Negative halothane-caffeine contracture test in mdx (dystrophin-deficient) mice.

The genetics of malignant hyperthermia (MH) are ill-understood; however, the association of Duchenne muscular dystrophy (DMD) with MH is well known. A deficiency of dystrophin is common to both the DMD and mdx mouse, an animal model for DMD. Using muscle contracture tests for MH, we have shown that in the mdx mouse there is no MH susceptibility, suggesting the lack of a direct role of the dystrophin in the development of MH syndrome.

The dystrophin connection--ATP?

Clinical evidence is presented supporting the hypothesis that the metabolic abnormality in the dystrophin-defective muscular dystrophies (DMD and BMD) involves the ATP pathway. Objective laboratory data show corrective trends in the abnormal values of parameters relating to creatine and calcium metabolism (ATP) by use of glucagon-stimulated c-AMP and by use of synthetically produced adenylosuccinic acid (ASA). Disease accelerating mechanisms as suggested by analysis of the clinical features, and the therapeutic potential of ASA are discussed.

'Oil globules' in Duchenne muscular dystrophy--history, demonstration, and metabolic significance.

Duchenne, Meryon, Wernich, Clarke, Down and other dystrophy pioneers recognized, illustrated and/or described Duchenne muscular dystrophy skeletal muscle's 'oil globules'. Rarely mentioned or acknowledged since the introduction in 1869 by Klebs of paraffin embedding and modern histological technique (in which tissue lipids are eliminated) this microscopical marker of metabolic dysfunction is utilized to find its metabolic site of origin in the living cell, to identify the disease's major dysfunctioning metabolic pathway, and finally to determine its dystrophin connection which accounts for the primary metabolic malfunction and the clinical manifestations of disease. This paper presents a working hypothesis developed through a long-term empirical study and suggests a practical method of therapy.

Medical diagnostic systems: a case for neural networks.

Recent advances in computer technology offer to the medical profession specialized tools for gathering medical data, processing power, as well as fast storing and retrieving capabilities. Artificial intelligence (AI), an emerging field of computer science is studying the issues of human problem solving and decision making. Furthermore, rule-based systems and knowledge-based systems that are other fields of AI have been adopted by many scientists in an effort to develop intelligent medical diagnostic systems. In this study artificial neural networks (ANN) are introduced as a tool for building an intelligent diagnostic system; the system does not attempt to replace the physician from being the decision maker but to enhance ones facilities for reaching a correct decision. An integrated diagnostic system for assessing certain neuromuscular disorders is used in this study as an example for demonstrating the proposed methodology. The diagnostic system is composed of modules that independently provide numerical data to the system from the clinical examination of a patient, and from various laboratory tests that are performed. The examination procedure has been standardized by developing protocols for each specialized area, in cooperation with experts in the area. At the conclusion of the clinical examination and laboratory tests, data in the form of a numerical vector represents a medical examination snapshot of the subject. Artificial neural network (ANN) models were developed using the unsupervised self-organizing feature maps algorithm. Data from 71 subjects were collected. The ANN models were trained with the data from 41 subjects, and tested with the data from the remaining 30 subjects. Two sets of models were developed; those trained with the data from only the clinical examinations; and those trained by combining the clinical and the laboratory test data. The diagnostic yield that was obtained for the unknown cases is in the region of 73 to 93% for the models trained with only the clinical data, and in the region of 73 to 100% for those trained by combining both the clinical and laboratory data. The pictorial representation of the diagnostic models through the self organized two dimensional feature maps provide the physician with a friendly human-computer interface and a comprehensive tool that can be used for further observations, for example in monitoring disease progression of a subject.