Publications by authors named "M Hornig"
Importance: Classification of persons with long COVID (LC) or post-COVID-19 condition must encompass the complexity and heterogeneity of the condition. Iterative refinement of the classification index for research is needed to incorporate newly available data as the field rapidly evolves.
Objective: To update the 2023 research index for adults with LC using additional participant data from the Researching COVID to Enhance Recovery (RECOVER-Adult) study and an expanded symptom list based on input from patient communities.
Objectives: Contrast-enhanced mammography (CEM) is an accurate competitor for contrast-enhanced breast magnetic resonance imaging (CE-MRI), but the examination is limited by the lack of 3D information. Digital breast tomosynthesis (DBT) allows better lesion detection and characterization compared with mammography. The availability of quasi-3D contrast imaging could further improve the performance of CEM.
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- The study explores how exposure to maternal pro-inflammatory cytokines during pregnancy can lead to long-lasting effects on brain function and memory in offspring, with implications for both sexes.
- It highlights that men and women develop differently, suggesting that understanding these sex differences is crucial for identifying early risk factors and interventions for brain aging and memory impairment.
- Results showed that higher levels of maternal cytokines were linked to altered brain activity related to memory in midlife, and these effects persisted from childhood performance into early adulthood, emphasizing the importance of prenatal health on long-term cognitive outcomes.
Article Synopsis
- * Conducted across 26 children's hospitals in the US from March 2020 to May 2023, the research involved analyzing data from over 172,000 eligible children and young adults aged 5 to 20 with confirmed COVID-19.
- * The findings aim to establish a clear association between pre-infection BMI categories—ranging from healthy weight to severe obesity—and the likelihood of experiencing PASC, with statistical analyses adjusting for various demographic and clinical
Pediatric Long COVID has been associated with a wide variety of symptoms, conditions, and organ systems, but distinct clinical presentations, or subphenotypes, are still being elucidated. In this exploratory analysis, we identified a cohort of pediatric (age <21) patients with evidence of Long COVID and no pre-existing complex chronic conditions using electronic health record data from 38 institutions and used an unsupervised machine learning-based approach to identify subphenotypes. Our method, an extension of the Phe2Vec algorithm, uses tens of thousands of clinical concepts from multiple domains to represent patients' clinical histories to then identify groups of patients with similar presentations.
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