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Record W2035814108 · doi:10.1002/bit.20618

Neurogenesis and neuronal communication on micropatterned neurochips

2005· article· en· W2035814108 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

VenueBiotechnology and Bioengineering · 2005
Typearticle
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsInstitute for Biological SciencesInstitute for Microstructural SciencesNational Research Council Canada
Fundersnot available
KeywordsNeurogenesisNeuroscienceBiologyCell biology

Abstract

fetched live from OpenAlex

Neural networks are formed by accurate connectivity of neurons and glial cells in the brain. These networks employ a three-dimensional bio-surface that both assigns precise coordinates to cells during development and facilitates their connectivity and functionality throughout life. Using specific topographic and chemical features, we have taken steps towards the development of poly(dimethylsiloxane; PDMS) neurochips that can be used to generate and study synthetic neural networks. These neurochips have micropatterned structures that permit adequate cell positioning and support cell survival. Within days of plating, cells differentiate into neurons displaying excitability and communication, as evidenced by intracellular calcium oscillations and action potentials. The structural and functional capacities of such simple neural networks open up new opportunities to study synaptic communication and plasticity.

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 score0.250
Threshold uncertainty score0.518

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 head0.010
GPT teacher head0.216
Teacher spread0.207 · 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