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Genomic imprinting is an important epigenetic process that silences one of the parentally-inherited alleles of a gene and thereby exhibits allelic-specific expression (ASE). Detection of human imprinting events is hampered by the infeasibility of the reciprocal mating system in humans and the removal of ASE events arising from non-imprinting factors. Here, we describe a pipeline with the pattern of reciprocal allele descendants (RADs) through genotyping and transcriptome sequencing data across independent parent-offspring trios to discriminate between varied types of ASE (e.g., imprinting, genetic variation-dependent ASE, and random monoallelic expression (RME)). We show that the vast majority of ASE events are due to sequence-dependent genetic variant, which are evolutionarily conserved and may themselves play a cis-regulatory role. Particularly, 74% of non-RAD ASE events, even though they exhibit ASE biases toward the same parentally-inherited allele across different individuals, are derived from genetic variation but not imprinting. We further show that the RME effect may affect the effectiveness of the population-based method for detecting imprinting events and our pipeline can help to distinguish between these two ASE types. Taken together, this study provides a good indicator for categorization of different types of ASE, opening up this widespread and complex mechanism for comprehensive characterization.
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http://dx.doi.org/10.1038/s41598-017-07514-z | DOI Listing |
EClinicalMedicine
September 2025
Department of Internal Medicine, Radboud University Medical Center, Nijmegen, the Netherlands.
Background: How to identify suspected infection for sepsis surveillance purposes remains a well-recognised challenge. This study aimed to operationalise suspected infection for sepsis surveillance by developing an interpretable machine learning (ML) model for retrospective identification of patients with sepsis.
Methods: This multicentre cohort and machine learning study was conducted in two Dutch tertiary care hospitals.
J Intensive Care
August 2025
Department of Critical Care Medicine and Division of Pulmonary and Critical Care Medicine, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.
Background: The Adult Sepsis Event (ASE) criteria, developed by the US. Centers for Disease Control and Prevention (CDC), utilize electronic Sequential Organ Failure Assessment (eSOFA) scores derived from structured electronic health records to retrospectively detect organ dysfunction in patients with suspected sepsis. While validated primarily in inpatient cohorts, their applicability in emergency department (ED) populations remains uncertain.
View Article and Find Full Text PDFAnat Sci Educ
August 2025
Department of Anatomy, Cell Biology, & Physiology, Indiana University School of Medicine, Indianapolis, Indiana, USA.
Novice faculty mentors often struggle with the transition from mentee to mentor. Although they may face similar challenges, each mentor's experience and journey of professional identity formation is unique, influenced by their background, experiences, relationships, and context. This autoethnographic study describes my personal experiences as a first-year faculty mentor in medical education, including the challenges I encountered, lessons I learned, and recommendations I have for novice faculty mentors in similar situations.
View Article and Find Full Text PDFInfect Control Hosp Epidemiol
August 2025
Division of Infectious Diseases, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.
Objective: To evaluate potential modifications to the Centers for Disease Control and Prevention (CDC)'s Adult Sepsis Event (ASE) definition aimed at mitigating variable blood culturing practices, better-capturing cases where timely care may have prevented deterioration, and improving clinical credibility.
Design: Retrospective observational study.
Setting: 5 US hospitals.
Front Artif Intell
July 2025
Siemens Healthineers, Princeton, NJ, United States.
Introduction: Early identification of sepsis in the emergency department using machine learning remains a challenging problem, primarily due to the lack of a gold standard for sepsis diagnosis, the heterogeneity in clinical presentations, and the impact of confounding conditions.
Methods: In this work, we present a deep-learning-based predictive model designed to enable early detection of patients at risk of developing sepsis, using data from the first 24 h of admission. The model is based on routine blood test results commonly performed on patients, including CBC (Complete Blood Count), CMP (Comprehensive Metabolic Panel), lipid panels, vital signs, age, and sex.