Data on electroconvulsive seizure in mice, effects of anesthesia on immediate early gene expression
Bibliographic record
Abstract
Although electroconvulsive therapy (ECT) is one of the most effective treatments for severe mood and psychotic disorders, the mechanisms underlying its therapeutic effects remain unknown. Electroconvulsive stimulation (ECS), the animal model for ECT, can be used to investigate the potential therapeutic mechanisms of ECT in rodents. ECS produces numerous effects in the brain, such as increasing levels of growth factors, inducing dendritic sprouting, and stimulating neurogenesis. It also induces high-level expression of immediate early genes (IEGs) that have been implicated in the pathogenesis of schizophrenia, such as early growth response 3 (Egr3) and activity-regulated cytoskeleton-associated protein (Arc), a validated downstream target of Egr3 [1–3]. However, the effect of isoflurane anesthesia preceding ECS on IEG response in mice has not been well characterized. This article provides immunofluorescent data of the activity responsive IEG ARC in the dorsal and ventral dentate gyrus of wildtype (WT) mice following ECS with or without anesthesia, as well as following sham ECS. The data in this article relate to a published article that employed serial ECS in mice to investigate the requirement of Egr3 in the neurobiological effects of this model of ECT [4]. The ability to study the effects of serial ECS has been limited in mice due to high rates of mortality during seizure. Administration of isoflurane anesthesia prior to ECS significantly reduces rodent mortality, irrespective of the number of times ECS is applied [5]. Since general anesthesia is administered to patients prior to ECT, use of isoflurane prior to ECS also more closely models the clinical use of ECT [6].
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.008 |
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".