DNA Methylation signatures underpinning blood neutrophil to lymphocyte ratio during first week of human life.

Understanding of newborn immune ontogeny in the first week of life will enable age-appropriate strategies for safeguarding vulnerable newborns against infectious diseases. Here we conducted an observational study exploring the immunological profile of infants longitudinally throughout their first week of life. Our Expanded Program on Immunization - Human Immunology Project Consortium (EPIC-HIPC) studies the epigenetic regulation of systemic immunity using small volumes of peripheral blood samples collected from West African neonates on days of life (DOL) 0, 1, 3, and 7.

Breastfeeding and Neonatal Age Influence Neutrophil-Driven Ontogeny of Blood Cell Populations in the First Week of Human Life.

The first few days of life are characterized by rapid external and internal changes that require substantial immune system adaptations. Despite growing evidence of the impact of this period on lifelong immune health, this period remains largely uncharted. To identify factors that may impact the trajectory of immune development, we conducted stringently standardized, high-throughput phenotyping of peripheral white blood cell (WBC) populations from 796 newborns across two distinct cohorts (The Gambia, West Africa; Papua New Guinea, Melanesia) in the framework of a Human Immunology Project Consortium (HIPC) study.

MAGIC-DR: An interpretable machine-learning guided approach for acute myeloid leukemia measurable residual disease analysis.

Multiparameter flow cytometry is widely used for acute myeloid leukemia minimal residual disease testing (AML MRD) but is time consuming and demands substantial expertise. Machine learning offers potential advancements in accuracy and efficiency, but has yet to be widely adopted for this application. To explore this, we trained single cell XGBoost classifiers from 98 diagnostic AML cell populations and 30 MRD negative samples.

Recommendations for using artificial intelligence in clinical flow cytometry.

Flow cytometry is a key clinical tool in the diagnosis of many hematologic malignancies and traditionally requires close inspection of digital data by hematopathologists with expert domain knowledge. Advances in artificial intelligence (AI) are transferable to flow cytometry and have the potential to improve efficiency and prioritization of cases, reduce errors, and highlight fundamental, previously unrecognized associations with underlying biological processes. As a multidisciplinary group of stakeholders, we review a range of critical considerations for appropriately applying AI to clinical flow cytometry, including use case identification, low and high risk use cases, validation, revalidation, computational considerations, and the present regulatory frameworks surrounding AI in clinical medicine.

flowSim: Near duplicate detection for flow cytometry data.

The analysis of large amounts of data is important for the development of machine learning (ML) models. flowSim is the first algorithm designed to visualize, detect and remove highly redundant information in flow cytometry (FCM) training sets to decrease the computational time for training and increase the performance of ML algorithms by reducing overfitting. flowSim performs near duplicate image detection by combining community detection algorithms with the density analysis of the marker expression values.