MétaCan
Menu
Back to cohort
Record W2132759745 · doi:10.1039/c4ib00219a

Magnetic engineering of stable rod-shaped stem cell aggregates: circumventing the pitfall of self-bending

2015· article· en· W2132759745 on OpenAlexaff
Vicard Du, Delphine Fayol, Myriam Reffay, Nathalie Luciani, J.‐C. Bacri, Cyprien Gay, Claire Wilhelm

Bibliographic record

VenueIntegrative Biology · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMicro and Nano Robotics
Canadian institutionsCanadian Nautical Research Society
FundersAgence Nationale de la Recherche
KeywordsTissue engineeringScaffoldBiophysicsSpheroidStem cellChemistryMagnetAdhesionCell biologyMaterials scienceNanotechnologyBiomedical engineeringBiologyComposite materialBiochemistryIn vitroPhysics

Abstract

fetched live from OpenAlex

A current challenge for tissue engineering while restoring the function of diseased or damaged tissue is to customize the tissue according to the target area. Scaffold-free approaches usually yield spheroid shapes with the risk of necrosis at the center due to poor nutrient and oxygen diffusion. Here, we used magnetic forces developed at the cellular scale by miniaturized magnets to create rod-shaped aggregates of stem cells that subsequently matured into a tissue-like structure. However, during the maturation process, the tissue-rods spontaneously bent and coiled into sphere-like structures, triggered by the increasing cell-cell adhesion within the initially non-homogeneous tissue. Optimisation of the intra-tissular magnetic forces successfully hindered the transition, in order to produce stable rod-shaped stem cells aggregates.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.000
Threshold uncertainty score0.001

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.015
GPT teacher head0.229
Teacher spread0.213 · 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 source (direct Gemma or distilled Codex), 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

Citations9
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueIntegrative BiologySame topicMicro and Nano RoboticsFrench-language works237,207