Nima Aghaeepour

LENS decodes musculoskeletal pain intensity from sEMG, showing strong generalization to chronic pain cohorts and revealing a conserved pain signature masked by motor variability.

The paper identifies a plasma proteomic signature linked to blood-brain barrier dysfunction, which predicts cognitive decline after ischemic stroke.

This study identifies persistent immune dysregulation in women with hypertensive disorders of pregnancy years after delivery, linked to increased cardiovascular disease risk.

The paper introduces SIMCO, a single-cell digital twin framework for evaluating PTL prevention drugs, identifying tetrahydrofolate, maprotiline, and aspirin-lansoprazole as top candidates.

This study shows that blocking VCAM1 or VLA4 after stroke preserves vascular integrity and prevents cognitive decline by promoting blood vessel growth and restoring the blood-brain barrier.

This study identifies an immune signature predicting postoperative cognitive decline using single-cell and plasma proteomic data from elderly patients.

This study uses single-cell immunoprofiling to identify distinct immune signatures in Lewy body disease compared to other neurodegenerative and immune conditions.

The paper analyzes synaptic molecular changes in Alzheimer's disease and related conditions using mass cytometry and imaging, identifying disease-specific molecular differences.

The paper develops predictive models for preterm birth using maternal blood multi-omics data, finding transcriptomic and proteomic markers can predict gestational age and spontaneous preterm birth with high accuracy.

The paper identifies non-structural proteins in scalp hair from preschool children that show heritability and are linked to brain development, immune function, and stress response regulation, with age- and sex-related differences.

This study identifies changes in pregnancy-related serum biomarkers early in gestation as indicators of later preeclampsia development, validated in both human and mouse models.

The paper introduces a machine learning method that integrates immunological knowledge to improve predictive power from mass cytometry data.