Molecular effects of digital psychological intervention for perinatal stress: cell culture, animal model validation, and machine learning-based biomarker identification.
📄 Abstract
Perinatal psychological stress significantly impacts maternal and fetal health through complex molecular pathways, yet the biological basis of digital health interventions for pregnant and postpartum women remains poorly understood. This study investigated molecular effects underlying digital psychological intervention effectiveness through cell culture experiments, animal models, and computational biomarker analysis relevant to obstetric populations. Cell culture studies using stress-responsive cellular models revealed that glucocorticoid exposure induced NR3C1 upregulation (2.3-fold, p = 0.003), FKBP5 elevation (3.1-fold, p < 0.001), and IL6 increase (2.7-fold, p = 0.002), while BDNF decreased by 39% (p = 0.012) and SLC6A4 decreased by 48% (p = 0.009). Intervention-simulating treatment partially restored BDNF expression to 0.85-fold of control levels (p = 0.023) and reduced IL6 to 1.4-fold above control (p = 0.007). Animal model validation confirmed that hippocampal BDNF showed 45% reduction under chronic stress (p < 0.001) with recovery to 82% following intervention (p = 0.009), while serum corticosterone decreased from 243.7 ± 42.1 ng/mL to 132.6 ± 28.4 ng/mL after intervention (p < 0.001). Machine learning ensemble methods achieved the highest predictive accuracy for intervention responsiveness with AUC of 0.91 (95% CI: 0.88-0.94). Regional biomarker screening across 2,847 individuals identified 23 biomarkers with significant predictive contributions (Bonferroni-corrected p < 0.01). These findings provide molecular frameworks for understanding digital psychological intervention effectiveness in perinatal care and support evidence-based personalized intervention strategies for pregnant and postpartum women.