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Record W2593992308 · doi:10.7150/ijms.18137

High quality <i>in vitro</i> expansion of human endothelial progenitor cells of human umbilical vein origin

2017· article· en· W2593992308 on OpenAlexfundno aff
Yan Mou, Zhen Yue, Haiying Zhang, Xu Shi, Mingrui Zhang, Xiaona Chang, Hang Gao, Ronggui Li, Zonggui Wang

Bibliographic record

VenueInternational Journal of Medical Sciences · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAngiogenesis and VEGF in Cancer
Canadian institutionsnot available
FundersNational Natural Science Foundation of ChinaPeople's Government of Jilin ProvinceUniversity of ManitobaNational Science Foundation
KeywordsUmbilical veinCD34Progenitor cellAngiogenesisIn vitroCell biologyRegenerative medicineFlow cytometryEndothelial progenitor cellImmunologyStem cellVasculogenesisHuman umbilical vein endothelial cellBiologyChemistryMolecular biologyCancer researchBiochemistry

Abstract

fetched live from OpenAlex

The limited availability of qualified endothelial progenitor cells (EPCs) is a major challenge for regenerative medicine. In the present study, we isolated human EPCs from human umbilical vein endothelial cells (HUVECs) by using magnetic micro-beads coated with an antibody against human CD34. Flow cytometric assay showed that majority of these cells expressed VEGFR2 (KDR), CD34 and CD133, three molecular markers for early EPCs. It was also found that a bioreactor micro-carrier cell culture system (bio-MCCS) was superior to dish culture for in vitro expansion of EPCs. It expanded more EPCs which were in the early stage, as shown by the expression of characteristic molecular markers and had better angiogenic potential, as shown by matrix-gel based in vitro angiogenesis assay. These results suggest that HUVECs might be a novel promising resource of EPCs for regenerative medicine and that a bio-MCCS cell culture system might be broadly used for in vitro expansion of EPCs.

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)0.002
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 score0.006
Threshold uncertainty score0.235

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.047
GPT teacher head0.404
Teacher spread0.357 · 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

Citations10
Published2017
Admission routes1
Has abstractyes

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