A comprehensive stroke risk assessment by combining atrial computational fluid dynamics simulations and functional patient data.

Stroke, a major global health concern often rooted in cardiac dynamics, demands precise risk evaluation for targeted intervention. Current risk models, like the CHA 2 DS 2 -VASc score, often lack the granularity required for personalized predictions. In this study, we present a nuanced and thorough stroke risk assessment by integrating functional insights from cardiac magnetic resonance (CMR) with patient-specific computational fluid dynamics (CFD) simulations. Our cohort, evenly split between control and stroke groups, comprises eight patients. Utilizing CINE CMR, we compute kinematic features, revealing smaller left atrial volumes for stroke patients. The incorporation of patient-specific atrial displacement into our hemodynamic simulations unveils the influence of atrial compliance on the flow fields, emphasizing the importance of LA motion in CFD simulations and challenging the conventional rigid wall assumption in hemodynamics models. Standardizing hemodynamic features with functional metrics enhances the differentiation between stroke and control cases. While standalone assessments provide limited clarity, the synergistic fusion of CMR-derived functional data and patient-informed CFD simulations offers a personalized and mechanistic understanding, distinctly segregating stroke from control cases. Specifically, our investigation reveals a crucial clinical insight: normalizing hemodynamic features based on ejection fraction fails to differentiate between stroke and control patients. Differently, when normalized with stroke volume, a clear and clinically significant distinction emerges and this holds true for both the left atrium and its appendage, providing valuable implications for precise stroke risk assessment in clinical settings. This work introduces a novel framework for seamlessly integrating hemodynamic and functional metrics, laying the groundwork for improved predictive models, and highlighting the significance of motion-informed, personalized risk assessments.

A comprehensive stroke risk assessment by combining atrial computational fluid dynamics simulations and functional patient data.

Stroke, a major global health concern often rooted in cardiac dynamics, demands precise risk evaluation for targeted intervention. Current risk models, like the CHA2DS2-VASc score, often lack the granularity required for personalized predictions. In this study, we present a nuanced and thorough stroke risk assessment by integrating functional insights from cardiac magnetic resonance (CMR) with patient-specific computational fluid dynamics (CFD) simulations. Our cohort, evenly split between control and stroke groups, comprises eight patients. Utilizing CINE CMR, we compute kinematic features, revealing smaller left atrial volumes for stroke patients. The incorporation of patient-specific atrial displacement into our hemodynamic simulations unveils the influence of atrial compliance on the flow fields, emphasizing the importance of LA motion in CFD simulations and challenging the conventional rigid wall assumption in hemodynamics models. Standardizing hemodynamic features with functional metrics enhances the differentiation between stroke and control cases. While standalone assessments provide limited clarity, the synergistic fusion of CMR-derived functional data and patient-informed CFD simulations offers a personalized and mechanistic understanding, distinctly segregating stroke from control cases. Specifically, our investigation reveals a crucial clinical insight: normalizing hemodynamic features based on ejection fraction fails to differentiate between stroke and control patients. Differently, when normalized with stroke volume, a clear and clinically significant distinction emerges and this holds true for both the left atrium and its appendage, providing valuable implications for precise stroke risk assessment in clinical settings. This work introduces a novel framework for seamlessly integrating hemodynamic and functional metrics, laying the groundwork for improved predictive models, and highlighting the significance of motion-informed, personalized risk assessments.

A Correspondence-Based Network Approach for Groupwise Analysis of Patient-Specific Spatiotemporal Data.

Methods for statistically analyzing patient-specific data that vary both spatially and over time are currently either limited to summary statistics or require elaborate surface registration. We propose a new method, called correspondence-based network analysis, which leverages particle-based shape modeling to establish correspondence across a population and preserve patient-specific measurements and predictions through statistical analysis. Herein, we evaluated this method using three published datasets of the hip describing cortical bone thickness of the proximal femur, cartilage contact stress, and dynamic joint space between control and patient cohorts to evaluate activity- and group-based differences, as applicable, using traditional statistical parametric mapping (SPM) and our proposed spatially considerate correspondence-based network analysis approach. The network approach was insensitive to correspondence density, while the traditional application of SPM showed decreasing area of the region of significance with increasing correspondence density. In comparison to SPM, the network approach identified broader and more connected regions of significance for all three datasets. The correspondence-based network analysis approach identified differences between groups and activities without loss of subject and spatial specificity which could improve clinical interpretation of results.

