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Record W2070066958 · doi:10.1039/c2lc40891k

Growth and positioning of adipose-derived stem cells in microfluidic devices

2012· article· en· W2070066958 on OpenAlexafffund
Nitin Wadhawan, Harmandeep Kalkat, Kanmani Natarajan, Xiuli Ma, Sivakumar Gajjeraman, Saravanan Nandagopal, Ning Hao, Jing Li, Michael Zhang, Jixian Deng, Bo Xiang, Shadreck Mzengeza, Darren H. Freed, Rakesh C. Arora, Ganghong Tian, Francis Lin

Bibliographic record

VenueLab on a Chip · 2012
Typearticle
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsSt. Boniface HospitalWinnipeg Regional Health AuthorityNational Research Council Institute for BiodiagnosticsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchManitoba Medical Service Foundation
KeywordsHoming (biology)Stem cellEpidermal growth factorMicrofluidicsCell biologyAdipose tissueCellBiologyChemistryCell cultureMedicineMaterials scienceInternal medicineNanotechnologyBiochemistry

Abstract

fetched live from OpenAlex

Stem cells hold great promise for treatment of various degenerative diseases. However, clinical studies have only shown very moderate benefits of cell therapy. We believe that insufficiency of therapeutic benefits is due to limited homing of implanted stem cells to targeted organs. Microfluidic devices are a very useful research tool for quantitative characterizations of stem cells. The present study therefore was to assess the effects of epidermal growth factor (EGF) and direct current electric field (dcEF) on the growth and trafficking of adipose-derived stem cells (ASC). It was found that EGF did not affect cell proliferation in cell-culture flasks. However, ASC proliferated at a higher rate in microfluidic devices with continuous infusion of EGF. Furthermore, we found that ASC migrated toward an EGF gradient in microfluidic devices. Moreover, we found that ASC tended to position perpendicularly to dcEF. The results suggest that EGF and dcEF may be effective in guiding homing and trafficking of implanted ASC.

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 score0.071
Threshold uncertainty score0.281

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 head0.016
GPT teacher head0.246
Teacher spread0.229 · 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

Citations23
Published2012
Admission routes2
Has abstractyes

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