Bubbles are known to form in the body after scuba dives, even those done well within the decompression model limits. These can sometimes trigger decompression sickness and the dive protocols should therefore aim to limit bubble formation and growth from hyperbaric decompression. Understanding these processes physiologically has been a challenge for decades and there are a number of questions still unanswered. The physics and historical background of this field of study is presented and the latest studies and current developments reviewed. Heterogeneous nucleation is shown to remain the prime candidate for bubble formation in this context. The two main theories to account for micronuclei stability are then to consider hydrophobicity of surfaces or tissue elasticity, both of which could also explain some physiological observations. Finally the modeling relevance of the bubble formation process is discussed, together with that of bubble growth as well as multiple bubble behavior.Lees verder
In spite of many modifications to decompression algorithms, the incidence of decompression sickness (DCS) in scuba divers has changed very little. The success of stage, compared to linear ascents, is well described yet theoretical changes in decompression ratios have diminished the importance of fast tissue gas tensions as critical for bubble generation. The most serious signs and symptoms of DCS involve the spinal cord, with a tissue half time of only 12.5 minutes. It is proposed that present decompression schedules do not permit sufficient gas elimination from such fast tissues, resulting in bubble formation. Further, it is hypothesized that introduction of a deep stop will significantly reduce fast tissue bubble formation and neurological DCS risk. A total of 181 dives were made to 82 fsw (25 m) by 22 volunteers. Two dives of 25 min and 20 min were made, with a 3 hr 30 min surface interval and according to 8 different ascent protocols. Ascent rates of 10, 33 or 60 fsw/min (3, 10, 18 m/min) were combined with no stops or a shallow stop at 20 fsw (6 m) or a deep stop at 50 fsw (15 m) and a shallow at 20 fsw (6 m). The highest bubbles scores (8.78/9.97), using the Spencer Scale (SS) and Extended Spencer Scale (ESS) respectively, were with the slowest ascent rate. This also showed the highest 5 min and 10 min tissue loads of 48% and 75%. The lowest bubble scores (1.79/2.50) were with an ascent rate of 33 fsw (10 m/min) and stops for 5 min at 50 fsw (15 m) and 20 fsw (6 m). This also showed the lowest 5 and 10 min tissue loads at 25% and 52% respectively. Thus, introduction of a deep stop significantly reduced Doppler detected bubbles together with tissue gas tensions in the 5 and 10 min tissues, which has implications for reducing the incidence of neurological DCS in divers.Lees verder
Venous gas emboli (VGE) are often quantified as a marker of decompression stress on echocardiograms. Bubble-counting has been proposed as an easy to learn method, but remains time-consuming, rendering large dataset analysis impractical. Computer automation of VGE counting following this method has therefore been suggested as a means to eliminate rater bias and save time. A necessary step for this automation relies on the selection of a frame during late ventricular diastole (LVD) for each cardiac cycle of the recording. Since electrocardiograms (ECG) are not always recorded in field experiments, here we propose a fully automated method for LVD frame selection based on regional intensity minimization.Lees verder
2D echocardiography which is the golden standard in clinics becomes the new trend of analysis in diving via its high advantages in portability for diagnosis. By the way, the major weakness of this system is non-integrated analysis platform for bubble recognition. In this study, we developed a full automatic method to recognize bubbles in videos. Gabor Wavelet based neural networks are commonly used in face recognition and biometrics. We adopted a similar approach to overcome recognition problem by training our system through real bubble morphologies. Our method does not require a segmentation step which is almost crucial in several studies. Our correct detection rate varies between 82.7-94.3%. After the detection, we classified our findings on ventricles and atria using fuzzy k-means algorithm. Bubbles are clustered in three different subjects with 84.3-93.7% accuracy rates. We suggest that this routine would be useful in longitudinal analysis and subjects with congenital risk factorsLees verder
Nutritional antioxidants have been proposed as an expedient strategy to counter the potentially deleterious effects of scuba diving on endothelial function, flow-mediated dilation (FMD) and heart function...Lees verder
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