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Cell cycle-related changes in regulatory volume decrease and volume-sensitive chloride conductance in mouse fibroblasts

2001· article· en· W2614343612 on OpenAlexaff
P. A. Doroshenko, V. Kh. Sabanov, N. A. Doroshenko

Bibliographic record

VenueJournal of Cellular Physiology · 2001
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicIon channel regulation and function
Canadian institutionsOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsVolume (thermodynamics)ConductanceChlorideCell biologyCellChemistryCell cycleBiophysicsBiologyBiochemistryThermodynamicsMathematicsPhysics

Abstract

fetched live from OpenAlex

Cell cycle-related changes in the ability to regulate cell volume following hyposmotic swelling were studied in mouse fibroblasts using videomicroscopy and the whole-cell patch clamp technique. Regulatory volume decrease (RVD) and volume-sensitive Cl- conductance (G(Cl,vol)) were measured: (1) in proliferating cells of different sizes; (2) in cells arrested in defined phases of the cell cycle (G1, G1/S, S, and M phases) using mevastatin, mimosine, hydroxyurea, aphidicolin, cytosine beta-D-arabinofuranoside, and taxol; and (3) in serum-starved cells (G(0) state). Cells in all groups were able to undergo RVD, although the cells approaching mitosis (i.e., the largest cells in proliferating cultures and the taxol-treated cells) had the lowest rates of shrinkage during RVD. In agreement with this finding, the density of G(Cl,vol) was stable in proliferating and cell cycle-arrested cells for most of the cell cycle, with the exception of the cells approaching mitosis and the new daughter cells where the density was decreased to half. The impairment of RVD was greatest in serum-starved cells which also had the lowest density of G(Cl,vol). We conclude that proliferating cells maintain an ability to recover from osmotic swelling as they progress through the cell cycle, although this ability may be compromised during mitosis.

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 score0.055
Threshold uncertainty score0.578

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 head0.006
GPT teacher head0.203
Teacher spread0.197 · 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

Citations25
Published2001
Admission routes1
Has abstractyes

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