MétaCan
Menu
Back to cohort
Record W2101097858 · doi:10.1002/mrm.20747

In vivo magnetic resonance imaging of single cells in mouse brain with optical validation

2005· article· en· W2101097858 on OpenAlexafffund
Chris Heyn, John A. Ronald, Lisa T. MacKenzie, Ian C. MacDonald, Ann F. Chambers, Brian K. Rutt, Paula J. Foster

Bibliographic record

VenueMagnetic Resonance in Medicine · 2005
Typearticle
Languageen
FieldEngineering
TopicPhotoacoustic and Ultrasonic Imaging
Canadian institutionsWestern UniversityRobarts Clinical TrialsUniversity of Toronto
FundersCanadian Institutes of Health ResearchMultiple Sclerosis Society of CanadaHeart and Stroke Foundation of Canada
KeywordsMagnetic resonance imagingIn vivoNuclear magnetic resonanceConfocalPreclinical imagingMaterials scienceMagnetic resonance microscopySingle-cell analysisBiomedical engineeringCellChemistryMedicineOpticsPhysicsBiologySpin echoRadiology

Abstract

fetched live from OpenAlex

In the current work we demonstrate, for the first time, that single cells can be detected in mouse brain in vivo using magnetic resonance imaging (MRI). Cells were labeled with superparamagnetic iron oxide nanoparticles and injected into the circulation of mice. Individual cells trapped within the microcirculation of the brain could be visualized with high-resolution MRI using optimized MR hardware and the fast imaging employing steady state acquisition (FIESTA) pulse sequence on a 1.5 T clinical MRI scanner. Single cells appear as discrete signal voids on MR images. Direct optical validation was provided by coregistering signal voids on MRI with single cells visualized using high-resolution confocal microscopy. This work demonstrates the sensitivity of MRI for detecting single cells in small animals for a wide range of application from stem cell to cancer cell tracking.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.215
Teacher spread0.209 · 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
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

Citations273
Published2005
Admission routes2
Has abstractyes

Explore more

Same venueMagnetic Resonance in MedicineSame topicPhotoacoustic and Ultrasonic ImagingFrench-language works237,207