Cell volume and monovalent ion transporters: their role in cell death machinery triggering and progression
Bibliographic record
Abstract
Cell death is accompanied by the dissipation of electrochemical gradients of monovalent ions across the plasma membrane that, in turn, affects cell volume via modulation of intracellular osmolyte content. In numerous cell types, apoptotic and necrotic stimuli caused cell shrinkage and swelling, respectively. Thermodynamics predicts a cell type-specific rather than an ubiquitous impact of monovalent ion transporters on volume perturbations in dying cells, suggesting their diverse roles in the cell death machinery. Indeed, recent data showed that apoptotic collapse may occur in the absence of cell volume changes and even follow cell swelling rather than shrinkage. Moreover, side-by-side with cell volume adjustment, monovalent ion transporters contribute to cell death machinery engagement independently of volume regulation via cell type-specific signaling pathways. Thus, inhibition of Na(+)-K(+)-ATPase by cardiotonic steroids (CTS) rescues rat vascular smooth muscle cells from apoptosis via a novel Na(+)i-K(+)i-mediated, Ca(2+)i-independent mechanism of excitation-transcription coupling. In contrast, CTS kill renal epithelial cells independently of Na(+)-K(+)-ATPase inhibition and increased [Na(+)]i/[K(+)]i ratio. The molecular origin of [Na(+)]i/[K(+)]i sensors involved in the inhibition of apoptosis as well as upstream intermediates of Na(+)i/K(+)i-independent death signaling triggered by CTS remain unknown.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".