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Using an Optical Clearing Technique for Visualizing the Midbrain Cholinergic System in Mice

2015· article· en· W1183569263 on OpenAlexaff
Alexandria De Santis‐Smith, Tristan Conciatori, Brian L. Allman, Kem A. Rogers, Susanne Schmid

Bibliographic record

VenueThe FASEB Journal · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced Fluorescence Microscopy Techniques
Canadian institutionsWestern University
Fundersnot available
KeywordsCholinergicMidbrainConfocalCholinergic neuronConfocal microscopyCoronal planeNeuroscienceSlice preparationAnatomyChemistryCell biologyBiologyBiophysicsCentral nervous systemOpticsPhysics

Abstract

fetched live from OpenAlex

The 3D visualization of the midbrain cholinergic system is key for understanding its function and its relationship with other brain areas. In preliminary experiments, our lab used an optical clearing technique, CLARITY, along with confocal microscopy, to image cholinergic cells and projections in up to 4 mm thick brain slices. Brains from transgenic mice expressing YFP under the choline aceteyltransferase 1 (CHaT1) promotor were harvested and sliced into coronal sections of different thicknesses. The samples were then embedded into a hydrogel monomer solution for 3 hours and left to incubate at 37°C. Afterwards, the tissue samples were left in a clearing solution for lipid membrane removal and left to incubate at 37 °C between 19 to 44 days.When qualitatively studied, passive CLARITY was found to effectively clear coronal sections of 1, 2, 3 and 4 mm thick after 19, 26, 35 and 44 days of incubation, respectively. Confocal microscopy confirmed sufficient expression of YFP to visualize thin axons and single axon terminals of cholinergic neurons within the rostral regions of the mouse brain. Based on our preliminary results, our ultimate goal is to construct a 3D visualization of the entire midbrain cholinergic system and its projections in the mouse brain.

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)0.002
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 score0.354
Threshold uncertainty score0.321

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.054
GPT teacher head0.359
Teacher spread0.305 · 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
Published2015
Admission routes1
Has abstractyes

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