Investigating the effects of chronic perinatal alcohol and combined nicotine and alcohol exposure on dopaminergic and non-dopaminergic neurons in the VTA
Bibliographic record
Abstract
The ventral tegmental area (VTA) is the origin of dopaminergic neurons and the dopamine (DA) reward pathway. This pathway has been widely studied in addiction and drug reinforcement studies and is believed to be the central processing component of the reward circuit. In this study, we used a well-established rat model to expose mother dams to alcohol, nicotine-alcohol, and saline perinatally. DA and non-DA neurons collected from the VTA of the rat pups were used to study expression profiles of miRNAs and mRNAs. miRNA pathway interactions, putative miRNA-mRNA target pairs, and downstream modulated biological pathways were analyzed. In the DA neurons, 4607 genes were differentially upregulated and 4682 were differentially downregulated following nicotine-alcohol exposure. However, in the non-DA neurons, only 543 genes were differentially upregulated and 506 were differentially downregulated. Cell proliferation, differentiation, and survival pathways were enriched after the treatments. Specifically, in the PI3K/AKT signaling pathway, there were 41 miRNAs and 136 mRNAs differentially expressed in the DA neurons while only 16 miRNAs and 20 mRNAs were differentially expressed in the non-DA neurons after the nicotine-alcohol exposure. These results depicted that chronic nicotine and alcohol exposures during pregnancy differentially affect both miRNA and gene expression profiles more in DA than the non-DA neurons in the VTA. Understanding how the expression signatures representing specific neuronal subpopulations become enriched in the VTA after addictive substance administration helps us to identify how neuronal functions may be altered in the brain.
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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 | 0.000 | 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 | 0.001 |
| 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".