Back to Authors

Chen, Jun

Mayo Clinic

WinnMed

Citations 2

Microbiome and Vocalization Biomarkers of Infant Distress, Maternal Depression and Parenting Styles
Ligezka, Anna, Lynch, Brian , Saliba, Maria , Corral-Frias, Martha, Johnson, S., Chen, Jun, Allen, Summer V. , Frank, Jennifer M., Voort, Jennifer L. Vande, Croarkin, Paul E. , Romanowicz, Magdalena , Lynch, Brian A , Vande Voort, Jennifer L , Croarkin, Paul E , Ligezka, Anna N. , Chen, Jun, Voort, Jennifer L. Vande

Chronic Stress | March 2, 2026 | Volume: 10 | Issue: No issue available | Pages: No page available

10.1177/24705470261425120 Get It

Introduction Psychiatric disorders have their genesis in early life. Standard screening approaches during well child visits for pathological infant distress, maternal depression, and dysfunctional parenting behaviors are likely inadequate. Microbiome measures and infant vocalizations have promise as scalable psychiatric biomarkers for infants. The purpose of this study was to examine associations among infant gut colonization based on microbiome measurements with maternal distress and maternal depressive symptoms in a sample of infants. Methods This study sought to examine infant microbiome correlates of infant distress, parent-infant interactions, maternal distress, and maternal depressive symptoms. We collected ( N = 31) microbiome samples, infant vocalizations during vaccination, and behavioral measures during a 4 month well child visit (WCV) and did a battery of clinical assessments to assess for maternal depression, parent-child interactions, family characteristics and family stress. Whole-genome SHOTGUN sequencing was utilized to identify three types of associations: alpha-diversity using Shannon and Inverse-Simpson indexes, beta-diversity using Bray-Curtis and Jaccard distances, and differential abundance using LinDA. Spectral measures of infant cries were also modeled to assess potential relationships with clinical assessments and the microbiome. Results There were 19 phyla, 417 genera, and 1246 species identified with taxonomic classification. Maternal distress as measured by PHQ-9 scores obtained when infants were 2 months old were associated with 4 bacterial species ( Actinomyces johnsonii, Bilophila wadsworthia, Clostridium dakarense and Ruminococcus flavefaciens ; FDR < 0.1) and beta-diversity ( p = 0.006-BC; p = 0.005-Jaccard). Infant cries with greater high frequency band power ( p < 0.03) and a greater high-to-mid frequency ratio–metrics ( p < 0.05) were associated with altered α-diversity of the microbiome. No correlations were present between maternal PHQ-9 at 4 months, PSI-IV and microbiome diversity. Conclusion The present findings suggest that an infant stress (assessed by quality of crying) is associated with lower microbiome diversity. Decreased diversity reflects an unhealthy microbiome. Parental depressive symptoms may also influence infant microbiome. Future interventional studies focused on the quality of the infant-caregiver relationship should examine related changes in intestinal microbiota.
ChatSpatial: Schema-Enforced Agentic Orchestration for Reproducible and Cross-Platform Spatial Transcriptomics
Yang, C., Chen, Jun, Zhang, Xi , Chen, Jun

No container title available | March 2, 2026 | Volume: No volume available | Issue: No issue available | Pages: No page available

10.64898/2026.02.26.708361 Get It

Abstract Spatial transcriptomics analyses often require coordinating specialized Python and R methods. When analytical intent is translated through ad hoc scripts or unconstrained LLM generated code, workflows can be difficult to inspect, rerun, or compare. We present ChatSpatial, a schema enforced orchestration framework in which an LLM selects versioned tools and parameters rather than executable code. Built on the Model Context Protocol, ChatSpatial integrates 65 methods across 15 analytical categories into 20 typed tools with curated defaults. In two cancer case studies, ChatSpatial coordinated cross ecosystem workflows that recovered the analytical structure and central conclusions of published ovarian cancer and oral squamous cell carcinoma analyses. Across three LLMs, schema enforcement raised parameter validity from 20% to 98% and stabilized method selection. In end to end benchmarks against two code generation alternatives, it yielded higher execution success and output concordance.