Differentiated experiences of financial precarity and lived precariousness among international students in Australia.

Empirical research on international student migrants has sometimes homogenised this group, framing it as predominantly made up of privileged members of the global middle-class. This has led to calls to acknowledge and address the precarity faced by international students in their respective host countries more comprehensively. This study aims to explore how levels of financial precarity vary among international students in Australia, and how this in turn contributes to varying levels of precariousness in the personal spheres of students' lives. In doing so, we centre and refine the concept of precarity for use in studies of internationally mobile students, arguing for its use as a 'relational nexus', bridging financial precarity and broader lived experiences. Drawing on a large-scale survey and semi-structured interviews with 48 students, we emphasise the linkages between financial precarity and precariousness as a socio-ontological experience, explored through the examples of time poverty, physical and mental wellbeing, and relationships.

Statistical multi-level shape models for scalable modeling of multi-organ anatomies.

Statistical shape modeling is an indispensable tool in the quantitative analysis of anatomies. Particle-based shape modeling (PSM) is a state-of-the-art approach that enables the learning of population-level shape representation from medical imaging data (e.g., CT, MRI) and the associated 3D models of anatomy generated from them. PSM optimizes the placement of a dense set of landmarks (i.e., correspondence points) on a given shape cohort. PSM supports multi-organ modeling as a particular case of the conventional single-organ framework via a global statistical model, where multi-structure anatomy is considered as a single structure. However, global multi-organ models are not scalable for many organs, induce anatomical inconsistencies, and result in entangled shape statistics where modes of shape variation reflect both within- and between-organ variations. Hence, there is a need for an efficient modeling approach that can capture the inter-organ relations (i.e., pose variations) of the complex anatomy while simultaneously optimizing the morphological changes of each organ and capturing the population-level statistics. This paper leverages the PSM approach and proposes a new approach for correspondence-point optimization of multiple organs that overcomes these limitations. The central idea of multilevel component analysis, is that the shape statistics consists of two mutually orthogonal subspaces: the within-organ subspace and the between-organ subspace. We formulate the correspondence optimization objective using this generative model. We evaluate the proposed method using synthetic shape data and clinical data for articulated joint structures of the spine, foot and ankle, and hip joint.

Learning spatiotemporal statistical shape models for non-linear dynamic anatomies.

Numerous clinical investigations require understanding changes in anatomical shape over time, such as in dynamic organ cycle characterization or longitudinal analyses (e.g., for disease progression). Spatiotemporal statistical shape modeling (SSM) allows for quantifying and evaluating dynamic shape variation with respect to a cohort or population of interest. Existing data-driven SSM approaches leverage information theory to capture population-level shape variations by learning correspondence-based (landmark) representations of shapes directly from data using entropy-based optimization schemes. These approaches assume sample independence and thus are unsuitable for sequential dynamic shape observations. Previous methods for adapting entropy-based SSM optimization schemes for the spatiotemporal case either utilize a cross-sectional design (ignoring within-subject correlation) or impose other limiting assumptions, such as the linearity of shape dynamics. Here, we present a principled approach to spatiotemporal SSM that relaxes these assumptions to correctly capture population-level shape variation over time. We propose to incorporate modeling the underlying time dependency into correspondence optimization via a regularized principal component polynomial regression. This approach is flexible enough to capture non-linear temporal dynamics while encoding population-specific spatial regularity. We demonstrate our method's efficacy on synthetic data and left atrium segmented from cardiac MRI scans. Our approach better captures the population modes of variation and a statistically significant time dependency than existing methods.

Precarity Before and During the Pandemic: International Student Employment and Personal Finances in Australia.

