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Record W2332599214 · doi:10.3727/096368913x675755

Long-Term Effects of Magnetically Targeted Ferumoxide-Labeled Human Neural Stem Cells in Focal Cerebral Ischemia

2013· article· en· W2332599214 on OpenAlexaff
Miyeoun Song, Young Ju Kim, Yoon-Ha Kim, Jina Roh, Eun‐Cheol Kim, Hong Jun Lee, Seung Up Kim, Byung‐Woo Yoon

Bibliographic record

VenueCell Transplantation · 2013
Typearticle
Languageen
FieldNeuroscience
TopicNeurogenesis and neuroplasticity mechanisms
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsNeural stem cellIschemiaStem cellMedicineAngiogenesisStem-cell therapyTail veinMiddle cerebral arteryStroke (engine)NeurosciencePathologyTransplantationInternal medicineBiologyCell biologyIn vivo

Abstract

fetched live from OpenAlex

The long-term effect of magnetically targeted neural stem cells in a rat focal cerebral ischemia model was investigated. In middle cerebral artery occlusion (MCAO) stroke model rats, ferumoxide-labeled human neural stem cells (hNSCs) were injected into the tail vein. MCAO rats were divided into three groups: ischemia only (IO), ischemia with NSC injection (IC), and ischemia with NSC injection and the use of magnet targeting (IM). Four weeks after MCAO and 3 weeks posttransplantation, a greater number of hNSCs were found in ischemic lesion sites in IM rat brain compared with IO and IC animals. In addition, differentiation of hNSCs into neurons or astrocytes and angiogenesis were markedly increased. In IM rats, infarct volume was considerably reduced, and function was significantly improved. The present study indicates that long-term use of magnetic fields may be a useful way to improve the efficacy of targeted migration of stem cells and functional deficits in stem cell-based therapy for ischemic brain injury.

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 score0.041
Threshold uncertainty score0.981

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 head0.013
GPT teacher head0.220
Teacher spread0.206 · 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

Citations40
Published2013
Admission routes1
Has abstractyes

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