Persistent Enhancement of Neuron–Glia Signaling Mediated by Increased Extracellular K<sup>+</sup> Accompanying Long-Term Synaptic Potentiation
Bibliographic record
Abstract
Neuron-glia signaling is important for neural development and functions. This signaling may be regulated by neuronal activity and undergo modification similar to long-term potentiation (LTP) of neuronal synapses, a hallmark of neuronal plasticity. We found that tetanic stimulation of Schaffer collaterals (Sc) in the hippocampus that induced LTP in neurons also resulted in LTP-like persistent elevation of Sc-evoked slow depolarization in perisynaptic astrocytes. The elevated slow depolarization in astrocytes was abolished by NMDA receptor antagonist and K(+) channel inhibitors, but not by Ca(2+) chelator BAPTA loaded in the recorded astrocytes, suggesting involvement of an increased extracellular K(+) accumulation accompanying LTP of neuronal synapses. The increased K(+) accumulation and astrocyte depolarization after LTP induction may reduce the efficiency of glial glutamate transporters, which may contribute to the enhanced synaptic efficacy. The neuronal activity-induced persistent enhancement of neuron-glia signaling may thus have important physiological relevance.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".