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Record W2417253274 · doi:10.1083/jcb.201511053

T cell activation requires force generation

2016· article· en· W2417253274 on OpenAlexfundno aff
Kenneth H. Hu, Manish J. Butte

Bibliographic record

VenueThe Journal of Cell Biology · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicForce Microscopy Techniques and Applications
Canadian institutionsnot available
FundersDivision of Chemical, Bioengineering, Environmental, and Transport SystemsNational Institute of General Medical SciencesChildren's Health Research InstituteNational Institutes of HealthNational Science Foundation
KeywordsT-cell receptorStimulationCytoskeletonAtomic force microscopyCantileverActinBiophysicsT cellAntigenMicroscopyChemistryActin cytoskeletonImmunological synapseCell biologyReceptorCellMaterials scienceNanotechnologyBiologyPhysicsImmunologyBiochemistryImmune systemNeuroscienceOptics

Abstract

fetched live from OpenAlex

Triggering of the T cell receptor (TCR) integrates both binding kinetics and mechanical forces. To understand the contribution of the T cell cytoskeleton to these forces, we triggered T cells using a novel application of atomic force microscopy (AFM). We presented antigenic stimulation using the AFM cantilever while simultaneously imaging with optical microscopy and measuring forces on the cantilever. T cells respond forcefully to antigen after calcium flux. All forces and calcium responses were abrogated upon treatment with an F-actin inhibitor. When we emulated the forces of the T cell using the AFM cantilever, even these actin-inhibited T cells became activated. Purely mechanical stimulation was not sufficient; the exogenous forces had to couple through the TCR. These studies suggest a mechanical-chemical feedback loop in which TCR-triggered T cells generate forceful contacts with antigen-presenting cells to improve access to antigen.

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 score0.001
Threshold uncertainty score0.005

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 integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.270
Teacher spread0.257 · 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

Citations200
Published2016
Admission routes1
Has abstractyes

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Same venueThe Journal of Cell BiologySame topicForce Microscopy Techniques and ApplicationsFrench-language works237,207