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Record W2078945362 · doi:10.2174/187152208787169260

Nanoscale Membrane Organization and Receptor Signaling in T- Lymphocytes

2008· article· en· W2078945362 on OpenAlexfundno aff
Yannick Hamon, Anne‐Marie Bernard, Audrey Salles, Omar Hawchar, Didier Marguet, Hai‐Tao He, Xiaojun Guo

Bibliographic record

VenueImmunology Endocrine & Metabolic Agents - Medicinal Chemistry · 2008
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicLipid Membrane Structure and Behavior
Canadian institutionsnot available
FundersAgence Nationale de la RechercheInstitut national de la recherche scientifiqueAssociation pour la Recherche sur le Cancer
KeywordsLipid raftSignal transductionCell biologyT-cell receptorCell surface receptorReceptorLipid microdomainCell membraneImmune systemMembraneTransduction (biophysics)Fluorescence correlation spectroscopyChemistryBiologyBiophysicsT cellImmunologyBiochemistry

Abstract

fetched live from OpenAlex

The presence of microdomains (i.e., lipid rafts) in the plasma membrane and their proposed role(s) in the signal transduction by cell surface receptors, particularly in immune cells, has elicited broad interest yet much debate. Here, we aim to review some observations made by our and other groups over past years on the structural and functional properties of membrane microdomains, focusing mainly on T cells and more particularly on activation of signaling cascades triggered by T-cell receptor (TCR) and Fas/CD95, respectively. We will focus on the progress made at both the experimental and conceptual levels. We will then discuss our recent studies on the identification and characterization of nano-sized membrane domains in the plasma membrane of living cells using an original fluorescent correlation spectroscopy (FCS) approach. Keywords: Lipid rafts, membrane microdomains, T-cell receptor, Fas, CD95, signal transduction, live cells, fluorescent correlation spectroscopy

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 categoriesMeta-epidemiology (narrow)
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 score0.023
Threshold uncertainty score1.000

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 head0.009
GPT teacher head0.236
Teacher spread0.228 · 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.

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

Citations0
Published2008
Admission routes1
Has abstractyes

Explore more

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