Pathological Evidence Exploration in Deep Retinal Image Diagnosis

Though deep learning has shown successful performance in classifying the label and severity stage of certain disease, most of them give few evidence on how to make prediction. Here, we propose to exploit the interpretability of deep learning application in medical diagnosis. Inspired by Koch's Postulates, a well-known strategy in medical research to identify the property of pathogen, we define a pathological descriptor that can be extracted from the activated neurons of a diabetic retinopathy detector. To visualize the symptom and feature encoded in this descriptor, we propose a GAN based method to synthesize pathological retinal image given the descriptor and a binary vessel segmentation. Besides, with this descriptor, we can arbitrarily manipulate the position and quantity of lesions. As verified by a panel of 5 licensed ophthalmologists, our synthesized images carry the symptoms that are directly related to diabetic retinopathy diagnosis. The panel survey also shows that our generated images is both qualitatively and quantitatively superior to existing methods.Comment: to appear in AAAI (2019). The first two authors contributed equally to the paper. Corresponding Author: Feng L

Case Report: Metagenomic Next-Generation Sequencing in Diagnosis of Legionella pneumophila Pneumonia in a Patient After Umbilical Cord Blood Stem Cell Transplantation

We report a case of hospital-acquired Legionella pneumonia that was detected by metagenomic next-generation sequencing (mNGS) of blood from a 7-year-old girl after umbilical cord blood stem cell transplantation (UCBT) with myelodysplastic syndrome. UCBT is traditionally associated with an increased risk of infection, particularly during the first 3 months after transplantation. Controlling interstitial pneumonia and severe infection is the key to reducing patient mortality from infection. Legionella pneumophila can cause a mild cough to rapidly fatal pneumonia. After mNGS confirmed that the pathogen was L. pneumophila, azithromycin, cefoperazone sulbactam, and posaconazole were used for treatment, and the patient's temperature decreased and remained normal. The details of this case highlight the benefits of the timely use of metagenomic NGS to identify pathogens for the survival of immunocompromised patients

Physician-modified fenestration or in situ fenestration for preservation of isolated left vertebral artery in thoracic endovascular aortic repair

ObjectiveTo present our experience of preserving the isolated left vertebral artery (ILVA) with physician-modified fenestration (PM-F) or in situ fenestration (ISF) during thoracic endovascular aortic repair (TEVAR) for aortic pathologies involving aortic arch.MethodsThis is a single-center, retrospective, observational cohort study. Between June 2016 and December 2021, 9 patients (8 men; median age 60.0 years old) underwent TEVAR with ILVA reconstruction (PM-F, n = 6; ISF, n = 3) were identified and analyzed.ResultsThe technical success rate was 100%. No early (<30 days) death occurred. No aortic rupture, major stroke or spinal cord injury was observed. The median follow up was 38.0 (rang: 1.0–66.0) months. One death occurred at 56 months, while the reason cannot be identified. No aortic rupture, major stroke or spinal cord injury was observed during follow up. No patient required reintervention. Out of the 22 successfully revascularized target vessels, 2 ILVAs were found occluded in 2 patients at 6 months and 7 months, respectively. However, these two patients were asymptomatic.ConclusionsOur initial experience reveals that PM-F or ISF for ILVA preservation was feasible, safe, and effective during TEVAR for complex thoracic aortic pathologies. However, the patency of preserved ILVA should be improved

Vehicle optimal road departure prevention via model predictive control

This article addresses the problem of road departure prevention using integrated brake control. The scenario considered is when a high speed vehicle leaves the highway on a curve and enters the shoulder or another lane, due to excessive speed, or where the friction of the road drops due to adverse weather conditions. In such a scenario, the vehicle speed is too high for the available tyre-road friction and road departure is inevitable; however, its effect can be minimized with an optimal braking strategy. To achieve online implementation, the task is formulated as a receding horizon optimization problem and solved in a linear model predictive control (MPC) framework. In this formulation, a nonlinear tire model is adopted in order to work properly at the friction limits. The optimization results are close to those obtained previously using a particle model optimization, PPR, coupled to a control algorithm, MHA, specifically designed to operate at the vehicle friction limits. This shows the MPC formulation may equally be effective for vehicle control at the friction limits. The major difference here, compared to the earlier PPR/MHA control formulation, is that the proposed MPC strategy directly generates an optimal brake sequence, while PPR provides an optimal reference first, then MHA responds to the reference to give closed-loop actuator control. The presented MPC approach has the potential to be used in futur