Holden Terry Maecker

This paper identifies 23 candidate causal genes for critical COVID-19 by integrating severity-stratified, longitudinal cis-eQTL mapping with GWAS signals from 1,031 patients.

The paper introduces IMM-AGE, a metric of immune-aging, and shows it independently predicts cardiovascular disease risk across multiple cohorts.

SPP1hi macrophages in RA synovial fibrin deposits promote tissue hyperplasia.

This study uses MOFA and Stabl algorithms to analyze multi-omics vaccine response data, identifying key features for predicting vaccine response and comparing the performance of these methods.

This study identifies bacterial exposure as a key driver of immune ageing and shows residual bacterial DNA in immune cells predicts immune age changes.

This study identifies cytokines that enhance influenza vaccine antibody responses in human organoids and mice, showing their potential as adjuvants to improve vaccine efficacy.

The paper identifies a multi-omics recovery factor predictive of long COVID using data from the IMPACC study, showing it can predict LC risk early after infection.

This study examines cytokine levels in serum during acute respiratory infection and their relationship to age, finding elevated pro-inflammatory cytokines in infected individuals and age-related trends in healthy cohorts.

This study identifies immune subtypes in hospitalized COVID-19 patients linked to clinical outcomes and long COVID risk through longitudinal multi-omics analysis.

This study investigates viral reactivation in COVID-19 patients, finding associations between reactivation of specific virus families and clinical outcomes, including mortality and PASC symptoms.

The paper evaluates deconvolution methods for inferring immune infiltration from bulk gene expression using a DREAM Challenge, finding that some community-contributed methods, especially deep learning-based ones, perform well.

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

The paper identifies platelet transcriptome markers for predicting disease progression in MPNs and develops a machine learning model to predict advanced MF with high accuracy.

The paper introduces FluPRINT, a database integrating multiple parameters to study influenza vaccine immunity, enabling machine learning analysis of vaccine response markers.

This study identifies autoimmunity to hypocretin and molecular mimicry with flu antigens in Type 1 narcolepsy, showing genetic modulation of T cell responses.