Competitive Inhibition of NMDA Receptor–Mediated Currents by Extracellular Calcium Chelators
Bibliographic record
Abstract
Calcium chelators have been widely used in electrophysiological recordings of N-methyl-D-aspartate (NMDA) receptor-mediated currents, as well as in studies of excitotoxicity. Intracellularly applied calcium chelators are known to inhibit, at least in part, such calcium-dependent processes as calmodulin-dependent inactivation, calcineurin-dependent desensitization, and rundown of NMDA receptors. On the other hand, the functional consequences and potential nonspecific effects of extracellularly applied chelators have not been extensively investigated. In whole-cell patch-clamp recordings from human embryonic kidney (HEK) 293 cells transiently transfected with recombinant NMDA receptors, we found that addition of calcium chelators such as EGTA shifted the glutamate dose-response curve to the right, from an EC(50) for NR1A/NR2A of 8 microM in 1.8 mM Ca(2+) to approximately 24 microM in a solution containing nominal 0 Ca(2+)/5 mM EGTA and further to approximately 80 microM in 20 mM EGTA. A similar shift in glutamate dose-response was observed for NR1A/NR2B currents. This dose-response shift was not due to a decrease in extracellular Ca(2+) concentration because there was no change in the glutamate EC(50) at Ca(2+) concentrations ranging from 10 mM to nominal 0/200 microM EGTA. Moreover, addition of 5 mM EGTA fully chelated with 6.8 mM Ca(2+) did not produce any shift in the glutamate dose-response curve. We propose that calcium chelators, containing four free carboxyl moieties, competitively inhibit glutamate binding to NMDA receptors.
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".