VEXAS without vacuoles: Linking genotype to phenotype.

Introduction: VEXAS syndrome is a rare condition characterized by somatic mutations in the ubiquitin-like modifier activating enzyme 1 () gene and a constellation of clinical/morphologic findings, including the presence of cytoplasmic vacuoles within bone marrow hematopoietic cells.

Methods And Objectives: In this report, we present a case of a male patient diagnosed with VEXAS-associated myelodysplastic syndrome following the detection of a non-canonical p.Gly477Ala variant whose bone marrow biopsy revealed a conspicuous absence of cytoplasmic vacuolization in hematopoietic cells.

Deep learning model to predict the need for mechanical ventilation using chest X-ray images in hospitalised patients with COVID-19.

Objectives: There exists a wide gap in the availability of mechanical ventilator devices and their acute need in the context of the COVID-19 pandemic. An initial triaging method that accurately identifies the need for mechanical ventilation in hospitalised patients with COVID-19 is needed. We aimed to investigate if a potentially deteriorating clinical course in hospitalised patients with COVID-19 can be detected using all X-ray images taken during hospitalisation.

The Cervical Dysplasia Worksheet: A Longitudinal Map of Cervical Dysplasia Cytology and Histology Tests and Procedures.

Objective: Our objectives were to describe the Cervical Dysplasia Worksheet (CDW), provide evidence of its feasibility to view patient cervical dysplasia results over time, and evaluate patient attitudes toward the tool in the setting of abnormal result follow-up.

Materials And Methods: The CDW augments the ASCCP guidelines for managing abnormal cervical cancer screenings by visually depicting cervical cytological and histological history along a color gradient showing severity. We evaluated tool feasibility by graphing a retrospectively reviewed convenience sample of patient data.

Discovery of the Tyrobetaine Natural Products and Their Biosynthetic Gene Cluster via Metabologenomics.

Natural products (NPs) are a rich source of medicines, but traditional discovery methods are often unsuccessful due to high rates of rediscovery. Genetic approaches for NP discovery are promising, but progress has been slow due to the difficulty of identifying unique biosynthetic gene clusters (BGCs) and poor gene expression. We previously developed the metabologenomics method, which combines genomic and metabolomic data to discover new NPs and their BGCs.