Inhibition of Notch Signaling Attenuates Epileptic Discharges in the Adolescent Rat Brain after Status Epilepticus Induction
Bibliographic record
Abstract
Abstract Background Notch signaling plays a critical role in neuroregeneration after injuries such as those caused by status epilepticus (SE). Objective To explore the effects of Notch signaling on epileptogenesis and the underlying mechanisms in adolescent rat brains in the acute phase after SE induction. Methods N-[N-(3,5-difluorophenacetyl)- L-alanyl)]-S-phenylglycine t-butyl ester (DAPT), which indirectly inhibits Notch, was injected into rats during the acute phase after SE induction to inhibit Notch signaling. Electroencephalogram (EEG) was used to observe spontaneous recurrent seizures. Differences in the synaptic structures of the hippocampus were observed by transmission electron microscopy. Nissl staining and Timm staining were used to observe the loss of hippocampal neurons and sprouting of mossy fibers, respectively, in the hippocampus at 28 days after SE. Results EEG illustrated that DAPT treatment reduced the severity of epileptic discharges after SE induction. Transmission electron microscopy revealed reductions in the presynaptic membrane active band length and postsynaptic membrane dense matter thickness in the CA1 region of the hippocampus. Meanwhile, Nissl staining demonstrated that DAPT treatment reduced the loss of hippocampal neuronal cell degeneration, and the hippocampal structure was repaired to a certain extent. Meanwhile, Timm staining illustrated that DAPT treatment did not affect mossy fiber sprouting (MFS) after SE induction. Conclusion Inhibiting Notch signaling reduced EEG epileptic activity, attenuated synaptic damage, and partially restored the hippocampal neuronal structure. However, it did not alter MFS after SE induction.
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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) | 0.001 | 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".