Surgical management of retro-rectal tumors in the adult.

Retrorectal tumors (RRT), whether benign or malignant in nature, are rare in adults and often asymptomatic. While diagnosis is based on clinical findings, differential diagnosis depends mainly on magnetic resonance imaging (MRI). MRI provides guidance for surgical management, the first-line treatment of choice. Four surgical approaches are described: abdominal, perineal, posterior and abdomino-sacral.. This review of major reported series has made it possible to specify the indications for each surgical approach, as well as the advantages, disadvantages and complications of each one. The choice of surgical approach is determined by the nature of the RRT, its anatomical position relative to the middle of the third sacral vertebra (S3) and the presence or absence of invasion of the neighboring organs, the pelvis or sacral vertebrae. The abdominal route is chosen for tumors situated above the middle of S3, whether benign or malignant, but without invasion of neighboring organs. The perineal route is indicated for benign RRT situated below the middle of S3. The posterior route is chosen for tumors located below the middle of S3, and allows an associated resection of sacral segments in case of tumor invasion. The combined abdomino-sacral route is indicated for RRT above the middle of S3, when there is an invasion of a pelvic organ or a sacral vertebra. Intra- and post-operative complications are mainly hemorrhagic, neurological and infectious. The long-term prognosis is usually favorable, but varies according to the nature of the RRT and its management.

Early postoperative obstruction after Roux-en-Y gastric bypass.

We report a case of early postoperative intestinal obstruction after gastric bypass. The most frequent radiologic presentation is one of gastric dilatation on the CT scan. It is a true surgical emergency.

An online sleep apnea detection method based on recurrence quantification analysis.

This paper introduces an online sleep apnea detection method based on heart rate complexity as measured by recurrence quantification analysis (RQA) statistics of heart rate variability (HRV) data. RQA statistics can capture nonlinear dynamics of a complex cardiorespiratory system during obstructive sleep apnea. In order to obtain a more robust measurement of the nonstationarity of the cardiorespiratory system, we use different fixed amount of neighbor thresholdings for recurrence plot calculation. We integrate a feature selection algorithm based on conditional mutual information to select the most informative RQA features for classification, and hence, to speed up the real-time classification process without degrading the performance of the system. Two types of binary classifiers, i.e., support vector machine and neural network, are used to differentiate apnea from normal sleep. A soft decision fusion rule is developed to combine the results of these classifiers in order to improve the classification performance of the whole system. Experimental results show that our proposed method achieves better classification results compared with the previous recurrence analysis-based approach. We also show that our method is flexible and a strong candidate for a real efficient sleep apnea detection system.

Topology and random-walk network representation of cardiac dynamics for localization of myocardial infarction.

While detection of acute cardiac disorders such as myocardial infarction (MI) from electrocardiogram (ECG) and vectorcardiogram (VCG) has been widely reported, identification of MI locations from these signals, pivotal for timely therapeutic and prognostic interventions, remains a standing issue. We present an approach for MI localization based on representing complex spatiotemporal patterns of cardiac dynamics as a random-walk network reconstructed from the evolution of VCG signals across a 3-D state space. Extensive tests with signals from the PTB database of the PhysioNet databank suggest that locations of MI can be determined accurately (sensitivity of ∼88% and specificity of ∼92%) from tracking certain consistently estimated invariants of this random-walk representation.