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Record W3041086365 · doi:10.1016/j.isci.2020.101350

Bright Ferritin—a Reporter Gene Platform for On-Demand, Longitudinal Cell Tracking on MRI

2020· article· en· W3041086365 on OpenAlexafffund
Daniel A. Szulc, Xavier Lee, Hai‐Ying Mary Cheng, Hai‐Ling Margaret Cheng

Bibliographic record

VenueiScience · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Interference and Gene Delivery
Canadian institutionsTed Rogers Centre for Heart ResearchUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchCanada First Research Excellence FundCanada Foundation for Innovation
KeywordsTracking (education)FerritinOn demandGeneComputational biologyChemistryCell biologyNanotechnologyBiologyComputer sciencePsychologyMultimediaBiochemistryMaterials science

Abstract

fetched live from OpenAlex

A major unresolved challenge in cell-based regenerative medicine is the absence of non-invasive technologies for tracking cell fate in deep tissue and with high spatial resolution over an extended interval. MRI is highly suited for this task, but current methods fail to provide longitudinal monitoring or high sensitivity, or both. In this study, we fill this technological gap with the first discovery and demonstration of in vivo cellular production of endogenous bright contrast via an MRI genetic reporter system that forms manganese-ferritin nanoparticles. We demonstrate this technology in human embryonic kidney cells genetically modified to stably overexpress ferritin and show that, in the presence of manganese, these cells produce far greater contrast than conventional ferritin overexpression with iron or manganese-permeable cells. In living mice, diffusely implanted bright-ferritin cells produce the highest and most sustained contrast in skeletal muscle. The bright-ferritin platform has potential for on-demand, longitudinal, and sensitive cell tracking in vivo.

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)0.000
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 score0.008
Threshold uncertainty score0.440

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.043
GPT teacher head0.277
Teacher spread0.234 · 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

Citations17
Published2020
Admission routes2
Has abstractyes

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