Biomedical subjects

Jun He

Publications and source records attributed to Jun He.

3 recordsLinked to original sources

PubMed2026

Validated UPLC-MS/MS quantification and intracellular PK-PD Modeling of periplocin-related cardiac glycosides in H/R-injured H9c2 cells.

Reliable intracellular quantification is essential for characterizing the target-site disposition and exposure-response relationships of bioactive natural products. In this study, an ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) method was developed and validated for the simultaneous determination of periplocin and four related cardiac glycoside metabolites in H9c2 cell lysates. Acceptable linearity, precision, recovery, and stability were achieved for intracellular quantification. Cells were treated with each compound at 50 μM, and intracellular concentrations and cell viability were monitored over 48 h. In hypoxia/reoxygenation (H/R) -injured cells, the time to maximum intracellular concentration was shortened for all five compounds, indicating altered cellular disposition under injury conditions. Cell viability was improved by all compounds during the observation period. Pharmacokinetic-pharmacodynamic (PK-PD) integration was performed using a sigmoid Emax model, and acceptable model fits were obtained, with Akaike information criterion (AIC) values ranging from 79.22 to 130.46. Low apparent EC50 values were estimated under this single-dose design, whereas the estimated Ke0 values suggested delayed equilibration with the effect compartment. These findings indicate that sustained cytoprotective responses can be produced by periplocin and related metabolic markers in injured cardiomyocytes. This intracellular bioanalytical strategy provides a quantitative approach for linking cellular exposure to pharmacodynamic response and may support further evaluation of periplocin-related cardiac glycosides.

Tandem Mass Spectrometry↗

PubMed2026

Epigenetic and immunological alterations in umbilical cord blood of overweight/obese women with gestational diabetes mellitus: insights into DNA methylation signatures and immune cell dysregulation.

BACKGROUND: Gestational diabetes mellitus (GDM) is a common pregnancy complication associated with adverse maternal and neonatal outcomes. Epigenetic modifications may reflect intrauterine metabolic exposure and contribute to immune and metabolic alterations. This study aimed to explore DNA methylation profiles in umbilical cord blood from overweight and obese women with and without GDM. METHODS: Umbilical cord blood samples from 30 overweight/obese pregnant women (with and without GDM) were analyzed using the Illumina 850 K methylation array to identify differentially methylated positions (DMPs) and regions (DMRs). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to assess the functional relevance of methylation changes. Immune cell composition was estimated using deconvolution analysis and further examined in an independent single-cell RNA sequencing (scRNA-seq) cohort. Lasso regression was applied to identify CpG sites associated with GDM status and construct a preliminary methylation-based classification model. RESULTS: A total of 23,331 hypermethylated and 29,501 hypomethylated DMPs were identified between women with and without GDM, with hypomethylation predominating. Enrichment analyses indicated associations with neurodevelopmental pathways, metabolic processes, immune regulation, and epigenetic modification. Immune deconvolution analysis suggested reduced proportions of CD4+ T cells (p < 0.05) and a trend toward decreased NK cells in the GDM group, alongside increased CD8+ T cells and neutrophils. Seven CpG sites were selected for model construction and demonstrated strong discriminatory performance within this cohort. CONCLUSION: This exploratory study identifies distinct cord blood DNA methylation patterns associated with GDM in overweight/obese pregnancies. The findings suggest potential links between epigenetic alterations and immune cell composition in GDM-exposed offspring. The identified CpG signature warrants further validation in larger, prospective cohorts to determine its clinical applicability.

Humans↗

PubMed2026

Assessing the comorbidity between asthma and depression through polygenic risk scoring and time-to-event models.

BACKGROUND: Patients with asthma have an increased risk of developing depression, affecting their quality of life. To date, the processes contributing to this comorbidity remain unclear. METHODS: We integrated two large genome-wide association studies (88,486 patients with asthma and 447,859 controls; 412,024 patients with depression and 1,587,577 controls) with cross-sectional and longitudinal information available from the All of Us Research Program (N = 87,167) through polygenic risk scoring (PRS), Cox proportional-hazards models, one-sample Mendelian randomization (MR), and gene-set and drug-repurposing analyses. RESULTS: We observed that depression PRS was associated with increased asthma risk (hazard ratio, HR = 1.13, 95% CI = 1.09-1.17), also when accounting for comorbidity status (HR = 1.08, 95% CI = 1.04-1.12). Conversely, the effect of asthma PRS was null after accounting for comorbidity status. One-sample MR analysis showed an effect of depression genetic liability on asthma, ranging from beta = 0.36 ± 0.03 when considering a linear relationship to beta = 3.21 ± 0.31 when considering possible nonlinear relationships. Conversely, the effect of asthma genetic risk on depression was null after accounting for potential confounders. The gene-set analyses showed that asthma and depression polygenic risks share biological processes, molecular functions, and cellular components related to the immune system and the lung-brain axis. CONCLUSIONS: Genetic predisposition contributes to asthma-depression comorbidity through direct effects and shared pathogenic processes. These findings highlight the potential to develop targeted interventions to prevent and treat the co-occurrence of respiratory and neuropsychiatric disorders.

Comorbidity↗