SUMMARY Clinical, electrocardiographic, and scintigraphic data were reviewed from 32 patients (18 men and 14 women) who had syndrome X (chest pain, evidence of ischaemia, and normal coronary arteries without coronary vasospasm). The mean (SD) resting left ventricular ejection fraction, determined by first pass radionuclide angiography was 62-6 (9-2)% and was > 50% in all subjects. There was no significant difference between men and women. On exercise, left ventricular ejection fraction decreased significantly to 57-4 (13-0)%. In 17 of 32 subjects there was a fall in left ventricular ejection fraction of > 5%, and regional wall motion abnormalities developed in 12 subjects. The fall in left ventricular ejection fraction on exercise was significant in women (from 61-9 (8-5)% at rest to 54 0 (9-8)% on exercise) but not in men (from 63-2 (9-8)% at rest to 60 0 (14 8)% on exercise). Exercise left ventricular ejection fraction fell by > 5 % in 10 (71 %) of 14 women and in seven (39%) of 18 men. Dyskinetic segments developed in eight (57%) of 14 women and only four (22%) of men. Exercise duration in women was significantly shorter than in men (4-1 (1 5) vs 6 6 (2-1) minutes) and was the only one of several clinical and scintigraphic variables that correlated with the change in left ventricular ejection fraction on exercise.In this selected group of subjects with chest pain and angiographically normal coronary arteries, exercise induced left ventricular dysfunction, as shown by a fall in ejection fraction or the development of regional abnormalities, is a common finding. These are more likely to occur in women than men and are associated with a lower exercise capacity. The data suggest that the sex of the patient is important in the interpretation of the non-invasive evaluation of subjects suspected of having syndrome X.Most of the clinical manifestations of ischaemic heart disease are due to fixed or dynamic obstruction of epicardial coronary arteries. Coronary angiography remains the gold standard by which normality or abnormality of the coronary circulation is judged and its importance in clinical decision making and prognosis cannot be denied.' None the less, there is a subset of patients in whom there is evidence of ischaemia in the absence of abnormal coronary arteriograms or evidence of coronary vasospasm. This clinical group has been labelled as syndrome X.2 These patients have func- Accepted for publication 24 November 1986 tional rather than anatomical ischaemia and are particularly difficult to manage. They have been investigated by various invasive and non-invasive methods.This study assessed the frequency of exercise induced left ventricular dysfunction in a group of these patients and examined the differences in response between male and female patients.Patients and methods STUDY POPULATIONThe records of 174 patients were reviewed. They had been referred to the Nuclear Cardiology Laboratory in the years 1982-86 and had undergone (within one year) both coronary arteriography for a primary diagnosis of po...
Autoencoders are the ideal analysis tool for the LHC, as they represent its main goal of finding physics beyond the Standard Model. The key challenge is that out-of-distribution anomaly searches based on the compressibility of features do not apply to the LHC, while existing density-based searches lack performance. We present the first autoencoder which identifies anomalous jets symmetrically in the directions of higher and lower complexity. The normalized autoencoder combines a standard bottleneck architecture with a well-defined probabilistic description. It works better than all available autoencoders for top vs QCD jets and reliably identifies different dark-jet signals.
We develop a self-supervised method for density-based anomaly detection using contrastive learning, and test it using event-level anomaly data from CMS ADC2021. The Anomaly-CLR technique is data-driven and uses augmentations of the background data to mimic non-Standard-Model events in a model-agnostic way. It uses a permutation-invariant Transformer Encoder architecture to map the objects measured in a collider event to the representation space, where the data augmentations define a representation space which is sensitive to potential anomalous features. An AutoEncoder trained on background representations then computes anomaly scores for a variety of signals in the representation space. With AnomalyCLR we find significant improvements on performance metrics for all signals when compared to the raw data baseline.
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