PRENATAL EXPOSURE TO SEROTONIN REUPTAKE INHIBITORS, BUT NOT MATERNAL MOOD, DECREASES NEONATAL S100B LEVELS
Bibliographic record
Abstract
Objectives: The development of the serotonergic system is dependent on serotonin-related astroglial-specific calcium binding protein, S100B. S100B mediates the growth of serotonin neurons and may reflect the developmental integrity of the brain, the effects of early adverse experience and developmental risk. This study was undertaken to determine whether prenatal exposure to serotonin reuptake inhibitor (SRI) antidepressants alters neonatal cord serum S100B levels. Methods: Maternal depression and anxiety symptoms were assessed during the third trimester (33–36 weeks) using clinician-rated (Hamilton rating scale for anxiety; Hamilton rating scale for depression) and patient-rated measures (Edinburgh postnatal depression scale). Neonatal outcomes including Apgar scores, birth weight, gender and gestational age at birth were determined in 36 prenatally SRI-exposed neonates (230 ± 71 days) and compared with 14 non-exposed neonates. Serum S100B levels were assayed from maternal blood obtained at delivery and cord blood using a S100B ELISA (Biovendor). Results: S100B levels were significantly lower in prenatally SRI-exposed neonates compared with non-exposed neonates when controlling for lower 5-minute Apgar scores and third trimester maternal mood (p Conclusions: Prolonged prenatal SRI exposure is associated with lower cord S100B protein levels, even when controlling for depressed/anxious maternal mood. While these findings are consistent with other prenatal exposures that alter central serotonin (alcohol and cocaine), it remains to be determined whether this biomarker reflects altered early serotonergic system function and long-term developmental risk.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".