1,721,077 research outputs found
Enabling health, independence and wellbeing for patients with bipolar disorder through Personalised Ambient Monitoring
No full textThis thesis describes the role of mathematical modelling in the evaluation of an innovative automated system of wearable and environmental sensors to monitor the activity patterns of patients with Bipolar Disorder (BD). BD is a chronic and recurrent mental disorder associated with severe episodes of mania and depression, interspersed with periods of remission. Early detection of transitions between the normal, manic and depressed stages is crucial for effective self-management and treatment. Personalised Ambient Monitoring (PAM) is an EPSRC-funded multidisciplinary project involving biomedical engineers, computer scientists and operational researchers. The broad aim of PAM is to build and test a network of sensors (chosen by the patient) to collect and analyse daily activity data in order to identify an ‘activity signature’ for that individual in various health states. The hypothesis is that small but potentially significant changes in this activity pattern can then be automatically detected and the patient alerted, enabling him/her to take appropriate action. The research presented in this thesis involves the development and use of a Monte Carlo simulation model to evaluate the potential of PAM without the need for a costly and time-consuming clinical trial. A unique and novel disease state transition model for bipolar disorder is developed, using data from the clinical literature. This model is then used stochastically to test many different scenarios, for example the removal or technical failure of a sensor, or the limited availability of various types of data, for various simulated patient types and a wide range of assumptions and conditions. The feasibility of obtaining sufficient information to derive clinically useful information from a limited set of sensors is analysed statistically. The minimum best set of sensors suitable to detect both aspects of the disorder is identified, and the performance of the PAM system evaluated for a range of personalised choices of sensor
The Effect of Heart Rate on Normal and Abnormal QRS Voltage
No full textScalar electrocardiography, despite its shortcomings, is still the best single clinical tool for the detection of enlargement of cardiac chambers, particularly the left ventricle. The electrocardiographic diagnosis of left ventricular enlargement is primarily based upon increase in the QRS voltage in conventional limb and precordial leads. | Admittedly, the usage of increase in voltage for diagnosis of left ventricular enlargement leads to both, false positive and false negative results. The source of error lies in the fact that several other factors besides increase in left ventricular mass can influence the QRS voltage. | It has been recognized that body build, the position of chest electrodes, the amount and type of the electrocardiographic paste, skin resistance, respiratory motion, age of the patient and many other factors can modify voltage. The effect of heart rate has been noted but not fully investigated particularly in regard to its influence upon the voltage criteria commonly used in the diagnosis of left ventricular enlargement.ProQuest Traditional Publishing Optio
Identification of pathogenic gene mutations in <i>LMNA</i> and <i>MYBPC3</i> that alter RNA splicing
No full textSignificance
Sequence variants that create or eliminate splice sites are often clinically classified as variants of unknown significance (VUS) due to imperfect understanding of RNA splice signals and cumbersome functional assays. In autosomal dominant disorders caused by haploinsufficiency, variants that alter normal splicing of one allele are pathogenic. We developed enhanced computational tools to prioritize potential splice-altering VUS and used a minigene assay to functionally confirm splice-altering sequence variants. In studying all reported variants across
LMNA
and
MYBPC3
, two known heart disease genes, we demonstrate that ∼5% of VUS from affected patients alter splicing and are undetected disease-causing variants. This strategy improves clinical detection of pathogenic variants and should be broadly relevant to other human disorders that are caused by haploinsufficiency.
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