There is mounting evidence of increased international student financial and work precarity over the last decade in Australia. Yet, there has been a little scholarly analysis of which students are most affected by precarity and its sources. Drawing on two surveys of international students in Australia's two largest cities, conducted before and during the pandemic, we investigate the financial and work vulnerabilities of international students. We demonstrate that vulnerability is related to characteristics which describe particular cohorts of students: being from low-income countries, working class families, seeking a low-level qualification, enrolled in a non-university institution, and being without a scholarship. The concepts of "noncitizenship" and "work precarity" are used to explain how the mechanisms of each characteristic heighten vulnerability, thereby contributing to a broader evidence-base about the causality of international student precarity.

Particle-Based Shape Modeling for Arbitrary Regions-of-Interest.

Statistical Shape Modeling (SSM) is a quantitative method for analyzing morphological variations in anatomical structures. These analyses often necessitate building models on targeted anatomical regions of interest to focus on specific morphological features. We propose an extension to particle-based shape modeling (PSM), a widely used SSM framework, to allow shape modeling to arbitrary regions of interest. Existing methods to define regions of interest are computationally expensive and have topological limitations. To address these shortcomings, we use mesh fields to define free-form constraints, which allow for delimiting arbitrary regions of interest on shape surfaces. Furthermore, we add a quadratic penalty method to the model optimization to enable computationally efficient enforcement of any combination of cutting-plane and free-form constraints. We demonstrate the effectiveness of this method on a challenging synthetic dataset and two medical datasets.

Explaining Government Policy Inaction on International Student Housing in Australia: The Perspectives of Stakeholders.

Housing is a major concern for many international students. This is especially so in those countries where students are mostly dependent on the private market for their accommodation. Australia is one such country, and is one of the world's major destinations for international students. This article analyses governmental failure to address problems relating to international student housing affordability and conditions. Using theory on 'policy inaction' to frame the analysis, we draw on 20 interviews with policy stakeholders to explain the Australian government's reliance on: (1) market-based housing provision for international students, and (2) a longstanding policy preference not to provide support. Interviewees were widely critical of the lack of action to address international student housing problems and understood inaction in relation, rather than in opposition, to the dominance of market-based action in housing and higher education. However, analysis of stakeholder perspectives also illuminates how policy-making action benefiting some emerges as inaction for others left behind or overlooked by the status quo. The interview data points to the need for government to overhaul its policy framework, and in doing so, to collaborate with higher education providers in revising the market-based regulatory approach. The main implications for theory and policy are discussed.

Computer clinical decision support that automates personalized clinical care: a challenging but needed healthcare delivery strategy.

How to deliver best care in various clinical settings remains a vexing problem. All pertinent healthcare-related questions have not, cannot, and will not be addressable with costly time- and resource-consuming controlled clinical trials. At present, evidence-based guidelines can address only a small fraction of the types of care that clinicians deliver. Furthermore, underserved areas rarely can access state-of-the-art evidence-based guidelines in real-time, and often lack the wherewithal to implement advanced guidelines. Care providers in such settings frequently do not have sufficient training to undertake advanced guideline implementation. Nevertheless, in advanced modern healthcare delivery environments, use of eActions (validated clinical decision support systems) could help overcome the cognitive limitations of overburdened clinicians. Widespread use of eActions will require surmounting current healthcare technical and cultural barriers and installing clinical evidence/data curation systems. The authors expect that increased numbers of evidence-based guidelines will result from future comparative effectiveness clinical research carried out during routine healthcare delivery within learning healthcare systems.

All Roads Lead to Rome: Diverse Etiologies of Tricuspid Regurgitation Create a Predictable Constellation of Right Ventricular Shape Changes.

