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Record W3215682569 · doi:10.3791/62725

Brain Pericyte Calcium and Hemodynamic Imaging in Transgenic Mice <em>In Vivo</em>

2021· article· en· W3215682569 on OpenAlexafffund
Jessica Meza-Resillas, Noushin Ahmadpour, Michael Stobart, Jillian L. Stobart

Bibliographic record

VenueJournal of Visualized Experiments · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCell Image Analysis Techniques
Canadian institutionsUniversity of Manitoba
FundersResearch ManitobaAzrieli FoundationHealth CanadaUniversity of ManitobaCanadian Institutes of Health ResearchMitacsFondation Brain Canada
KeywordsIn vivoPericyteHemodynamicsCalcium in biologyCalcium imagingBlood flowCalciumBiologyGenetically modified mousePathologyTransgeneCell biologyChemistryMedicineIntracellularEndothelial stem cellInternal medicineIn vitroBiochemistryGene

Abstract

fetched live from OpenAlex

Recent advances in protein biology and mouse genetics have made it possible to measure intracellular calcium fluctuations of brain cells in vivo and to correlate this with local hemodynamics. This protocol uses transgenic mice that have been prepared with a chronic cranial window and express the genetically encoded calcium indicator, RCaMP1.07, under the α-smooth muscle actin promoter to specifically label mural cells, such as vascular smooth muscle cells and ensheathing pericytes. Steps are outlined on how to prepare a tail vein catheter for intravenous injection of fluorescent dyes to trace blood flow, as well as how to measure brain pericyte calcium and local blood vessel hemodynamics (diameter, red blood cell velocity, etc.) by two photon microscopy in vivo through the cranial window in ketamine/xylazine anesthetized mice. Finally, details are provided for the analysis of calcium fluctuations and blood flow movies via the image processing algorithms developed by Barrett et al. 2018, with an emphasis on how these processes can be adapted to other cellular imaging data.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.008
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.011
GPT teacher head0.359
Teacher spread0.348 · 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.

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

Citations4
Published2021
Admission routes2
Has abstractyes

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