Platelet-derived Growth Factor Selectively Inhibits NR2B-containing N-Methyl-D-aspartate Receptors in CA1 Hippocampal Neurons
Bibliographic record
Abstract
Platelet-derived growth factor (PDGF) beta receptor activation inhibits N-methyl-d-aspartate (NMDA)-evoked currents in hippocampal and cortical neurons via the activation of phospholipase Cgamma, PKC, the release of intracellular calcium, and a rearrangement of the actin cytoskeleton. In the hippocampus, the majority of NMDA receptors are heteromeric; most are composed of 2 NR1 subunits and 2 NR2A or 2 NR2B subunits. Using NR2B- and NR2A-specific antagonists, we demonstrate that PDGF-BB treatment preferentially inhibits NR2B-containing NMDA receptor currents in CA1 hippocampal neurons and enhances long-term depression in an NR2B subunit-dependent manner. Furthermore, treatment of hippocampal slices or cultures with PDGF-BB decreases the surface localization of NR2B but not of NR2A subunits. PDGFbeta receptors colocalize to a higher degree with NR2B subunits than with NR2A subunits. After neuronal injury, PDGFbeta receptors and PDGF-BB are up-regulated and PDGFbeta receptor activation is neuroprotective against glutamate-induced neuronal damage in cultured neurons. We demonstrate that the neuroprotective effects of PDGF-BB are occluded by the NR2B antagonist, Ro25-6981, and that PDGF-BB promotes NMDA signaling to CREB and ERK1/2. We conclude that PDGFbetaR signaling, by preferentially targeting NR2B receptors, provides an important mechanism for neuroprotection by growth factors in the central nervous system.
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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 | 0.000 |
| 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 | 0.000 |
| 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".