Effect of hyaluronidase on albumin diffusion in lung interstitium.

Albumin diffusion measured in an isolated segment of rabbit lung interstitium with a radioactive tracer ((125)I-albumin) technique was independent of albumin concentration and similar to the free diffusion of albumin in water (Qiu et al, 1998. J Appl Physiol 85: 575-583). We studied the effect of hyaluronidase on the diffusion of albumin. Isolated rabbit lungs were inflated with silicon rubber by way of airways and blood vessels, and two chambers were bonded to the sides of a approximately 0.5-cm thick slab enclosing a vessel with an interstitial cuff. One chamber was filled with 2 g/dl albumin solution containing (125)I-albumin and 0.02 g/dl hyaluronidase. Unbound (125)I was removed from the tracer by dialysis before use. The other chamber filled with Ringer's solution was placed within a NaI(Tl) scintillation detector. Diffusion of tracer was measured continuously for 120 h. Albumin diffusion coefficient (D) and interstitial area (A) were obtained by fitting the tracer-time curve with the theoretical solution of the equation describing one-dimension diffusion of a solute across a membrane. D averaged 5.2 x 10(-7) cm(2)/s for albumin diffusion with hyaluronidase, 20% less than that measured previously without hyaluronidase. Hyaluronidase had no effect on A. Results indicated an interaction between albumin and interstitial hyaluronan that was the opposite of the steric effect on albumin excluded volume measured in solution.

Simulating patients with Parallel Health State Networks.

The American Board of Family Practice is developing a computer-based recertification process to generate patient simulations from a knowledge base. Simulated patients require a stochastically generated history and response to treatment, suggesting a Monte Carlo-like patient generation process. Knowledge acquisition experiments revealed that description of a patient's overall health as a node in a Monte Carlo model was difficult for domain experts to use, severely limited knowledge reusability, and created a plethora of awkwardly defined health states. We explored a model in which patients traverse several parallel health state networks simultaneously, so that overall health is a vector describing the current nodes from every Parallel Network. This model has a reasonable biological basis, more easily defined data, and greatly improved reuse potential, at the cost of more complex simulation algorithms. Experiments using osteoarthritis stages, weight classification, and absence or presence of gastric ulcers as three Parallel Networks demonstrate the feasibility of this approach to simulating patients.

A formal model of family medicine.

BACKGROUND: The American Board of Family Practice is developing a computer-based recertification process. An optimal implementation requires a formal model of family medicine, which will become the basis for a knowledge base. DESIGN: The proposed model of family medicine contains six entities: Population, Record, Agents of Change, Health States, Findings, and Courses of Action. The model illustrates 15 important relations between entities. For instance: Health States Lead to Health States, and Findings Associate with Health States. These two relations describe natural history, manifestations of disease, and the effects of medical interventions and risk factors. Because time is such an important aspect of primary care, nearly all numeric data are represented as graphs of possible values over time, called Patterns, which include details about periodicity. Patterns and other aspects of the model provide a means of describing covariance between observations, such as the influence of height on weight. RESULTS: The model reflects many family practice activities and suggests some formal descriptions of family practice. For instance, diagnostic activities focus largely on classifying early or short segments of Patterns in Findings. Most medical interventions attempt to alter either the probability distributions in a Lead-to relation or the impact of a Finding. CONCLUSION: The proposed model of family medicine could find uses in many applications, including computer-based tests, medical records, reference systems, and decision support tools.