MétaCan
Menu
Back to cohort
Record W4377086935 · doi:10.3791/65180

Micro-CT Imaging and Morphometric Analysis of Mouse Neonatal Brains

2023· article· en· W4377086935 on OpenAlexaff
Jimena Barbeito‐Andrés, Laura Beatríz Andrini, Mariana Vallejo-Azar, Sara Seguel, Jay Devine, Benedikt Hallgrímsson, Paula N. González

Bibliographic record

VenueJournal of Visualized Experiments · 2023
Typearticle
Languageen
FieldNeuroscience
TopicAnesthesia and Neurotoxicity Research
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCytoarchitectureNeuroimagingMagnetic resonance imagingBrain morphometryBiomedicineSegmentationBiologyComputed tomographyDigitizationBrain developmentMedical imagingNeurosciencePathologyComputer scienceAnatomyArtificial intelligenceRadiologyMedicineComputer visionBioinformatics

Abstract

fetched live from OpenAlex

Neuroimages are a valuable tool for studying brain morphology in experiments using animal models. Magnetic resonance imaging (MRI) has become the standard method for soft tissues, although its low spatial resolution poses some limits for small animals. Here, we describe a protocol for obtaining high-resolution three-dimensional (3D) information on mouse neonate brains and skulls using micro-computed tomography (micro-CT). The protocol includes those steps needed to dissect the samples, stain and scan the brain, and obtain morphometric measurements of the whole organ and regions of interest (ROIs). Image analysis includes the segmentation of structures and the digitization of point coordinates. In sum, this work shows that the combination of micro-CT and Lugol's solution as a contrast agent is a suitable alternative for imaging the perinatal brains of small animals. This imaging workflow has applications in developmental biology, biomedicine, and other sciences interested in assessing the effect of diverse genetic and environmental factors on brain development.

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)0.001
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 score0.007
Threshold uncertainty score0.453

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.004
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 head0.066
GPT teacher head0.448
Teacher spread0.383 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Visualized ExperimentsSame topicAnesthesia and Neurotoxicity ResearchFrench-language works237,207