Between the local and the state : practices and discourses of identity among the Kadazan of Sabah (East Malaysia)

Abstract This thesis investigates the effects of the nation-building agenda carried out by the Malaysian state on the sense of collective belonging of the Kadazan people of the Bornean State of Sabah. The thesis includes a reconstruction of the formation of the two most important forms of collective identification, the nation and the ethnic group, and the analysis of the way in which Kadazan villagers identify themselves in relation to discourses circulating in various media and the practices in which they get involved in their everyday life. Kadazan villagers consistently show a rejection of the state propaganda and a general unwillingness to identify themselves as members of the Malaysian nation, which I attribute to their marginal position within the Malaysian state. They more often identify themselves as members of their ethnic group or village, collective forms of identification that seem to allow for a higher degree of participation in their definition than the national one. The empirical analysis of the everyday self-identification in relation to practices and discourses shows a complex picture, as Kadazan villagers differently situate themselves as Malaysian, Kadazan, Sabahan and members of their village in different occasions and contexts. One of the explanations of this fact lies in the ambiguous character of Malaysian nation-building, promoting unity while at the same time treating citizens differently depending on their ethnic and religious background. The official discourse and practice of ethnic and religious differentiation has been deeply internalised by the Kadazan and has become a primary reason for their opposition to the state, as they feel treated as second-class citizens. Another explanation for the development of a sense of belonging to various collective forms of identification among the Kadazan rests in the fact that their recent history has made these significant as expression of different sets of shared lived experiences, providing the basis for the development of senses of commonality with members of the national, sub-national, ethnic and village communities at the same time.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Gender Related Differences in the Clinical Presentation of Hypertrophic Cardiomyopathy-An Analysis from the SILICOFCM Database

Background and Objectives: Hypertrophic cardiomyopathy (HCM) is the most common inherited cardiac disease that affects approximately 1 in 500 people. Due to an incomplete disease penetrance associated with numerous factors, HCM is not manifested in all carriers of genetic mutation. Although about two-thirds of patients are male, it seems that female gender is associated with more severe disease phenotype and worse prognosis. The objective of this study was to evaluate the gender related differences in HCM presentation. Materials and Methods: This study was conducted as a part of the international multidisciplinary SILICOFCM project. Clinical information, laboratory analyses, electrocardiography, echocardiography, and genetic testing data were collected for 362 HCM patients from four clinical centers (Florence, Newcastle, Novi Sad, and Regensburg). There were 33% female patients, and 67% male patients. Results: Female patients were older than males (64.5 vs. 53.5 years, p < 0.0005). The male predominance was present across all age groups until the age of 70, when gender distribution became comparable. Females had higher number of symptomatic individuals then males (69% vs. 52%, p = 0.003), most frequently complaining of dyspnea (50% vs. 30%), followed by chest pain (30% vs. 17%), fatigue (26% vs. 13%), palpitations (22% vs. 13%), and syncope (13% vs. 8%). The most common rhythm disorder was atrial fibrillation which was present in a similar number of females and males (19% vs. 13%, p = 0.218). Levels of N-terminal pro-brain natriuretic peptide were comparable between the genders (571 vs. 794 ng/L, p = 0.244). Echocardiography showed similar thickness of interventricular septum (18 vs. 16 mm, p = 0.121) and posterolateral wall (13 vs. 12 mm, p = 0.656), however, females had a lower number of systolic anterior motion (8% vs. 16%, p = 0.020) and other mitral valve abnormalities. Conclusions: Female patients are underrepresented but seem to have a more pronounced clinical presentation of HCM. Therefore, establishing gender specific diagnostic criteria for HCM should be considered

Genetic determinants of clinical phenotype in hypertrophic cardiomyopathy

BackgroundHypertrophic cardiomyopathy (HCM) is the most common inherited cardiovascular disease that affects approximately one in 500 people. HCM is a recognized genetic disorder most often caused by mutations involving myosin-binding protein C (MYBPC3) and beta -myosin heavy chain (MYH7) which are responsible for approximately three-quarters of the identified mutations.MethodsAs a part of the international multidisciplinary SILICOFCM project (www.silicofcm.eu) the present study evaluated the association between underlying genetic mutations and clinical phenotype in patients with HCM. Only patients with confirmed single pathogenic mutations in either MYBPC3 or MYH7 genes were included in the study and divided into two groups accordingly. The MYBPC3 group was comprised of 48 patients (76%), while the MYH7 group included 15 patients (24%). Each patient underwent clinical examination and echocardiography.ResultsThe most prevalent symptom in patients with MYBPC3 was dyspnea (44%), whereas in patients with MYH7 it was palpitations (33%). The MYBPC3 group had a significantly higher number of patients with a positive family history of HCM (46% vs. 7%; p=0.014). There was a numerically higher prevalence of atrial fibrillation in the MYH7 group (60% vs. 35%, p=0.085). Laboratory analyses revealed normal levels of creatinine (85.518.3 vs. 81.3 +/- 16.4 mu mol/l; p=0.487) and blood urea nitrogen (10.2 +/- 15.6 vs. 6.9 +/- 3.9 mmol/l; p=0.472) which were similar in both groups. The systolic anterior motion presence was significantly more frequent in patients carrying MYH7 mutation (33% vs. 10%; p=0.025), as well as mitral leaflet abnormalities (40% vs. 19%; p=0.039). Calcifications of mitral annulus were registered only in MYH7 patients (20% vs. 0%; p=0.001). The difference in diastolic function, i.e. E/e ' ratio between the two groups was also noted (MYBPC3 8.8 +/- 3.3, MYH7 13.9 +/- 6.9, p=0.079).Conclusions Major findings of the present study corroborate the notion that MYH7 gene mutation patients are presented with more pronounced disease severity than those with MYBPC3

