Dysregulation of long non-coding RNA gene expression pathways in monocytes of type 2 diabetes patients with cardiovascular disease.

Background: Monocytes play a central role in the pathophysiology of cardiovascular complications in type 2 diabetes (T2D) patients through different mechanisms. We investigated diabetes-induced changes in lncRNA genes from T2D patients with cardiovascular disease (CVD), long-duration diabetes, and poor glycemic control.

Methods: We performed paired-end RNA sequencing of monocytes from 37 non-diabetes controls and 120 patients with T2D, of whom 86 had either macro or microvascular disease or both.

Global Sagittal Angle and T9-tilt seem to be the most clinically and functionally relevant global alignment parameters in patients with Adult Spinal Deformity.

Introduction: Radiographic analysis is necessary for the assessment and the surgical planning in adults with spinal deformity (ASD). Restoration of global alignment is key to improving patient's quality of life. However, the large number of existing global alignment parameters can be confusing for surgeons.

Functional assessment using 3D movement analysis can better predict health-related quality of life outcomes in patients with adult spinal deformity: a machine learning approach.

Introduction: Adult spinal deformity (ASD) is classically evaluated by health-related quality of life (HRQoL) questionnaires and static radiographic spino-pelvic and global alignment parameters. Recently, 3D movement analysis (3DMA) was used for functional assessment of ASD to objectively quantify patient's independence during daily life activities. The aim of this study was to determine the role of both static and functional assessments in the prediction of HRQoL outcomes using machine learning methods.

Spinopelvic Adaptations in Standing and Sitting Positions in Patients With Adult Spinal Deformity.

Purpose To describe spinopelvic adaptations in the standing and sitting positions in patients with adult spinal deformity (ASD). Methods Ninety-five patients with ASD and 32 controls completed health-related quality of life (HRQOL) questionnaires: short form 36 (SF36), Oswestry Disability Index (ODI), and visual analog scale (VAS) for pain. They underwent biplanar radiography in both standing and sitting positions.

Evaluation of how users interface with holographic augmented reality surgical scenes: Interactive planning MR-Guided prostate biopsies.

Background: User interfaces play a vital role in the planning and execution of an interventional procedure. The objective of this study is to investigate the effect of using different user interfaces for planning transrectal robot-assisted MR-guided prostate biopsy (MRgPBx) in an augmented reality (AR) environment.

Method: End-user studies were conducted by simulating an MRgPBx system with end- and side-firing modes.

End-user evaluation of software-generated intervention planning environment for transrectal magnetic resonance-guided prostate biopsies.

Background: This study presents user evaluation studies to assess the effect of information rendered by an interventional planning software on the operator's ability to plan transrectal magnetic resonance (MR)-guided prostate biopsies using actuated robotic manipulators.

Methods: An intervention planning software was developed based on the clinical workflow followed for MR-guided transrectal prostate biopsies. The software was designed to interface with a generic virtual manipulator and simulate an intervention environment using 2D and 3D scenes.

Lattice-Boltzmann interactive blood flow simulation pipeline.

Purpose: Cerebral aneurysms are one of the prevalent cerebrovascular disorders in adults worldwide and caused by a weakness in the brain artery. The most impressive treatment for a brain aneurysm is interventional radiology treatment, which is extremely dependent on the skill level of the radiologist. Hence, accurate detection and effective therapy for cerebral aneurysms still remain important clinical challenges.

Robust active shape models: a robust, generic and simple automatic segmentation tool.

This paper presents a new segmentation algorithm which combines active shape model and robust point matching techniques. It can use any simple feature detector to extract a large number of feature points in the image. Robust point matching is then used to search for the correspondences between feature and model points while the model is being deformed along the modes of variation of the active shape model.