Back

Inhibition of Notch Signaling Attenuates Epileptic Discharges in the Adolescent Rat Brain after Status Epilepticus Induction

Yuan, P.; Liu, J.; Chen, J.

2024-11-18 neuroscience
10.1101/2024.11.17.624007 bioRxiv
Show abstract

BackgroundNotch signaling plays a critical role in neuroregeneration after injuries such as those caused by status epilepticus (SE). ObjectiveTo explore the effects of Notch signaling on epileptogenesis and the underlying mechanisms in adolescent rat brains in the acute phase after SE induction. MethodsN-[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. ResultsEEG 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. ConclusionInhibiting 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.

Matching journals

The top 11 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.