Effect of Co-administration of Bumetanide and Phenobarbital on Seizure Attacks in Temporal Lobe Epilepsy
Bibliographic record
Abstract
INTRODUCTION: The resistance of temporal lobe epilepsy to classic drugs is thought to be due to disruption in the excitation/inhibition of this pathway. Two chloride transporters, NKCC1 and KCC2, are expressed differently for the excitatory state of Gamma-Amino Butyric Acid (GABA). The present study explored the effect of bumetanide as a selective NKCC1 inhibitor either alone or in combination with the phenobarbital in the pilocarpine model of epilepsy. METHODS: An animal model of Status Epilepticus (SE) was induced with pilocarpine in Wistar male rats followed by phenobarbital and or bumetanide or saline administration for 45 days after the induction of SE by Intraperitoneal (IP) injection. The rats were monitored, their behavior was recorded, and after 24 hours they were sacrificed to study the expression of NKCC1 and KCC2 using real time PCR. RESULTS: The data showed that the effects of a combination of bumetanide with phenobarbital on frequency rate and duration of seizure attack were more than those of the phenobarbital alone. In addition, in the bumetanide and combined treatment groups, NKCC1 expression decreased significantly, compared with untreated epileptic animals. A delayed decrement in NKCC1/KCC2 expression ratio after bumetanide application was also observed. CONCLUSION: The combination of bumetanide with phenobarbital increases the inhibition of SE and maximizes the potential of GABA signaling pathway, and can be considered as an effective therapeutic strategy in patients with epilepsy.
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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 | 0.000 |
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".