Chronic nicotine exposure induces molecular and transcriptomic endophenotypes associated with mood and anxiety disorders in a cerebral organoid neurodevelopmental model
Bibliographic record
Abstract
Introduction: Prenatal nicotine exposure (PNE) from maternal smoking disrupts regulatory processes vital to fetal development. These changes result in long-term behavioral impairments, including mood and anxiety disorders, that manifest later in life. However, the relationship underlying PNE, and the underpinnings of mood and anxiety molecular and transcriptomic phenotypes remains elusive. Methods: To model nicotine exposure during prenatal development, our study used human cerebral organoids that were chronically exposed to nicotine and collected for molecular analyses. Results: Short-term, nicotine altered molecular markers of neural identity, mood and anxiety disorders and those involved in maintaining the excitatory/inhibitory (E/I) balance in the cortex. RNA sequencing further revealed transcriptomic changes in genes pertaining to embryonic development, neurogenesis, and DNA binding. Long-term, mature organoids demonstrated similar disruptions in E/I balance, decreased expression of neural identity markers, and altered dopamine receptor expression. Discussion: model of PNE to better comprehend the emergence of neuropsychiatric molecular and transcriptomic endophenotypes resulting from gestational nicotine exposure.
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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".