Introduction: Myriad disorders cause right ventricular (RV) dilation and lead to tricuspid regurgitation (TR). Because the thin-walled, flexible RV is mechanically coupled to the pulmonary circulation and the left ventricular septum, it distorts with any disturbance in the cardiopulmonary system. TR, therefore, can result from pulmonary hypertension, left heart failure, or intrinsic RV dysfunction; but once it occurs, TR initiates a cycle of worsening RV volume overload, potentially progressing to right heart failure. Characteristic three-dimensional RV shape-changes from this process, and changes particular to individual TR causes, have not been defined in detail. Methods: Cardiac MRI was obtained in 6 healthy volunteers, 41 patients with ≥ moderate TR, and 31 control patients with cardiac disease without TR. The mean shape of each group was constructed using a three-dimensional statistical shape model via the particle-based shape modeling approach. Changes in shape were examined across pulmonary hypertension and congestive heart failure subgroups using principal component analysis (PCA). A logistic regression approach based on these PCA modes identified patients with TR using RV shape alone. Results: Mean RV shape in patients with TR exhibited free wall bulging, narrowing of the base, and blunting of the RV apex compared to controls (p < 0.05). Using four primary PCA modes, a logistic regression algorithm identified patients with TR correctly with 82% recall and 87% precision. In patients with pulmonary hypertension without TR, RV shape was narrower and more streamlined than in healthy volunteers. However, in RVs with TR and pulmonary hypertension, overall RV shape continued to demonstrate the free wall bulging characteristic of TR. In the subgroup of patients with congestive heart failure without TR, this intermediate state of RV muscular hypertrophy was not present. Conclusion: The multiple causes of TR examined in this study changed RV shape in similar ways. Logistic regression classification based on these shape changes reliably identified patients with TR regardless of etiology. Furthermore, pulmonary hypertension without TR had unique shape features, described here as the "well compensated" RV. These results suggest shape modeling as a promising tool for defining severity of RV disease and risk of decompensation, particularly in patients with pulmonary hypertension.

International multicenter study comparing demographics, therapy and outcomes in bleeding from Mallory Weiss tears and peptic ulcers.

Background and study aims Mallory Weiss tears (MWTs) are relatively uncommon causes of upper gastrointestinal bleeding (UGIB), and patients are generally considered at low risk of poor outcome, although data are limited. There is uncertainty about use of endoscopic therapy. We aimed to describe and compare an international cohort of patients presenting with UGIB secondary to MWT and peptic ulcer bleeding (PUB). Patients and methods From an international dataset of patients undergoing endoscopy for acute UGIB at seven hospitals, we assessed patients with MWT bleeding, including the endoscopic stigmata and endoscopic therapy applied. We compared baseline parameters, rebleeding rate, and 30-day mortality between patients with MWT and PUB. Results A total of 3648 patients presented with UGIB, 125 of whom (3.4 %) had bleeding from a MWT. Those patients were younger (61 vs 69 years, P  < 0.0001) and more likely to be men (66 % vs 53 %, P  = 0.006) compared to the patients PUB. The most common endoscopic stigmata seen in MWTs were oozing blood (26 %) or clean base (26 %). Of the patients with MWT, 53 (42 %) received endoscopic therapy. Forty-eight of them (90 %) had epinephrine injections and 25 (48 %) had through-the-scope clips. The rebleeding rate was lower in MWT patients compared with PUB patients (4.9 % vs 12 %, P  = 0.016), but mortality was similar (5.7 vs 7.0 %, P  = 0.71). Conclusions Although patients presenting with MWT were younger, with a lower rebleeding rate, their mortality was similar to that of patients with PUB. Endoscopic therapy was applied to 42 % MWT patients, with epinephrine injection as the most common modality.

Ancient DNA and deep population structure in sub-Saharan African foragers.

Multiple lines of genetic and archaeological evidence suggest that there were major demographic changes in the terminal Late Pleistocene epoch and early Holocene epoch of sub-Saharan Africa1-4. Inferences about this period are challenging to make because demographic shifts in the past 5,000 years have obscured the structures of more ancient populations3,5. Here we present genome-wide ancient DNA data for six individuals from eastern and south-central Africa spanning the past approximately 18,000 years (doubling the time depth of sub-Saharan African ancient DNA), increase the data quality for 15 previously published ancient individuals and analyse these alongside data from 13 other published ancient individuals. The ancestry of the individuals in our study area can be modelled as a geographically structured mixture of three highly divergent source populations, probably reflecting Pleistocene interactions around 80-20 thousand years ago, including deeply diverged eastern and southern African lineages, plus a previously unappreciated ubiquitous distribution of ancestry that occurs in highest proportion today in central African rainforest hunter-gatherers. Once established, this structure remained highly stable, with limited long-range gene flow. These results provide a new line of genetic evidence in support of hypotheses that have emerged from archaeological analyses but remain contested, suggesting increasing regionalization at the end of the Pleistocene epoch.

