical Research
KeywordsRegeneration (biology)NeuroscienceIn vitroSensory systemIn vivoAnatomyBiologyFunction (biology)Sensory nerveNerve cellsCorneaCell biologyBiotechnology

Abstract

fetched live from OpenAlex

A sensory nerve supply is crucial for optimal tissue function. However, the mechanisms for successful innervation and the signaling pathways between nerves and their target tissue are not fully understood. Engineered tissue substitutes can provide controllable environments in which to study tissue innervation. We have therefore engineered human corneal substitutes that promote nerve in-growth in a pattern similar to in vivo re-innervation. We demonstrate that these nerves (a) are morphologically equivalent to natural corneal nerves; (b) make appropriate contact with target cells; (c) can generate action potentials; (d) respond to chemical and physical stimuli; and (e) play an important role in the overall functioning of the bioengineered tissue. This model can be used for studying the more general topics of nerve ingrowth or regeneration and the interaction between nerves and their target cells and, more specifically, the role of nerves in corneal function. This model could also be used as an in vitro alternative to animals for safety and efficacy testing of chemicals and drugs.

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 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 score0.028
Threshold uncertainty score0.404

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 head0.059
GPT teacher head0.315
Teacher spread0.256 · 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

Citations66
Published2003
Admission routes2
Has abstractyes

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