Smooth muscle of the bladder in the normal and the diseased state: pathophysiology, diagnosis and treatment

The smooth muscle of the normal bladder wall must have some specific properties. It must be very compliant and able to reorganise itself during filling and emptying to accommodate the change in volume without generating any intravesical pressure, but whilst maintaining the normal shape of the bladder. It must be capable of synchronous activation to generate intravesical pressure at any length to allow voiding. The cells achieve this through spontaneous electrical activity combined with poor electrical coupling between cells, and a dense excitatory innervation. In the diseased state, alterations of the smooth muscle may lead to failure to store or failure to empty properly. The diseased states discussed are bladder instability and diabetic neuropathy. Bladder instability is characterised urodynamically by uninhibitable rises in pressure during filling, and is seen idiopathically and in association with bladder outflow obstruction and neuropathy. In diabetic neuropathy, many of the smooth muscle changes are a consequence of diuresis, but there is evidence for alterations in the sensory arm of the micturition reflex. In the unstable bladder, additional alterations of the smooth muscle are seen, which are probably caused by the patchy denervation that occurs. The causes of this denervation are not fully established. Nonsurgical treatment of instability is not yet satisfactory; neuromodulation has some promise, but is expensive, and the mechanisms poorly understood. Pharmacological treatment is largely through muscarinic receptor blockade. Drugs to reduce the excitability of the smooth muscle are being sought, since they may represent a better pharmacological option.     

Development of a recurrent Sigma-Pi neural network rainfall forecasting system in Hong Kong

At the moment, weather forecasting is still an art — the experience and intuition of forecasters play a significant role in determining the quality of forecasting. This paper describes the development of a new approach to rainfall forecasting using neural networks. It deals with the extraction of information from radar images and an evaluation of past rain gauge records to provide shortterm rainfall forecasting. All of the meteorological data were provided by the Royal Observatory of Hong Kong (ROHK). Preprocessing procedures were essential for this neural network rainfall forecasting. The forecast of the rainfall was performed every half an hour so that a storm warning signal can be delivered to the public in advance. The network architecture is based on a recurrent Sigma-Pi network. The results are very promising, and this neural-based rainfall forecasting system is capable of providing a rain storm warning signal to the Hong Kong public one hour ahead.     

Chronic wounds

Skin ulcers are the most common chronic wounds. Current management principles and theories of causation of the most common ulcers--pressure, diabetic, and venous--are described. Issues related to occlusive dressings, compression dressings, topical antimicrobials, debridement, growth factors, grafting, and bioengineered tissue therapy are discussed. Special emphasis is placed on regulatory concerns.     

CMV colitis masquerading as colon cancer--an unusual presentation of acquired immunodeficiency syndrome

To determine whether there is any correlation between sudden decrease in barometric pressure and onset of labor, a non-experimental, retrospective study at a 948-bed tertiary care hospital was done. Pregnant patients of 36 weeks gestation or more who presented with spontaneous onset of labor during the 48 hours surrounding the 12 occurrences of significant drop in barometric pressure in 1992 were included in the study. Significantly more occurrences of onset of labor were identified in the 24 hours after a drop in barometric pressure than were identified in the 24 hours prior to the drop in barometric pressure (P < 0.05). Therefore, the overall number of labor onsets increased in the 24 hours following a significant drop in barometric pressure.     

Prevalence of multiple cardiovascular disease risk factors among women in the United States, 1992 and 1995: the Behavioral Risk Factor Surveillance System

We sought to examine the prevalence of self-reported multiple cardiovascular disease (CVD) risk factors (hypertension, high blood cholesterol, diabetes, overweight, and current smoking) among women in 1992 and 1995 in the United States using data from the Behavioral Risk Factor Surveillance System. In 1992, 37.5%, 34.4%, and 28.1% of women had zero, one, and two or more of the five risk factors, respectively. In 1995, the respective estimates were 35.5%, 34.3%, and 30%. In both years, the prevalence of two or more risk factors increased with age, decreased with educational level, was higher among black women (lowest among Hispanic women and women of other ethnic groups), and higher among women reporting cost as a barrier to healthcare. The percentage of women with two or more risk factors was higher in 1995 than in 1992 for 35 of 48 states, being statistically significant for 7 states. The percentage of women with at least two risk factors was not significantly lower in 1995 than in 1992 for any state. A higher percentage of women reported having multiple CVD risk factors in 1995 compared with 1992. A multifactorial approach to primary prevention and risk factor reduction should be encouraged to help reduce the prevalence and burden of CVD among women.     

Power Distribution Fault Cause Identification With Imbalanced Data Using the Data Mining-Based Fuzzy Classification E-Algorithm

Power distribution systems have been significantly affected by many outage-causing events. Good fault cause identification can help expedite the restoration procedure and improve the system reliability. However, the data imbalance issue in many real-world data sets often degrades the fault cause identification performance. In this paper, the E-algorithm, which is extended from the fuzzy classification algorithm by Ishibuchi to alleviate the effect of imbalanced data constitution, is applied to Duke Energy outage data for distribution fault cause identification. Three major outage causes (tree, animal, and lightning) are used as prototypes. The performance of E-algorithm on real-world imbalanced data is compared with artificial neural network. The results show that the E-algorithm can greatly improve the performance when the data are imbalanced