MétaCan
Menu
Back to cohort
Record W2067516141 · doi:10.1142/s0219519415500736

MODELING MECHANICAL CELL DAMAGE IN THE BIOPRINTING PROCESS EMPLOYING A CONICAL NEEDLE

2015· article· en· W2067516141 on OpenAlexaff
Minggan Li, Janusz A. Koziński, Daniel Chen, Dae Kun Hwang

Bibliographic record

VenueJournal of Mechanics in Medicine and Biology · 2015
Typearticle
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsYork UniversityUniversity of SaskatchewanToronto Metropolitan University
Fundersnot available
KeywordsMaterials scienceBiofabricationProcess (computing)Cell damageBiomedical engineeringTissue engineeringComputer scienceChemistryEngineering

Abstract

fetched live from OpenAlex

Biofabrication technologies involve the incorporation of living cells into various bioproducts by employing different cell manipulation techniques. Among them, bioprinting, delivering cell suspension through a fine needle under pressurized air, has been widely used because of its capability of precise process control. In the cell-printing process of bioprinting, cells are exposed to fluid stresses due to the velocity gradient in the fine needle. If the stresses exceed a certain level, the cell membrane may be overstretched, leading to membrane failure and thus causing mechanical cell damage. Modeling the mechanical cell damage in the bioprinting process is a challenging task due to the complex fluid flow and cell deformation involved. This paper introduces a novel method based on computational fluid dynamics (CFD) to represent the mechanical cell damage in the bioprinting process using a conical needle. Specifically, the cell deformation and movement during the cell-fluid interaction processes were represented by the immersed boundary method (IBM). A strain energy density (SED)-based cell damage criterion was developed and used to determine cell damage. Experiments were performed by using 3T3 fibroblasts and the results agree well with the proposed model.

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)0.006
metaresearch head score (Gemma)0.002
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 score0.858
Threshold uncertainty score0.497

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.002
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 integrity0.0000.001
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 head0.140
GPT teacher head0.383
Teacher spread0.243 · 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

Citations46
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Mechanics in Medicine and BiologySame topic3D Printing in Biomedical ResearchFrench-language works237,207