Potassium Clearance in Optic Nerve: A Multidomain Model
Bibliographic record
Abstract
BACKGROUND: Ion and water transport in the central nervous system (CNS) is governed by tightly coupled mechanisms involving electrodiffusion, osmotic pressure, and fluid convection. Disruptions to these processes are implicated in pathological conditions. Understanding the coordinated roles of glial cells and perivascular spaces in regulating ionic and fluid homeostasis is essential for interpreting neural function and dysfunction. METHODS: We developed a multicompartment model of the optic nerve incorporating axons, glial cells, extracellular space (ECS), and three perivascular compartments (arterial, venous, and capillary-associated). The model integrates electrodiffusion of ions, osmotic water transport, and convection, while enforcing electroneutrality and compartmental volume conservation. Numerical simulations were performed using a finite volume method under axisymmetric geometry, and parameter sensitivity was explored through variations in glial membrane conductance, connexin permeability, and aquaporin-4 (AQP4) expression. RESULTS: The simulations reveal that potassium released from axons during stimulation is cleared via glial uptake and redistributed through electric drift within glial syncytia. The perivascular pathway provides a secondary route for potassium and water clearance. Decreased glial conductance leads to abnormal firing in unstimulated axons, mimicking epileptiform activity, while reduced connexin coupling increases dependence on perivascular drainage. Changes in AQP4 expression had limited effect on ionic homeostasis in the current model. CONCLUSIONS: accumulation and suggest potential therapeutic targets involving glial modulation and perivascular enhancement. The framework is extensible to other brain regions and conditions involving impaired clearance or excitability.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| 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".