David Furman

The paper develops an immune proteomic signature (iPACE) from serum proteins in elderly patients that predicts cardiovascular risk and mortality.

The paper shows that simulated microgravity causes aging-like transcriptional changes in human organoids, with effects varying by tissue type.

The paper develops methods to infer organ aging, senescence, and hallmark scores from blood transcriptomes and facial photographs, showing associations with mortality.

The paper introduces a compositional simulation framework for aging research that enables the integration and testing of mechanistic models using AI agents.

The paper identifies senescent fibroblast heterogeneity in IPF and links it to DNA damage response dysregulation using gene expression analysis.

The paper proposes the Decoherence via Demyelination Hypothesis (DDH) to explain age-related cognitive decline through tract-specific myelin loss and its impact on neural network coordination.

This study shows that cellular senescence in IPF fibroblasts disrupts antiviral responses, leading to increased viral titers and altered inflammation.

This study uses UK Biobank data to develop an 18-year mortality risk estimator for domain-specific intrinsic capacity, identifying immune system activation across domains.

This study shows DNA damage in neurons leads to a senescent-like state similar to Alzheimer's disease, with distinct features from fibroblasts.

This study uses simulated microgravity to model biological aging in humans, finding similar transcriptional changes to natural aging across multiple systems and aging hallmarks.

The paper identifies a distinct p16INK4a-associated senescence signature in human ovarian tissue through proteogenomic profiling and spatial analysis.

The paper identifies and maps senescent cells in postmenopausal ovaries using p16 expression and spatial transcriptomics, revealing a fibro-inflammatory signature associated with aging.

This paper develops a method to predict inflammatory aging from facial photographs using deep learning, showing significant correlation with blood-based biomarkers.

The paper develops a DNA methylation-based epigenetic clock to predict mortality and associate with clinical, immunological, and lifestyle factors related to intrinsic capacity.

The paper establishes a senotype of doxorubicin-induced senescence in postmenopausal human ovarian tissue using single nuclei sequencing and proteomics.

This study shows that substrate stiffness influences doxorubicin-induced senescence and secretory profiles in human pulmonary fibroblasts, with collagen enrichment observed in high-stiffness conditions.

The paper develops mortality-based predictors of biological age for 9 physiological systems using blood data from the UK Biobank and validates them in the Health and Retirement Study.

This study shows that expressing human amyloid-β and Tau in C. elegans neurons causes phenotypic and multi-omic changes, including accelerated aging and metabolic dysfunction.