Low-Intensity Ultrasound Decreases Ischemia-Induced Edema by Inhibiting<i>N</i>-Methyl-<scp>d</scp>-Aspartic Acid Receptors
Bibliographic record
Abstract
BACKGROUND: We have previously shown that low-intensity ultrasound (LIUS), a noninvasive mechanical stimulus, inhibits brain edema formation induced by oxygen and glucose deprivation (OGD) or treatment with glutamate, a mediator of OGD-induced edema, in acute rat hippocampal slice model in vitro. METHODS: In this study, we treated the rat hippocampal slices with N-methyl-d-aspartic acid (NMDA) or (S)-3,5-dihydroxyphenylglycine (DHPG) to determine whether these different glutamate receptor agonists induce edema. The hippocampal slices were then either sonicated with LIUS or treated with N-methyl-d-aspartic acid receptor (NMDAR) antagonists, namely, MK-801 and ketamine, and observed their effects on edema formation. RESULTS: We observed that treatment with NMDA, an agonist of ionotropic glutamate receptors, induced brain edema at similar degrees compared with that induced by OGD. However, treatment with DHPG, an agonist of metabotropic glutamate receptors, did not significantly induce brain edema. Treatment with the NMDAR antagonists MK-801 or ketamine efficiently prevented brain edema formation by both OGD and NMDA in a concentration-dependent manner. N-Methyl-d-aspartic acid-induced brain edema was alleviated by LIUS in an intensity-dependent manner when ultrasound was administered at 30, 50, or 100 mW/cm2 for 20 minutes before the induction of the edema. Furthermore, LIUS reduced OGD- and NMDA-induced phosphorylation of NMDARs at Y1325. CONCLUSION: These results suggest that LIUS can inhibit OGD- or NMDA-induced NMDAR activation by preventing NMDAR phosphorylation, thereby reducing a subsequent brain edema formation. The mechanisms by which LIUS inhibits NMDAR phosphorylation need further investigation.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".