Urothelial carcinoma masquerading as retroperitoneal fibrosis: A case report.

Retroperitoneal fibrosis, a rare and often idiopathic condition, poses significant diagnostic challenges. While most cases are considered idiopathic or immune-mediated, a small but important proportion are associated with malignant neoplasms, with implications for prognosis and management. The present study describes the case of a 69-year-old man who presented to the emergency department of the Virgen de las Nieves University Hospital (Granada, Spain), with a 2-week history of epigastric pain, vomiting and altered bowel habits. Laboratory investigations revealed previously undiagnosed renal insufficiency. An abdominal computed tomography (CT) scan showed extensive diffuse retroperitoneal infiltration extending from the periduodenal region to the pubic bone, resulting in gastric dilatation and hydronephrosis. A CT-guided retroperitoneal biopsy was performed and pathology confirmed the presence of urothelial carcinoma. This diagnosis led to the initiation of a chemotherapy regimen consisting of carboplatin and gemcitabine specifically designed for urothelial carcinoma. A follow-up 18F-FDG PET scan performed 6 months later showed a partial functional response. This case illustrates a rare presentation of urothelial carcinoma masked by extensive retroperitoneal fibrosis, and highlights the importance of accurate diagnosis in reducing tumor burden and improving the clinical status of patients.

Adaptive Trend Filtering for ECG Denoising and Delineation.

Standard recordings of electrocardiograhic signals are contaminated by a large variety of noises and interferences, which impair their analysis and the further related diagnosis. In this article, we propose a method, based on compressive sensing techniques, to remove the main noise artifacts and to locate the main features of the pulses in the electrocardiogram (ECG). The motivation is to use trend filtering with a varying proximal parameter, in order to sequentially capture the peaks of the ECG, which have different functional regularities. The practical implementation is based on an adaptive version of the alternating direction method of multiplier (ADMM) algorithm. We present results obtained on simulated signals and on real data illustrating the validity of this approach, showing that results in peak localization are very good in both cases and comparable to state of the art approaches.

Associations between Weight Status and Situational Motivation toward Fitness Testing in Physical Education: The Mediator Role of Physical Fitness.

BACKGROUND: This article examines the differences in situational motivation toward fitness testing in physical education classes between non-overweight and overweight students, as well as the mediator effect of objective and perceived physical fitness on the relationship between weight status and motivation toward fitness testing. METHODS: A total of 534 adolescents (298 boys, 55.80%) participated in the study. Perceived physical fitness and situational motivation toward fitness testing were measured through questionnaires, whereas weight status and physical fitness were objectively measured. RESULTS: Overweight students had lower intrinsic motivation (p < 0.001), and higher external regulation (p < 0.01) and amotivation (p < 0.05) during fitness testing in a physical education class than their non-overweight peers. The influence of being overweight on motivation regulations toward fitness testing was mediated by objective physical fitness level for intrinsic motivation (B = -0.140), external regulation (B = 0.104) and amotivation (B = 0.146). Perceived physical fitness was also used as a second mediator between weight status and intrinsic motivation (B = -0.117). CONCLUSIONS: Strategies to improve objective and perceived physical fitness in overweight students are necessary to increase self-determined motivation during fitness testing in physical education lesson.

Hierarchical Algorithms for Causality Retrieval in Atrial Fibrillation Intracavitary Electrograms.

Multichannel intracavitary electrograms (EGMs) are acquired at the electrophysiology laboratory to guide radio frequency catheter ablation of patients suffering from atrial fibrillation. These EGMs are used by cardiologists to determine candidate areas for ablation (e.g., areas corresponding to high dominant frequencies or complex fractionated electrograms). In this paper, we introduce two hierarchical algorithms to retrieve the causal interactions among these multiple EGMs. Both algorithms are based on Granger causality, but other causality measures can be easily incorporated. In both cases, they start by selecting a root node, but they differ on the way in which they explore the set of signals to determine their cause-effect relationships: either testing the full set of unexplored signals (GS-CaRe) or performing a local search only among the set of neighbor EGMs (LS-CaRe). The ensuing causal model provides important information about the propagation of the electrical signals inside the atria, uncovering wavefronts and activation patterns that can guide cardiologists towards candidate areas for catheter ablation. Numerical experiments, on both synthetic signals and annotated real-world signals, show the good performance of the two proposed approaches.

Feature Extraction of Galvanic Skin Responses by Nonnegative Sparse Deconvolution.

Wearable sensors are increasingly taking part in daily activities, not only because of the recent society health concern, but also due to their relevance in the medical industry. In this paper, a galvanic skin response (GSR) extraction technique has been developed in order to interpret electrodermal activity (EDA) records, which can be useful both for ambulatory and health applications. The core of the proposed approach is a novel feature extraction scheme that is based on a nonnegative sparse deconvolution of the observed GSR signals. Unlike previous approaches, the resulting SparsEDA algorithm is fast (immediately extracting the skin conductance level and response), efficient (being able to work with any sampling rate and signal length), and highly interpretable (due to the sparsity of the extracted phasic component of the GSR). Results on real data from 100 different subjects confirm the good performance of the method, which has been released through a free web-based code repository.

Estimation of Symmetric Channels for Discrete Cosine Transform Type-I Multicarrier Systems: A Compressed Sensing Approach.

The problem of channel estimation for multicarrier communications is addressed. We focus on systems employing the Discrete Cosine Transform Type-I (DCT1) even at both the transmitter and the receiver, presenting an algorithm which achieves an accurate estimation of symmetric channel filters using only a small number of training symbols. The solution is obtained by using either matrix inversion or compressed sensing algorithms. We provide the theoretical results which guarantee the validity of the proposed technique for the DCT1. Numerical simulations illustrate the good behaviour of the proposed algorithm.

Linear latent force models using Gaussian processes.

Purely data-driven approaches for machine learning present difficulties when data are scarce relative to the complexity of the model or when the model is forced to extrapolate. On the other hand, purely mechanistic approaches need to identify and specify all the interactions in the problem at hand (which may not be feasible) and still leave the issue of how to parameterize the system. In this paper, we present a hybrid approach using Gaussian processes and differential equations to combine data-driven modeling with a physical model of the system. We show how different, physically inspired, kernel functions can be developed through sensible, simple, mechanistic assumptions about the underlying system. The versatility of our approach is illustrated with three case studies from motion capture, computational biology, and geostatistics.