Benchmarking off-the-shelf statistical shape modeling tools in clinical applications.

Statistical shape modeling (SSM) is widely used in biology and medicine as a new generation of morphometric approaches for the quantitative analysis of anatomical shapes. Technological advancements of in vivo imaging have led to the development of open-source computational tools that automate the modeling of anatomical shapes and their population-level variability. However, little work has been done on the evaluation and validation of such tools in clinical applications that rely on morphometric quantifications(e.g., implant design and lesion screening). Here, we systematically assess the outcome of widely used, state-of-the-art SSM tools, namely ShapeWorks, Deformetrica, and SPHARM-PDM. We use both quantitative and qualitative metrics to evaluate shape models from different tools. We propose validation frameworks for anatomical landmark/measurement inference and lesion screening. We also present a lesion screening method to objectively characterize subtle abnormal shape changes with respect to learned population-level statistics of controls. Results demonstrate that SSM tools display different levels of consistencies, where ShapeWorks and Deformetrica models are more consistent compared to models from SPHARM-PDM due to the groupwise approach of estimating surface correspondences. Furthermore, ShapeWorks and Deformetrica shape models are found to capture clinically relevant population-level variability compared to SPHARM-PDM models.

Statistical Shape Modeling of Biventricular Anatomy with Shared Boundaries.

Statistical shape modeling (SSM) is a valuable and powerful tool to generate a detailed representation of complex anatomy that enables quantitative analysis and the comparison of shapes and their variations. SSM applies mathematics, statistics, and computing to parse the shape into a quantitative representation (such as correspondence points or landmarks) that will help answer various questions about the anatomical variations across the population. Complex anatomical structures have many diverse parts with varying interactions or intricate architecture. For example, the heart is a four-chambered anatomy with several shared boundaries between chambers. Coordinated and efficient contraction of the chambers of the heart is necessary to adequately perfuse end organs throughout the body. Subtle shape changes within these shared boundaries of the heart can indicate potential pathological changes that lead to uncoordinated contraction and poor end-organ perfusion. Early detection and robust quantification could provide insight into ideal treatment techniques and intervention timing. However, existing SSM approaches fall short of explicitly modeling the statistics of shared boundaries. In this paper, we present a general and flexible data-driven approach for building statistical shape models of multi-organ anatomies with shared boundaries that captures morphological and alignment changes of individual anatomies and their shared boundary surfaces throughout the population. We demonstrate the effectiveness of the proposed methods using a biventricular heart dataset by developing shape models that consistently parameterize the cardiac biventricular structure and the interventricular septum (shared boundary surface) across the population data.

Spatiotemporal Cardiac Statistical Shape Modeling: A Data-Driven Approach.

Clinical investigations of anatomy's structural changes over time could greatly benefit from population-level quantification of shape, or spatiotemporal statistic shape modeling (SSM). Such a tool enables characterizing patient organ cycles or disease progression in relation to a cohort of interest. Constructing shape models requires establishing a quantitative shape representation (e.g., corresponding landmarks). Particle-based shape modeling (PSM) is a data-driven SSM approach that captures population-level shape variations by optimizing landmark placement. However, it assumes cross-sectional study designs and hence has limited statistical power in representing shape changes over time. Existing methods for modeling spatiotemporal or longitudinal shape changes require predefined shape atlases and pre-built shape models that are typically constructed cross-sectionally. This paper proposes a data-driven approach inspired by the PSM method to learn population-level spatiotemporal shape changes directly from shape data. We introduce a novel SSM optimization scheme that produces landmarks that are in correspondence both across the population (inter-subject) and across time-series (intra-subject). We apply the proposed method to 4D cardiac data from atrial-fibrillation patients and demonstrate its efficacy in representing the dynamic change of the left atrium. Furthermore, we show that our method outperforms an image-based approach for spatiotemporal SSM with respect to a generative time-series model, the Linear Dynamical System (LDS). LDS fit using a spatiotemporal shape model optimized via our approach provides better generalization and specificity, indicating it accurately captures the underlying time-dependency.

Statistical shape modeling of multi-organ anatomies with shared boundaries.