A machine learning-based risk stratification model for ventricular tachycardia and heart failure in hypertrophic cardiomyopathy

Background: Machine learning (ML) and artificial intelligence are emerging as important components of precision medicine that enhance diagnosis and risk stratification. Risk stratification tools for hypertrophic cardiomyopathy (HCM) exist, but they are based on traditional statistical methods. The aim was to develop a novel machine learning risk stratification tool for the prediction of 5-year risk in HCM. The goal was to determine if its predictive accuracy is higher than the accuracy of the state-of-the-art tools. Method: Data from a total of 2302 patients were used. The data were comprised of demographic characteristics, genetic data, clinical investigations, medications, and disease-related events. Four classification models were applied to model the risk level, and their decisions were explained using the SHAP (SHapley Additive exPlanations) method. Unwanted cardiac events were defined as sustained ventricular tachycardia occurrence (VT), heart failure (HF), ICD activation, sudden cardiac death (SCD), cardiac death, and all-cause death. Results: The proposed machine learning approach outperformed the similar existing risk-stratification models for SCD, cardiac death, and all-cause death risk-stratification: it achieved higher AUC by 17%, 9%, and 1%, respectively. The boosted trees achieved the best performing AUC of 0.82. The resulting model most accurately predicts VT, HF, and ICD with AUCs of 0.90, 0.88, and 0.87, respectively. Conclusions: The proposed risk-stratification model demonstrates high accuracy in predicting events in patients with hypertrophic cardiomyopathy. The use of a machine-learning risk stratification model may improve patient management, clinical practice, and outcomes in general

Design of the SILICOFCM study: Effect of sacubitril/valsartan vs lifestyle intervention on functional capacity in patients with hypertrophic cardiomyopathy

Background Hypertrophic cardiomyopathy (HCM) is the most common genetic cardiovascular disease with a broad spectrum of disease severity. HCM ranges from a benign course to a progressive disorder characterized by angina, heart failure, malignant arrhythmia, syncope, or sudden cardiac death. So far, no medical treatment has reliably shown to halt or reverse progression of HCM or to alleviate its symptoms. While the angiotensin receptor neprilysin inhibitor sacubitril/valsartan has shown to reduce mortality and hospitalization in heart failure with reduced ejection fraction, data on its effect on HCM are sparse. Hypothesis A 4-month pharmacological (sacubitril/valsartan) or lifestyle intervention will significantly improve exercise tolerance (ie, peak oxygen consumption) in patients with nonobstructive HCM compared to the optimal standard therapy (control group). Methods SILICOFCM is a prospective, multicenter, open-label, randomized, controlled, three-arm clinical trial (NCT03832660) that will recruit 240 adult patients with a confirmed diagnosis of nonobstructive HCM. Eligible patients are randomized to sacubitril/valsartan, lifestyle intervention (physical activity and dietary supplementation with inorganic nitrate), or optimal standard therapy alone (control group). The primary endpoint is the change in functional capacity (ie, peak oxygen consumption). Secondary endpoints include: (a) Change in cardiac structure and function as assessed by transthoracic echocardiography and cardiac magnetic resonance (MRI imaging), (b) change in biomarkers (ie, CK, CKMB, and NT-proBNP), (c) physical activity, and (d) quality of life. Results Until December 2019, a total of 41 patients were recruited into the ongoing SILICOFCM study and were allocated to the study groups and the control group. There was no significant difference in key baseline characteristics between the three groups. Conclusion The SILICOFCM study will provide novel evidence about the effect of sacubitril/valsartan or lifestyle intervention on functional capacity, clinical phenotype, injury and stretch activation markers, physical activity, and quality of life in patients with nonobstructive HCM

A computational pipeline for data augmentation towards the improvement of disease classification and risk stratification models: A case study in two clinical domains

Virtual population generation is an emerging field in data science with numerous applications in healthcare towards the augmentation of clinical research databases with significant lack of population size. However, the impact of data augmentation on the development of AI (artificial intelligence) models to address clinical unmet needs has not yet been investigated. In this work, we assess whether the aggregation of real with virtual patient data can improve the performance of the existing risk stratification and disease classification models in two rare clinical domains, namely the primary Sjo &amp; uml;gren’s Syndrome (pSS) and the hypertrophic cardiomyopathy (HCM), for the first time in the literature. To do so, multivariate approaches, such as, the multivariate normal distribution (MVND), and straightforward ones, such as, the Bayesian networks, the artificial neural networks (ANNs), and the tree ensembles are compared against their performance towards the generation of high-quality virtual data. Both boosting and bagging algorithms, such as, the Gradient boosting trees (XGBoost), the AdaBoost and the Random Forests (RFs) were trained on the augmented data to evaluate the performance improvement for lymphoma classification and HCM risk stratification. Our results revealed the favorable performance of the tree ensemble generators, in both domains, yielding virtual data with goodness-of-fit 0.021 and KL-divergence 0.029 in pSS and 0.029, 0.027 in HCM, respectively. The application of the XGBoost on the augmented data revealed an increase by 10.9% in accuracy, 10.7% in sensitivity, 11.5% in specificity for lymphoma classification and 16.1% in accuracy, 16.9% in sensitivity, 13.7% in specificity in HCM risk stratification