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Record W4309621159 · doi:10.1101/2022.11.16.516758

Dynamic traction force measurements of migrating immune cells in 3D matrices

2022· preprint· en· W4309621159 on OpenAlexaff
David Böhringer, Mar Cóndor, Lars Bischof, Tina Czerwinski, Niklas Gampl, Phuong A. Ngo, Andreas Bauer, Caroline Voskens, Rocío López-Posadas, Kristian Franze, Silvia Budday, Christoph Mark, Ben Fabry, Richard Gerum

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2022
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCellular Mechanics and Interactions
Canadian institutionsYork University
FundersNational Institutes of Health
KeywordsTraction (geology)Tractive forceStiffnessOrganoidMatrix (chemical analysis)Immune systemBiophysicsMaterials sciencePhysicsCell biologyBiologyImmunologyComposite material

Abstract

fetched live from OpenAlex

Immune cells such as natural killer (NK) cells migrate with high speeds of several µm/min through dense tissue, but the traction forces are unknown. We present a method to measure dynamic traction forces of fast migrating cells in non-linear biopolymer matrices. The method accounts for the mechanical non-linearity of the 3D tissue matrix and can be applied to time series of confocal or bright-field image stacks. The method is highly sensitive over a large range of forces and object sizes, from ∼1 nN for axon growth cones up to ∼10 µN for mouse intestinal organoids. We find that NK cells display bursts of large traction forces that increase with matrix stiffness and facilitate migration through tight constrictions.

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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.231
Teacher spread0.219 · 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

Citations8
Published2022
Admission routes1
Has abstractyes

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