Introduction: Statistical shape modeling (SSM) is a valuable and powerful tool to generate a detailed representation of complex anatomy that enables quantitative analysis of shapes and their variations. SSM applies mathematics, statistics, and computing to parse the shape into some quantitative representation (such as correspondence points or landmarks) which can be used to study the covariance patterns of the shapes and answer various questions about the anatomical variations across the population. Complex anatomical structures have many diverse parts with varying interactions or intricate architecture. For example, the heart is a four-chambered organ with several shared boundaries between chambers. Subtle shape changes within the shared boundaries of the heart can indicate potential pathologic changes such as right ventricular overload. Early detection and robust quantification could provide insight into ideal treatment techniques and intervention timing. However, existing SSM methods do not explicitly handle shared boundaries which aid in a better understanding of the anatomy of interest. If shared boundaries are not explicitly modeled, it restricts the capability of the shape model to identify the pathological shape changes occurring at the shared boundary. Hence, this paper presents a general and flexible data-driven approach for building statistical shape models of multi-organ anatomies with shared boundaries that explicitly model contact surfaces. Methods: This work focuses on particle-based shape modeling (PSM), a state-of-art SSM approach for building shape models by optimizing the position of correspondence particles. The proposed PSM strategy for handling shared boundaries entails (a) detecting and extracting the shared boundary surface and contour (outline of the surface mesh/isoline) of the meshes of the two organs, (b) followed by a formulation for a correspondence-based optimization algorithm to build a multi-organ anatomy statistical shape model that captures morphological and alignment changes of individual organs and their shared boundary surfaces throughout the population. Results: We demonstrate the shared boundary pipeline using a toy dataset of parameterized shapes and a clinical dataset of the biventricular heart models. The shared boundary model for the cardiac biventricular data achieves consistent parameterization of the shared surface (interventricular septum) and identifies the curvature of the interventricular septum as pathological shape differences.

Statistical shape analysis of the left atrial appendage predicts stroke in atrial fibrillation.

The shape of the left atrium (LA) and left atrial appendage (LAA) have been shown to predict stroke in patients with atrial fibrillation (AF). Prior studies rely on qualitative assessment of shape, which limits reproducibility and clinical utility. Statistical shape analysis (SSA) allows for quantitative assessment of shape. We use this method to assess the shape of the LA and LAA and predict stroke in patients with AF. From a database of AF patients who had previously undergone MRI of the LA, we identified 43 patients with AF who subsequently had an ischemic stroke. We also identified a cohort of 201 controls with AF who did not have a stroke after the MRI. We performed SSA of the LA and LAA shape to quantify the shape of these structures. We found three of the candidate LAA shape parameters to be predictive of stroke, while none of the LA shape parameters predicted stroke. When the three predictive LAA shape parameters were added to a logistic regression model that included the CHA2DS2-VASc score, the area under the ROC curve increased from 0.640 to 0.778 (p = .003). The shape of the LA and LAA can be assessed quantitatively using SSA. LAA shape predicts stroke in AF patients, while LA shape does not. Additionally, LAA shape predicts stroke independent of CHA2DS2-VASc score. SSA for assessment of LAA shape may improve stroke risk stratification and clinical decision making for AF patients.

UCT Human Skeletal Repository: Its stewardship, history, composition and educational use.

The University of Cape Town (UCT) Human Skeletal Repository began in 1913 and its composition a century later reflects the history of biological anthropology at the University, in South Africa and internationally. It consists of 1059 skeletons from archaeological (472; 44%), cadaveric (372; 36%) and forensic contexts (160; 14%). They are used for educational and research purposes to provide engaged scholarship and experiential learning for undergraduate and postgraduate students from a variety of disciplines including health professionals. The cadaveric remains help build population specific standards, forensic cases assist to address social and criminal justice, and the archaeological discoveries to preserve African culture and heritage. Overall, the repository provides a distinct contribution to knowledge locally and globally. The new management approach of the repository is presented. Ethical considerations and management policies are discussed. Stewardship of these individuals is facing several challenges and there are areas that continue to require attention. UCT is committed to address past unethical procurement of remains through engaging with the relevant interested and affected parties in restitution and repatriation.