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Record W3204535149 · doi:10.3390/ijms221910366

Signaling Enzymes and Ion Channels Being Modulated by the Actin Cytoskeleton at the Plasma Membrane

2021· review· en· W3204535149 on OpenAlexaff
Filip Vasilev, Yulia Ezhova, Jong Tai Chun

Bibliographic record

VenueInternational Journal of Molecular Sciences · 2021
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCellular Mechanics and Interactions
Canadian institutionsUniversité de MontréalHôpital Maisonneuve-RosemontCentre Hospitalier de l’Université de Montréal
Fundersnot available
KeywordsCell biologyCytoskeletonActin cytoskeletonActinIon channelActin remodelingSignal transductionBiologyCellInositolReceptorBiochemistry

Abstract

fetched live from OpenAlex

A cell should deal with the changing external environment or the neighboring cells. Inevitably, the cell surface receives and transduces a number of signals to produce apt responses. Typically, cell surface receptors are activated, and during this process, the subplasmalemmal actin cytoskeleton is often rearranged. An intriguing point is that some signaling enzymes and ion channels are physically associated with the actin cytoskeleton, raising the possibility that the subtle changes of the local actin cytoskeleton can, in turn, modulate the activities of these proteins. In this study, we reviewed the early and new experimental evidence supporting the notion of actin-regulated enzyme and ion channel activities in various cell types including the cells of immune response, neurons, oocytes, hepatocytes, and epithelial cells, with a special emphasis on the Ca2+ signaling pathway that depends on the synthesis of inositol 1,4,5-trisphosphate. Some of the features that are commonly found in diverse cells from a wide spectrum of the animal species suggest that fine-tuning of the activities of the enzymes and ion channels by the actin cytoskeleton may be an important strategy to inhibit or enhance the function of these signaling proteins.

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)0.001
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.774
Threshold uncertainty score0.475

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.021
GPT teacher head0.310
Teacher spread0.289 · 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
GenreReview

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

Citations29
Published2021
Admission routes1
Has abstractyes

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