MétaCan
Menu
Back to cohort
Record W2966751640 · doi:10.11159/icbes19.104

Numerical Simulation by CFD of the Growth of Osteocytes within a Bioreactor

2019· article· en· W2966751640 on OpenAlexvenueno aff
Concepción Paz, Eduardo Suárez, Jesús Vence, Andrea Sande

Bibliographic record

VenueProceedings of the World Congress on Electrical Engineering and Computer Systems and Science · 2019
Typearticle
Languageen
FieldMedicine
TopicTissue Engineering and Regenerative Medicine
Canadian institutionsnot available
Fundersnot available
KeywordsComputational fluid dynamicsBioreactorComputer scienceEngineeringAerospace engineeringChemistry

Abstract

fetched live from OpenAlex

For years bone grafts have been used to regenerate or replace damaged bones, treat fractures or bone diseases, which requires surgical intervention and all the risks and problems that this entails, such as: immune response, transmission of diseases etc. As a consequence of the limitations of this technique was born the engineering of bone tissues. The objective of this new type of tissue engineering is to carry out the cellular regeneration of the damaged bone tissue until completely re-establishing its functionality. This requires the creation of artificial porous structures (scaffolding or three-dimensional matrices) biocompatible in which bone cells (osteocytes) extracted from the patient's own tissue are cultured. Achieving adequate oxygen supply, a high cell density and a uniform distribution of cells over three-dimensional scaffolds has become a challenge in recent years. For this reason, a numerical model of osteocyte growth has been implemented in ANSYS Fluent. The model includes the oxygen and nutrient consumption of biomass, the effect of shear stress on cell proliferation and the specific growth rate of osteocytes. This model was evaluated in realistic threedimensional scaffolds inside a perfusion bioreactor and it has been proved that the cell proliferation is conditioned by the wall shear stress.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.840
Threshold uncertainty score0.216

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.005
GPT teacher head0.210
Teacher spread0.205 · 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 designSimulation or modeling
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

Citations1
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the World Congress on Electrical Engineering and Computer Systems and ScienceSame topicTissue Engineering and Regenerative MedicineFrench-language works237,207