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Record W2154264349 · doi:10.5430/jbei.v1n1p70

Effect of ultrasound on human umbilical cord peri-vascular cells

2015· article· en· W2154264349 on OpenAlexafffund
Taghreed Aldosary, Hasan Uludağ, Michael R. Doschak, Tarek El‐Bialy

Bibliographic record

VenueJournal of Biomedical Engineering and Informatics · 2015
Typearticle
Languageen
FieldEngineering
TopicUltrasound and Hyperthermia Applications
Canadian institutionsUniversity of Alberta
FundersUniversity of Alberta
KeywordsLow-intensity pulsed ultrasoundAlkaline phosphataseProliferating cell nuclear antigenUmbilical cordStem cellAndrologyFlow cytometryCellTissue engineeringImmunophenotypingCord bloodMedicineImmunologyChemistryBiologyUltrasoundBiomedical engineeringCell biologyImmunohistochemistryTherapeutic ultrasoundBiochemistry

Abstract

fetched live from OpenAlex

Background: Tissue engineering involves using different types of stem cells. One of the roadblocks in tissue engineering is the scant supply of stem cells. The potential use of human umbilical cord peri-vascular Cells (HUCPVCs) has recently been considered as an important cell source for tissue engineering applications. The objective of this study was to explore the effect of low intensity pulsed ultrasound (LIPUS) on HUCPVCs. Materials and methods: HUCPVCs were divided into two groups: treatment group which received 30 mW/cm2 LIPUS for 10 minutes (1, 7, and 14 days) and control group which received sham treatment. The study groups were evaluated for cell count, alkaline phosphatase (ALP) activity, DNA-content, gene expression, and immunophenotype. Results: There was no significant differences in cell count, ALP, DNA-content, and CD-90 between LIPUS and control groups. A significantly higher expression of OSP and PCNA was observed on day 14 in LIPUS treatment group. Conclusion: LIPUS application for 10 minutes per day for 14 days enhanced OSP and PCNA expression without significant increase in cell count of HUCPVCs. Future research may aim at exploring different LIPUS applications (different time and frequency) to optimize HUCPVCs proliferation.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.499
Threshold uncertainty score0.372

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.008
GPT teacher head0.232
Teacher spread0.224 · 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

Citations0
Published2015
Admission routes2
Has abstractyes

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