3,3′-Diindolylmethane attenuates recognition memory impairment induced by binge ethanol exposure in mice
Bibliographic record
Abstract
Binge drinking, defined as excessive alcohol consumption over a short period, is most common at ages that are particularly vulnerable to the harmful effects of alcohol. Cognitive functions are severely affected, and there is a concern that the consequences of alcohol consumption in early life stages can perpetuate into adulthood. 3,3'-Diindolylmethane (DIM) has recently been found to have neuroprotective activity. We aimed to investigate whether DIM could mitigate binge ethanol-induced memory deficits, and to explore the underlying mechanisms. Mice underwent four binge episodes spaced five days apart. Each episode consisted of three intraperitoneal ethanol injections (2.5, 2.5 and 2 g/kg) given at two-hour intervals. DIM (50 mg/kg/day, i.p) was administered during the last four days of the protocol, with the final dose being given 30 min prior to the first ethanol injection of the last binge episode. DIM attenuates the object recognition memory deficit induced by ethanol 20 h after the last exposure, an effect that can be sustained until 7 days if dosing is continued until testing. DIM increases the expression of the glutamate transporter EAAT2 and reduces the ethanol-induced disruption of the GluN2A/GluN2B receptor ratio but does not alter the ethanol-induced increase in AMPA receptor membrane trafficking. DIM reduces the ethanol induced-increase in MMP-9 activity and facilitates the survival of immature neurons without modifying the ethanol-induced decrease in mature neurons nor the increase in microglia. DIM may be a promising therapeutic candidate for ethanol-induced memory deficits through modulation of glutamatergic neurotransmission, hippocampal neurogenesis and MMP-9 activity.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".