An open annotated dataset and baseline machine learning model for segmentation of vertebrae with metastatic bone lesions from CT.

Automatic analysis of pathologic vertebrae from computed tomography (CT) scans could significantly improve the diagnostic management of patients with metastatic spine disease. We provide the first publicly available annotated imaging dataset of cancerous CT spines to help develop artificial intelligence frameworks for automatic vertebrae segmentation and classification. This collection contains a dataset of 55 CT scans collected on patients with various types of primary cancers at two different institutions.

Registration of Longitudinal Spine CTs for Monitoring Lesion Growth.

Accurate and reliable registration of longitudinal spine images is essential for assessment of disease progression and surgical outcome. Implementing a fully automatic and robust registration is crucial for clinical use, however, it is challenging due to substantial change in shape and appearance due to lesions. In this paper we present a novel method to automatically align longitudinal spine CTs and accurately assess lesion progression.

CT-based finite element simulating spatial bone damage accumulation predicts metastatic human vertebrae strength and stiffness.

Pathologic vertebral fractures are devastating for patients with spinal metastases. However, the mechanical process underlying these fractures is poorly understood, limiting physician's ability to predict which vertebral bodies will fail. Here, we show the development of a damage-based finite element framework producing highly reliable pathologic vertebral strength and stiffness predictions from X-Ray computed tomography (CT) data.

Progesterone inactivation in decidual stromal cells: A mechanism for inflammation-induced parturition.

The process of human parturition involves inflammation at the interface where fetal chorion trophoblast cells interact with maternal decidual stromal (DS) cells and maternal immune cells in the decidua (endometrium of pregnancy). This study tested the hypothesis that inflammation at the chorion-decidua interface (CDI) induces labor by negating the capacity for progesterone (P4) to block labor and that this is mediated by inactivation of P4 in DS cells by aldo-keto reductase family 1 member C1 (AKR1C1). In human, Rhesus macaque, and mouse CDI, expression increased in association with term and preterm labor.

Researching COVID to enhance recovery (RECOVER) pregnancy study: Rationale, objectives and design.

Importance: Pregnancy induces unique physiologic changes to the immune response and hormonal changes leading to plausible differences in the risk of developing post-acute sequelae of SARS-CoV-2 (PASC), or Long COVID. Exposure to SARS-CoV-2 during pregnancy may also have long-term ramifications for exposed offspring, and it is critical to evaluate the health outcomes of exposed children. The National Institutes of Health (NIH) Researching COVID to Enhance Recovery (RECOVER) Multi-site Observational Study of PASC aims to evaluate the long-term sequelae of SARS-CoV-2 infection in various populations.