Signalling Effect of Elevated Potassium Concentrations and Monoamines on Brain Energy Metabolism at the Cellular Level (Part 2 of 2)
Bibliographic record
Abstract
The effects of elevated K+ concentrations and monoamine transmitters on different cell types in the CNS and on different subcellular structures in these cells are reviewed. Pronounced differences exist in the metabolic processes that are stimulated by excess K+ and by adrenergic agonists, e.g., noradrenaline. An elevation in the extracellular K+ concentration appears to enhance neuronal-astrocytic interaction by stimulating metabolic processes involved in (1) the promotion of supply of precursors for transmitter glutamate, and (2) reestablishment of resting ion distribution following neuronal excitation. The monoamine transmitters stimulate energy production and Na+, K+-ATPase activity in astrocytes in a complex manner and, in so doing, facilitate their role in ion regulation. However, in contrast to excess K+, they do not enhance the production of astrocytic precursors for neuronal glutamate production. Emphasis is placed on possible profound differences in metabolic effects on excitatory and inhibitory neurotransmission and on the importance of stimulation of glycolytic metabolism in astrocytes versus oxidative metabolism in neurons.
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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".