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Record W2891492664 · doi:10.1002/cpcb.62

An On‐Chip Method for Long‐Term Growth and Real‐Time Imaging of Brain Organoids

2018· article· en· W2891492664 on OpenAlexfundno aff
Eyal Karzbrun, Rami Yair Tshuva, Orly Reiner

Bibliographic record

VenueCurrent Protocols in Cell Biology · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPluripotent Stem Cells Research
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchCanadian HIV Trials Network, Canadian Institutes of Health ResearchInternational Development Research CentreIsrael Science FoundationNella and Leon Benoziyo Center for Neurological Diseases, Weizmann Institute of ScienceUnited States - Israel Binational Science FoundationHuman Frontier Science Program
KeywordsOrganoidComputer scienceCell biologyNeuroscienceNanotechnologyBiologyMaterials science

Abstract

fetched live from OpenAlex

Brain organoids are an emerging technique for studying human neurodevelopment in vitro, with biomedical implications. However, three-dimensional tissue culture poses several challenges, including lack of nutrient exchange at the organoid core and limited imaging accessibility of whole organoids. Here we present a method for culturing organoids in a micro-fabricated device that enables in situ real-time imaging over weeks with efficient nutrient exchange by diffusion. Our on-chip approach offers a means for studying the dynamics of organoid development, cell differentiation, cell cycle, and motion. © 2018 by John Wiley & Sons, Inc.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods · Consensus signal: Methods
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.030
GPT teacher head0.405
Teacher spread0.374 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

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

Citations24
Published2018
Admission routes1
Has abstractyes

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