Fixed negative charge and the Donnan effect: a description of the driving forces associated with brain tissue swelling and oedema
Bibliographic record
Abstract
Cerebral oedema or brain tissue swelling is a significant complication following traumatic brain injury or stroke that can increase the intracranial pressure (ICP) and impair blood flow. Here, we have identified a potential driver of oedema: the negatively charged molecules fixed within cells. This fixed charge density (FCD), once exposed, could increase ICP through the Donnan effect. We have shown that metabolic processes and membrane integrity are required for concealing this FCD as slices of rat cortex swelled immediately (within 30 min) following dissection if treated with 2 deoxyglucose + cyanide (2DG+CN) or Triton X-100. Slices given ample oxygen and glucose, however, did not swell significantly. We also found that dead brain tissue swells and shrinks in response to changes in ionic strength of the bathing medium, which suggests that the Donnan effect is capable of pressurizing and swelling brain tissue. As predicted, a non-ionic osmolyte, 1,2 propanediol, elicited no volume change at 2000 x 10(-3) osmoles l(-1) (Osm). Swelling data were well described by triphasic mixture theory with the calculated reference state FCD similar to that measured with a 1,9 dimethylmethylene blue assay. Taken together, these data suggest that intracellular fixed charges may contribute to the driving forces responsible for brain swelling.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".