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Record W4403214474 · doi:10.1021/acs.nanolett.4c03979

Stiffening of the Cytoplasm in Response to Intracellularly Applied Forces

2024· article· en· W4403214474 on OpenAlexafffund
Wentian Tang, Jintian Wang, Aojun Jiang, Yu Sun

Bibliographic record

VenueNano Letters · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCellular Mechanics and Interactions
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsOntario Research Foundation
KeywordsStiffeningCytoplasmNanotechnologyMaterials scienceBiophysicsChemistryBiologyCell biologyComposite material

Abstract

fetched live from OpenAlex

Cells constantly encounter mechanical forces that regulate various cellular functions, such as migration, division, and differentiation. Understanding how cells respond to forces at the intracellular level is essential for elucidating the mechanical adaptability of living cells. This study investigates how the cytoplasm alters its mechanical properties in response to forces applied inside a cell. The mechanical properties were measured through in situ characterization using magnetic tweezers to apply mechanical forces on magnetic beads internalized into cells. The findings reveal that the cytoplasm stiffens within seconds when force is applied to the cytoplasm. Macromolecular crowding and cytoskeletal structures, particularly F-actin, were found to significantly contribute to cytoplasm stiffening. The stiffening response was also observed across multiple length scales by using magnetic beads of varying diameters. These results highlight the rapid adaptation of the cytoplasm to mechanical forces applied to the inside of a cell.

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 score0.091
Threshold uncertainty score0.236

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 head0.005
GPT teacher head0.220
Teacher spread0.215 · 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

Citations1
Published2024
Admission routes2
Has abstractyes

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