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Record W2022460659 · doi:10.1182/blood-2007-05-088468

Extracellular calcium sensing promotes human B-cell activation and function

2007· article· en· W2022460659 on OpenAlexaff
Caitlin Hammond, D White, Jelena Tomić, Yonghong Shi, David Spaner

Bibliographic record

VenueBlood · 2007
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCalcium signaling and nucleotide metabolism
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsCell biologyBiologyExtracellularSignal transductionCalcium-sensing receptorProtein kinase BCalciumChemistryCalcium metabolism

Abstract

fetched live from OpenAlex

Calcium is a second messenger for many signaling pathways in B cells, but its role as a receptor ligand has not been well characterized. However, pulses of free calcium were found to cause the rapid release of internal calcium stores in normal human B cells. This response appeared to be mediated by a cell surface protein with receptor properties as it could be blocked by pretreatment with trypsin and with kinase and phospholipase Cgamma inhibitors. The calcium receptor on B cells was not the conventional calcium-sensing receptor (CaSR) since B cells did not express CaSR and calcium-induced responses could not be blocked by specific CaSR inhibitors. B-cell responses to extracellular calcium activated phosphoinositide-3 kinase/AKT, calcineurin, extracellular signal regulated kinase, p38 mitogen-activated protein kinase, protein kinase C, Ca(2+)/calmodulin kinase II, and nuclear factor-kappaB signaling pathways, and resulted in transcription of the early response gene, CD83. This extracellular calcium sensor enhanced B-cell responses to Toll-like receptor, B-cell receptor, and cytokine receptor agonists. These findings suggest a means by which B cells prepare to engage in immune responses by responding to calcium fluctuations in their environment.

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 score0.019
Threshold uncertainty score0.405

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 head0.013
GPT teacher head0.240
Teacher spread0.227 · 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

Citations28
Published2007
Admission routes1
Has abstractyes

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