Left Ventricle Quantification with Cardiac MRI: Deep Learning Meets Statistical Models of Deformation

Deep learning has been widely applied for left ventricle (LV) analysis, obtaining state of the art results in quantification through image segmentation. When the training datasets are limited, data augmentation becomes critical, but standard augmentation methods do not usually incorporate the natural variation of anatomy. In this paper we propose a pipeline for LV quantification applying our data augmentation methodology based on statistical models of deformations (SMOD) to quantify LV based on segmentation of cardiac MR (CMR) images, and present an in-depth analysis of the effects of deformation parameters in SMOD performance. We trained and evaluated our pipeline on the MICCAI 2019 Left Ventricle Full Quantification Challenge dataset, and achieved average mean absolute error (MAE) for areas, dimensions, regional wall thickness and phase of 106 mm2, 1.52 mm, 1.01 mm and 8.0% respectively in a 3-fold cross-validation experiment

Constructing bilayer and volumetric atrial models at scale.

To enable large in silico trials and personalized model predictions on clinical timescales, it is imperative that models can be constructed quickly and reproducibly. First, we aimed to overcome the challenges of constructing cardiac models at scale through developing a robust, open-source pipeline for bilayer and volumetric atrial models. Second, we aimed to investigate the effects of fibres, fibrosis and model representation on fibrillatory dynamics. To construct bilayer and volumetric models, we extended our previously developed coordinate system to incorporate transmurality, atrial regions and fibres (rule-based or data driven diffusion tensor magnetic resonance imaging (MRI)). We created a cohort of 1000 biatrial bilayer and volumetric models derived from computed tomography (CT) data, as well as models from MRI, and electroanatomical mapping. Fibrillatory dynamics diverged between bilayer and volumetric simulations across the CT cohort (correlation coefficient for phase singularity maps: left atrial (LA) 0.27 ± 0.19, right atrial (RA) 0.41 ± 0.14). Adding fibrotic remodelling stabilized re-entries and reduced the impact of model type (LA: 0.52 ± 0.20, RA: 0.36 ± 0.18). The choice of fibre field has a small effect on paced activation data (less than 12 ms), but a larger effect on fibrillatory dynamics. Overall, we developed an open-source user-friendly pipeline for generating atrial models from imaging or electroanatomical mapping data enabling in silico clinical trials at scale (https://github.com/pcmlab/atrialmtk)

Students’ perceptions of patient safety during the transition from undergraduate to postgraduate training: an activity theory analysis

Evidence that medical error can cause harm to patients has raised the attention of the health care community towards patient safety and influenced how and what medical students learn about it. Patient safety is best taught when students are participating in clinical practice where they actually encounter patients at risk. This type of learning is referred to as workplace learning, a complex system in which various factors influence what is being learned and how. A theory that can highlight potential difficulties in this complex learning system about patient safety is activity theory. Thirty-four final year undergraduate medical students participated in four focus groups about their experiences concerning patient safety. Using activity theory as analytical framework, we performed constant comparative thematic analysis of the focus group transcripts to identify important themes. We found eight general themes relating to two activities: learning to be a doctor and delivering safe patient care. Simultaneous occurrence of these two activities can cause contradictions. Our results illustrate the complexity of learning about patient safety at the workplace. Students encounter contradictions when learning about patient safety, especially during a transitional phase of their training. These contradictions create potential learning opportunities which should be used in education about patient safety. Insight into the complexities of patient safety is essential to improve education in this important area of medicine

Unlocking the grid: Language-in-education policy realisation in post-apartheid South Africa

This paper reflects on the state of educational language policy two decades into a postApartheid South Africa caught between official multilingualism and English. The focus is on the national language-in-education policy (LiEP) that advocates additive bi/multilingualism, and a provincial counterpart, the language transformation plan (LTP). Using Ricento and Hornberger’s onion metaphor, the paper seeks to uncover the meanings of policy realisation in education at legislative, institutional, and interpersonal levels. The LiEP’s non-realisation at institutional level is indexed by a ‘gridlock of collusion’ (Alexander, personal communication) between political elites and the majority of African-language speakers, who emulatively seek the goods that an English-medium education promises. To illustrate how teachers can become policy advocates, data are presented from a bilingual education in-service programme that supported the LTP. The paper argues that sociolinguistic insights into speakers’ heteroglossic practices should be used to counter prevailing monoglossic policy discourses and school language practices, and that all languages should be used as learning resources. Strategic essentialism would recognise the schooling system’s need to separately classify language subjects and to identify the languages most productively used for teaching across the curriculum. The paper concludes with a call for the revision of the LiEP

Lagrangian coherent structure assisted path planning for transoceanic autonomous underwater vehicle missions

Abstract Transoceanic Gliders are Autonomous Underwater Vehicles (AUVs) for which there is a developing and expanding range of applications in open-seas research, technology and underwater clean transport. Mature glider autonomy, operating depth (0–1000 meters) and low energy consumption without a CO2 footprint enable evolutionary access across ocean basins. Pursuant to the first successful transatlantic glider crossing in December 2009, the Challenger Mission has opened the door to long-term, long-distance routine transoceanic AUV missions. These vehicles, which glide through the water column between 0 and 1000 meters depth, are highly sensitive to the ocean current field. Consequently, it is essential to exploit the complex space-time structure of the ocean current field in order to plan a path that optimizes scientific payoff and navigation efficiency. This letter demonstrates the capability of dynamical system theory for achieving this goal by realizing the real-time navigation strategy for the transoceanic AUV named Silbo, which is a Slocum deep-glider (0–1000 m), that crossed the North Atlantic from April 2016 to March 2017. Path planning in real time based on this approach has facilitated an impressive speed up of the AUV to unprecedented velocities resulting in major battery savings on the mission, offering the potential for routine transoceanic long duration missions

