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Record W4414962815 · doi:10.1063/5.0287434

Mechanical interaction among cells in hydrogel-based microfluidic assays

2025· article· en· W4414962815 on OpenAlexafffund
Jiaqi Zhang, Pengtao Yue, James J. Feng

Bibliographic record

VenueBiomicrofluidics · 2025
Typearticle
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsUniversity of British Columbia
FundersBasic and Applied Basic Research Foundation of Guangdong ProvinceNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of ChinaNational Science Foundation
KeywordsMicrofluidicsMechanobiologyScaffoldPoromechanicsProcess (computing)CellCell mechanicsExtracellular

Abstract

fetched live from OpenAlex

Hydrogels are widely used in cell cultures and microfluidic organ-on-chip devices as a mimic for an extracellular matrix. Soft and porous, they provide a gentle scaffold for the cell colonies to develop into properly structured tissues and organoids. A key factor in this process is the transmission of forces through the hydrogel, originating from the flowing perfusate and propagating toward and among the cells. Such forces serve as mechanical cues in the proliferation and differentiation of cells and in their aggregation into functional organoids. In this work, we use a poroelastic model to study the mechanical interaction among cells that is mediated by the hydrogel. The model predicts that closely spaced cells induce the formation of prominent “tension ribbons” within the hydrogel, actively pulling neighboring cells together and prompting the development of mutual protrusions. In larger cellular arrays, the deformation patterns become highly heterogeneous, strongly dependent on the relative positions of individual cells. These insights provide valuable guidance for optimizing the design and operational parameters of organ-on-chip devices.

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 categoriesMeta-epidemiology (narrow)
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 score0.039
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.012
GPT teacher head0.265
Teacher spread0.252 · 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.

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
Published2025
Admission routes2
Has abstractyes

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