MétaCan
Menu
Back to cohort
Record W4321763625 · doi:10.3791/64974

Whole Central and Peripheral Nervous System Mice Dissection

2023· article· en· W4321763625 on OpenAlexaff
Krystina Rhéaume, Zhiguo Chen, Yong Wang, Camille Plante, Dhanushka Hewa Bostanthirige, Maude Lévesque, Sameh Geha, Lu Q. Le, Jean‐Philippe Brosseau

Bibliographic record

VenueJournal of Visualized Experiments · 2023
Typearticle
Languageen
FieldMedicine
TopicNeuroblastoma Research and Treatments
Canadian institutionsCentre Hospitalier Universitaire de SherbrookeUniversité de Sherbrooke
Fundersnot available
KeywordsSpinal cordAnatomyCentral nervous systemPeripheral nervous systemNervous systemDorsal root ganglionNeuroscienceDissection (medical)MedicineBiologyGanglionPathology

Abstract

fetched live from OpenAlex

Animal models represent the workhorse of the neuroscience field. Despite this, today, there is still no step-by-step protocol to dissect a complete rodent nervous system, nor is there a complete schematic representing it that is freely available. Only methods to harvest the brain, the spinal cord, a specific dorsal root ganglion, and the sciatic nerve (separately) are available. Here, we provide detailed pictures and a schematic of the central and peripheral murine nervous system. More importantly, we outline a robust procedure to perform its dissection. The 30 min pre-dissection step allows isolating the intact nervous system within the vertebra with muscles free of viscera and skin. A 2-4 h dissection follows it under a micro-dissection microscope to expose the spinal cord and the thoracic nerves, and finally peel the whole central and peripheral nervous system off the carcass. This protocol represents a significant step forward in studying the anatomy and pathophysiology of the nervous system globally. For example, the dissected dorsal root ganglions from a neurofibromatosis type I mice model can be further processed for histology to unravel changes in tumor progression.

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)0.002
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.022
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0220.011

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 head0.428
Teacher spread0.398 · 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 designNot applicable
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

Citations1
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Visualized ExperimentsSame topicNeuroblastoma Research and TreatmentsFrench-language works237,207