The Pump-Leak/Donnan ion homeostasis strategies of skeletal muscle fibers and neurons
Bibliographic record
Abstract
ABSTRACT Skeletal muscle fibers (SMFs) and neurons are low and high duty-cycle excitable cells constituting exceptionally large and extraordinarily small fractions of vertebrate bodies. The immense ClC-1-based chloride-permeability (P Cl ) of SMFs has thwarted understanding of their Pump-Leak/Donnan (P-L/D) ion homeostasis. After formally defining P-L/D set-points and feedbacks, we therefore devise a simple yet demonstrably realistic model for SMFs. Hyper-stimulated, it approximates rodent fibers’ ouabain-sensitive ATP-consumption. Size-matched neuron-model/SMF-model comparisons reveal steady-states occupying two ends of an energetics/resilience P-L/D continuum. Excitable neurons’ costly vulnerable process is Pump-Leak dominated. Electrically-reluctant SMFs’ robust low-cost process is Donnan dominated: collaboratively, Donnan effectors and [big P Cl ] stabilize V rest , while SMFs’ exquisitely small P Na minimizes ATP-consumption, thus maximizing resilience. “Classic” excitable cell homeostasis ([small P Cl ][big I Naleak ]), de rigueur for electrically-agile neurons, is untenable for vertebrates’ (including humans’) major tissue. Vertebrate bodies evolved thanks to syncytially-efficient SMFs using a Donnan dominated ([big P Cl ][small I Naleak ]) ion homeostatic strategy.
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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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".