The Influence of Cross-Fostering on Alcohol Consumption and Depressive-Like Behaviors in HA and LA Mice: The Role of the Endogenous Opioid System
Bibliographic record
Abstract
The development of alcohol dependence and depression is determined by various genetic and environmental factors. In the presented study, we used high analgesia (HA) and low analgesia (LA) mouse lines, characterized by different endogenous opioid system activity and divergent blood-brain barrier permeability, to determine the influence of cross-fostering of these lines raised by surrogate mothers on ethanol consumption and development of depressive-like behaviors. We also investigated ethanol drinking by biological parents or surrogate mothers. Furthermore, we investigated whether these parental changes would alter the effect of naloxone on ethanol intake and depressive-like behaviors in offspring. Our results reveal that cross-fostering of HA and LA raised by surrogate mothers has a greater impact on depressive-like behaviors than ethanol consumption. Ethanol intake by biological parents substantially affected depressive-like behaviors and ethanol consumption in offspring. Moreover, ethanol intake by biological parents or an adoptive mother modified the effect of naloxone on ethanol consumption and preference and depressive-like behaviors in the HA offspring only. Together, these results indicate that cross-fostering differentially affects the effect of naloxone on alcohol consumption and the development of depression.
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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) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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".