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Record W2426591181 · doi:10.1039/c6lc00368k

Magnetic domain wall tweezers: a new tool for mechanobiology studies on individual target cells

2016· article· en· W2426591181 on OpenAlexaff
Marco Monticelli, Dario Valter Conca, Edoardo Albisetti, A. Torti, Parikshit Pratim Sharma, Gururaj Rao Kidiyoor, Sara Barozzi, Dario Parazzoli, Pasquale Ciarletta, Monica Lupi, Daniela Petti, R. Bertacco

Bibliographic record

VenueLab on a Chip · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCellular Mechanics and Interactions
Canadian institutionsToronto Metropolitan University
FundersFondazione Cariplo
KeywordsMechanobiologyMagnetic tweezersDomain (mathematical analysis)TweezersOptical tweezersNanotechnologyComputer scienceBiologyCell biologyMaterials sciencePhysicsOpticsMathematics

Abstract

fetched live from OpenAlex

In vitro tests are of fundamental importance for investigating cell mechanisms in response to mechanical stimuli or the impact of the genotype on cell mechanical properties. In particular, the application of controlled forces to activate specific bio-pathways and investigate their effects, mimicking the role of the cellular environment, is becoming a prominent approach in the emerging field of mechanobiology. Here, we present an on-chip device based on magnetic domain wall manipulators, which allows the application of finely controlled and localized forces on target living cells. In particular, we demonstrate the application of a magnetic force in the order of hundreds of pN on the membrane of HeLa cells cultured on-chip, via manipulation of 1 μm superparamagnetic beads. Such a mechanical stimulus produces a sizable local indentation of the cellular membrane of about 2 μm. Upon evaluation of the beads' position within the magnetic field originated by the domain wall, the force applied during the experiments is accurately quantified via micromagnetic simulations. The obtained value is in good agreement with that calculated by the application of an elastic model to the cellular membrane.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.331
Threshold uncertainty score0.543

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.021
GPT teacher head0.267
Teacher spread0.246 · 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

Citations18
Published2016
Admission routes1
Has abstractyes

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