An antisense construct reduces <i>N</i>‐methyl‐<scp>D</scp>‐aspartate receptor 2A expression and receptor‐mediated excitotoxicity as determined by a novel flow cytometric approach
Bibliographic record
Abstract
The N-methyl-D-aspartate receptor (NMDAR) is a major neurotransmitter receptor in the central nervous system (CNS), with functional roles in learning, memory, and sensation. Several mechanisms potentiate NMDARs, and NMDAR hyperexcitability plays pathophysiological roles in many conditions, such as neurodegenerative disease, ischemia, and chronic conditions arising from spinal cord injury. Previous research suggests that the NR2A subunit of the receptor contributes to NMDAR excitotoxicity in heterologous cells and in neurons in vivo. To investigate the role of NR2A in NMDAR excitotoxicity, we have developed a system based on flow cytometry that allows rapid evaluation of the effect of antisense constructs on protein expression and channel function. The enhanced yellow fluorescent protein (EYFP) was fused to obligatory NMDAR subunits, allowing expression to be monitored in living cultured cells. An NR2A antisense construct, asNR2A, specifically and effectively reduced NR2A-EYFP expression. NR1 and NR2A fusion proteins formed functional, excitotoxic channels upon co-expression. The asNR2A RNA significantly reduced NMDAR excitotoxicity when NR2A levels were limiting for channel formation. Using our assay system, further optimization can be achieved rapidly. The asNR2A construct and the assays developed for this study can be used to provide insights into NMDAR biology and disease.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".