Fluids and barriers of the CNS establish immune privilege by confining immune surveillance to a two-walled castle moat surrounding the CNS castle

Neuronal activity within the central nervous system (CNS) strictly depends on homeostasis and therefore does not tolerate uncontrolled entry of blood components. It has been generally believed that under normal conditions, the endothelial blood-brain barrier (BBB) and the epithelial blood-cerebrospinal fluid barrier (BCSFB) prevent immune cell entry into the CNS. This view has recently changed when it was realized that activated T cells are able to breach the BBB and the BCSFB to perform immune surveillance of the CNS. Here we propose that the immune privilege of the CNS is established by the specific morphological architecture of its borders resembling that of a medieval castle. The BBB and the BCSFB serve as the outer walls of the castle, which can be breached by activated immune cells serving as messengers for outside dangers. Having crossed the BBB or the BCSFB they reach the castle moat, namely the cerebrospinal fluid (CSF)-drained leptomeningeal and perivascular spaces of the CNS. Next to the CNS parenchyma, the castle moat is bordered by a second wall, the glia limitans, composed of astrocytic foot processes and a parenchymal basement membrane. Inside the castle, that is the CNS parenchyma proper, the royal family of sensitive neurons resides with their servants, the glial cells. Within the CSF-drained castle moat, macrophages serve as guards collecting all the information from within the castle, which they can present to the immune-surveying T cells. If in their communication with the castle moat macrophages, T cells recognize their specific antigen and see that the royal family is in danger, they will become activated and by opening doors in the outer wall of the castle allow the entry of additional immune cells into the castle moat. From there, immune cells may breach the inner castle wall with the aim to defend the castle inhabitants by eliminating the invading enemy. If the immune response by unknown mechanisms turns against self, that is the castle inhabitants, this may allow for continuous entry of immune cells into the castle and lead to the death of the castle inhabitants, and finally members of the royal family, the neurons. This review will summarize the molecular traffic signals known to allow immune cells to breach the outer and inner walls of the CNS castle moat and will highlight the importance of the CSF-drained castle moat in maintaining immune surveillance and in mounting immune responses in the CNS

Ensemble Models of Neutrophil Trafficking in Severe Sepsis

A hallmark of severe sepsis is systemic inflammation which activates leukocytes and can result in their misdirection. This leads to both impaired migration to the locus of infection and increased infiltration into healthy tissues. In order to better understand the pathophysiologic mechanisms involved, we developed a coarse-grained phenomenological model of the acute inflammatory response in CLP (cecal ligation and puncture)-induced sepsis in rats. This model incorporates distinct neutrophil kinetic responses to the inflammatory stimulus and the dynamic interactions between components of a compartmentalized inflammatory response. Ensembles of model parameter sets consistent with experimental observations were statistically generated using a Markov-Chain Monte Carlo sampling. Prediction uncertainty in the model states was quantified over the resulting ensemble parameter sets. Forward simulation of the parameter ensembles successfully captured experimental features and predicted that systemically activated circulating neutrophils display impaired migration to the tissue and neutrophil sequestration in the lung, consequently contributing to tissue damage and mortality. Principal component and multiple regression analyses of the parameter ensembles estimated from survivor and non-survivor cohorts provide insight into pathologic mechanisms dictating outcome in sepsis. Furthermore, the model was extended to incorporate hypothetical mechanisms by which immune modulation using extracorporeal blood purification results in improved outcome in septic rats. Simulations identified a sub-population (about of the treated population) that benefited from blood purification. Survivors displayed enhanced neutrophil migration to tissue and reduced sequestration of lung neutrophils, contributing to improved outcome. The model ensemble presented herein provides a platform for generating and testing hypotheses in silico, as well as motivating further experimental studies to advance understanding of the complex biological response to severe infection, a problem of growing magnitude in humans

Repetitive Pertussis Toxin Promotes Development of Regulatory T Cells and Prevents Central Nervous System Autoimmune Disease

Bacterial and viral infections have long been implicated in pathogenesis and progression of multiple sclerosis (MS). Incidence and severity of its animal model experimental autoimmune encephalomyelitis (EAE) can be enhanced by concomitant administration of pertussis toxin (PTx), the major virulence factor of Bordetella pertussis. Its adjuvant effect at the time of immunization with myelin antigen is attributed to an unspecific activation and facilitated migration of immune cells across the blood brain barrier into the central nervous system (CNS). In order to evaluate whether recurring exposure to bacterial antigen may have a differential effect on development of CNS autoimmunity, we repetitively administered PTx prior to immunization. Mice weekly injected with PTx were largely protected from subsequent EAE induction which was reflected by a decreased proliferation and pro-inflammatory differentiation of myelin-reactive T cells. Splenocytes isolated from EAE-resistant mice predominantly produced IL-10 upon re-stimulation with PTx, while non-specific immune responses were unchanged. Longitudinal analyses revealed that repetitive exposure of mice to PTx gradually elevated serum levels for TGF-β and IL-10 which was associated with an expansion of peripheral CD4+CD25+FoxP3+ regulatory T cells (Treg). Increased frequency of Treg persisted upon immunization and thereafter. Collectively, these data suggest a scenario in which repetitive PTx treatment protects mice from development of CNS autoimmune disease through upregulation of regulatory cytokines and expansion of CD4+CD25+FoxP3+ Treg. Besides its therapeutic implication, this finding suggests that encounter of the immune system with microbial products may not only be part of CNS autoimmune disease pathogenesis but also of its regulation