SERÉ S, LUIS, JORDI LOPEZ-AYERBE, RAMÓ N COLL, ORIOL RODRIGUEZ, JUAN VILA, XAVIER FORMIGUERA, ANTONIO ALASTRUE, MIGUEL RULL, AND VICENTE VALLE. Increased exercise capacity after surgically induced weight loss in morbid obesity. Obesity. 2006;14:273-279. Objective: To investigate the effects of surgically induced weight loss on exercise capacity in patients with morbid obesity (MO). Research Methods and Procedures:A prospective 1-year follow-up study was carried out, with patients being their own controls. A symptom-limited cardiopulmonary exercise stress test was performed in 31 MO patients (BMI Ͼ 40 kg/m 2 ) before and 1 year after undergoing bariatric surgery. Results: At 1 year after surgery, weight was reduced from 146 Ϯ 33 to 95 Ϯ 19 kg (p Ͻ 0.001), and BMI went from 51 Ϯ 4 to 33 Ϯ 6 kg/m 2 (p Ͻ 0.001). After weight loss, obese patients performed each workload with lower oxygen consumption, heart rate, systolic arterial pressure, and ventilatory volume (p Ͻ 0.001). This reduced energy expenditure allowed them to increase the duration of their effort test from 13.8 Ϯ 3.8 to 21 Ϯ 4.2 minutes (p Ͻ 0.001). Upon finishing the exercise, MO patients before surgery were able to reach only 83% of their age-predicted maximal heart rate, and their respiratory exchange ratio was 0.87 Ϯ 0.06. After weight loss, those values were 90% and 1 Ϯ 0.08, respectively (p Ͻ 0.01). When we compared the peak O 2 pulse corrected by fat free mass before and after surgery, no significant differences between the groups were found. Discussion: After surgically induced weight loss, MO patients markedly improved their exercise capacity. This is due to the fact that they were able to perform the external work with lower energy expenditure and also to increase cardiovascular stress, optimizing the use of cardiac reserve. There were no differences in cardiac function before and after surgery.

Vessel plaque assessment by analysis of intravascular ultrasound sequences is a useful tool for cardiac disease diagnosis and intervention. Manual detection of luminal (inner) and media-adventitia (external) vessel borders is the main activity of physicians in the process of lumen narrowing (plaque) quantification. Difficult definition of vessel border descriptors, as well as, shades, artifacts, and blurred signal response due to ultrasound physical properties trouble automated adventitia segmentation. In order to efficiently approach such a complex problem, we propose blending advanced anisotropic filtering operators and statistical classification techniques into a vessel border modelling strategy. Our systematic statistical analysis shows that the reported adventitia detection achieves an accuracy in the range of interobserver variability regardless of plaque nature, vessel geometry, and incomplete vessel borders.

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Coronary plaque rupture is one of the principal causes of sudden death in western societies. Reliable diagnostic of the different plaque types are of great interest for the medical community the predicting their evolution and applying an effective treatment. To achieve this, a tissue classification must be performed. Intravascular Ultrasound (IVUS) represents a technique to explore the vessel walls and to observe its histological properties. In this paper, a method to reconstruct IVUS images from the raw Radio Frequency (RF) data coming from ultrasound catheter is proposed. This framework offers a normalization scheme to compare accurately different patient studies. The automatic tissue classification is based on texture analysis and Adapting Boosting (Adaboost) learning technique combined with Error Correcting Output Codes (ECOC). In this study, 9 in-vivo cases are reconstructed with 7 different parameter set. This method improves the classification rate based on images, yielding a 91% of well-detected tissue using the best parameter set. It also reduces the inter-patient variability compared with the analysis of DICOM images, which are obtained from the commercial equipment.