Fast, Non-Competitive and Reversible Inhibition of NMDA-Activated Currents by 2-BFI Confers Neuroprotection
Bibliographic record
Abstract
Excessive activation of the N-methyl-D-aspartic acid (NMDA) type glutamate receptors (NMDARs) causes excitotoxicity, a process important in stroke-induced neuronal death. Drugs that inhibit NMDA receptor-mediated [Ca(2+)]i influx are potential leads for development to treat excitotoxicity-induced brain damage. Our previous studies showed that 2-(2-benzofu-ranyl)-2-imidazoline (2-BFI), an immidazoline receptor ligand, dose-dependently protects rodent brains from cerebral ischemia injury. However, the molecular mechanisms remain unclear. In this study, we found that 2-BFI transiently and reversibly inhibits NMDA, but not AMPA currents, in a dose-dependent manner in cultured rat cortical neurons. The mechanism of 2-BFI inhibition of NMDAR is through a noncompetitive fashion with a faster on (Kon = 2.19±0.33×10(-9) M(-1) sec(-1)) and off rate (Koff = 0.67±0.02 sec(-1)) than those of memantine, a gold standard for therapeutic inhibition NMDAR-induced excitotoxicity. 2-BFI also transiently and reversibly blocked NMDA receptor-mediated calcium entry to cultured neurons and provided long-term neuroprotection against NMDA toxicity in vitro. Collectively, these studies demonstrated a potential mechanism of 2-BFI-mediated neuroprotection and indicated that 2-BFI is an excellent candidate for repositioning as a drug for stroke treatment.
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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) | 0.001 | 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 | 0.001 |
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".