Neuroimmune Characteristics of Animals with Prenatal Alcohol Intoxication
Bibliographic record
Abstract
Neuroinflammation can be an important factor of many disorders in central nervous system (CNS) including cognitive dysfunction, affective disorders, and addictive behavior associated with prenatal alcohol exposure and presented in early adulthood. In this study we used an experimental rodent model of prenatal alcohol (PA) exposure (consumption of a 10% ethanol solution by female Wistar rats throughout pregnancy), multiplex immunofluorescence analysis of interleukins (IL-1α, IL-1β, IL-3, IL-6, IL-9, and IL-12), tumor necrosis factor (TNF-α), and chemokine CCL5, as well as quantitative real-time PCR to assess the level of cytokine mRNAs in the prefrontal cortex of the sexually mature (PND60) offspring - male and female rats with prenatal alcohol intoxication and control animals. Significant decrease in the content of TNF-α and interleukins IL-1β, IL-3, IL-6, IL-9 was detected in the prefrontal cortex of male, but not in the female PA offspring. Importantly, PA males also showed decrease in the level of TNF-α mRNA in the prefrontal cortex by 45% compared to the control males, which may underlie the detected decrease in its content. Taken together, our study demonstrates that a number of neuroimmune factors are regulated in a sex-specific manner in the prefrontal cortex and are differentially affected in males and females by the prenatal exposure to alcohol. Sex factor must be taken into account when conducting further translational studies of the fetal alcohol spectrum disorders and developing new methods for prevention and therapy.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".