Vascular imaging in diabetes.

Diabetes is a global epidemic affecting individuals of all socioeconomic backgrounds. Despite intensive efforts, morbidity and mortality secondary to the micro- and macrovascular complications remain unacceptably high. As a result, the use of imaging modalities to determine the underlying pathophysiology, early onset of complications, and disease progression has become an integral component of the management of such individuals. Echocardiography, stress echocardiography, and nuclear imaging have been the mainstay of noninvasive cardiovascular imaging tools to detect myocardial ischemia, but newer modalities such as cardiac MRI, cardiac CT, and PET imaging provide incremental information not available with standard imaging. While vascular imaging to detect cerebrovascular and peripheral arterial disease non-invasively has traditionally used ultrasound, CT- and MRI-based techniques are increasingly being employed. In this review, we will provide an outline of recent studies utilizing non-invasive imaging techniques to assist in disease diagnosis as well as monitoring disease progression. In addition, we will review the evidence for newer modalities such as MR spectroscopy, 3D intravascular ultrasound, and optical coherence tomography that provide exquisite detail of metabolic function and coronary anatomy not available with standard imaging, but that have not yet become mainstream.

NMRES: an artificial intelligence expert system for quantification of cardiac metabolites from 31phosphorus nuclear magnetic resonance spectroscopy.

The application of high-resolution 31Phosphorus Nuclear Magnetic Resonance (31P NMR) Spectroscopy in biology and medicine has provided new insights into biochemical processes and also a unique assessment of metabolites. However, accurate quantification of biological NMR spectra is frequently complicated by: (a) non-Lorentzian form of peak lineshapes, (b) contamination of peak signals by neighboring peaks, (c) presence of broad resonances, (d) low signal-to-noise ratios, and (e) poorly defined sloping baselines. Our objectives were to develop an expert system that captures and formalizes 31P NMR spectroscopists' expert knowledge, and to provide a reliable, efficient, and automated system for the interpretation of biological spectra. The NMR Expert System (NMRES) was written in the C and OPS5 programming languages and implemented on a Unix-based (Ultrix) mainframe system with XWindows bit-map graphics display. Expert knowledge was acquired from NMR spectroscopists and represented as production rules in the knowledge base. A heuristic weights method was employed to determine the confidence levels of potential peaks. Statistical and numerical methods were used to facilitate processing decisions. NMR spectra obtained from studies of ischemic neonatal and immature hearts were used to assess the performance of the expert system. The expert system performed signal extraction, noise treatment, resonance assignment, intracellular pH determination, and metabolite intensity quantitation in about 10 s per 4 KB (kilobyte) spectrum. The peak identification success rate was 98.2%. Peak areas and pH estimated by the expert system compared favorably with those determined by human experts. We conclude that the expert system has provided a framework for reliable and efficient quantification of complex biological 31P NMR spectra.

"Functioning" from the perspective of production supervisors.

Ethnographic research at two work activity centers revealed the hidden connotations of "functioning" in production supervisors' discussions of high functioning and lower functioning clients. Teachers at the centers defined functioning in narrowly academic terms, whereas supervisors used the term to connote a whole set of mental, physical, and moral traits related to job performance. Implications for communication among staff members and for treatment of clients were considered.