Effects of the intrahippocampal injection of anti-Lgi1 antibody on cognitive function and seizure susceptibility in mice
Bibliographic record
Abstract
Anti-LGI1 (Leucine-rich, glioma-inactivated 1) antibody is an autoantibody detected from some patients with autoimmune epilepsy or limbic encephalitis with clinical features of focal seizures and cognitive impairment. In addition, previous study reported that Lgi1-knock out mice exhibited increased susceptibility to seizure induction. In this study, we evaluated the effects of intrahippocampal anti-Lgi1 antibody on cognitive function and seizure susceptibility in mice. C57BL/6J mice were intrahippocampally microinjected with anti-Lgi1 antibody (Abcam). A week later, the novel object recognition test and the seizure susceptibility test against pentylenetetrazol (PTZ, 35 mg/kg, i.p.) were performed. Areas of brain excitation were also evaluated by immunohistochemical analysis of c-Fos expression. While no cognitive impairment was observed, the intrahippocampal injection of anti-Lgi1 antibody showed a tendency to increase the seizure susceptibility to PTZ, as compared to control mice. In addition, analysis of c-Fos expression using mice without seizure showed significant increase in c-Fos expression in cerebral cortex and hippocampal CA3 area of the antibody-injected mice compared to control mice. Anti-Lgi1 antibody may promote the seizure occurrence by enhancing neural activity in cerebral cortex and hippocampus.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".