Differential correlation of serum BDNF and microRNA content in rats with rapid or late onset of heavy alcohol use
Bibliographic record
Abstract
Heavy alcohol use reduces the levels of the brain-derived neurotrophic factor (BDNF) in the prefrontal cortex of rodents through the upregulation of microRNAs (miRs) targeting BDNF mRNA. In humans, an inverse correlation exists between circulating blood levels of BDNF and the severity of psychiatric disorders including alcohol abuse. Here, we set out to determine whether a history of heavy alcohol use produces comparable alterations in the blood of rats. We used an intermittent access to 20% alcohol using the two-bottle choice paradigm (IA20%2BC) and measured circulating levels of BDNF protein and miRs targeting BDNF in the serum of Long-Evans rats before and after 8 weeks of excessive alcohol intake. We observed that the drinking profile of heavy alcohol users is not unified, whereas 70% of the rats gradually escalate their alcohol intake (late onset), and 30% of alcohol users exhibit a very rapid onset of drinking (rapid onset). We found that serum BDNF levels are negatively correlated with alcohol intake in both rapid onset and late onset rats. In contrast, increased expression of the miRs targeting BDNF, miR30a-5p, miR-195-5p, miR191-5p and miR206-3p, was detected only in the rapid onset rats. Finally, we report that the alcohol-dependent molecular changes are not due to alterations in platelet number. Together, these data suggest that rats exhibit both late and rapid onset of alcohol intake. We further show that heavy alcohol use produces comparable changes in BDNF protein levels in both groups. However, circulating microRNAs are responsive to alcohol only in the rapid onset rats.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".