Prognostic factors in cancer patients infected with SARS-CoV-2: a Latin American country results.

PURPOSE: The aim of this study was to evaluate the demographic characteristics, clinical and pathological factors, and the outcome of cancer and COVID-19 patients in Mexico. PATIENTS AND METHODS: A prospective, multicentric study was performed through a digital platform to have a national registry of patients with cancer and positive SARS-CoV-2 test results through reverse transcription quantitative polymerase chain reaction (RT-qPCR). We performed the analysis through a multivariate logistic regression model and Cox proportional hazard model. RESULTS: From May to December 2020, 599 patients were registered with an average age of 56 years with 59.3% female; 27.2% had hypertension. The most frequent diagnoses were breast cancer (30.4%), lymphoma (14.7%), and colorectal cancer (14.0%); 72.1% of patients had active cancer and 23.5% of patients (141/599) were deceased, the majority of which were men (51.7%). This study found that the prognostic factors that reduced the odds of death were gender (OR = 0.42, p = 0.031) and oxygen saturation (OR = 0.90, p = 0.0001); meanwhile, poor ECOG (OR = 5.4, p = 0.0001), active disease (OR = 3.9, p = 0.041), dyspnea (OR = 2.5, p = 0.027), and nausea (OR = 4.0, p = 0.028) increased the odds of death. In the meantime, the factors that reduce survival time were age (HR = 1.36, p = 0.035), COPD (HR = 8.30, p = 0.004), having palliative treatment (HR = 10.70, p = 0.002), and active cancer without treatment (HR = 8.68, p = 0.008). CONCLUSION: Mortality in cancer patients with COVID-19 is determined by prognostic factors whose identification is necessary. In our cancer population, we have observed that being female, younger, non-COPD, with non-active cancer, good performance status, and high oxygen levels reduce the probability of death.

Facing High EEG Signals Variability during Classification Using Fractal Dimension and Different Cutoff Frequencies.

In the development of a brain-computer interface (BCI), some issues should be regarded in order to improve its reliability and performance. Perhaps, one of the most challenging issues is related to the high variability of the brain signals, which directly impacts the accuracy of the classification. In this sense, novel feature extraction techniques should be explored in order to select those able to face this variability. Furthermore, to improve the performance of the selected feature extraction technique, the parameters of the filter applied in the preprocessing stage need to be properly selected. Then, this work presents an analysis of the robustness of the fractal dimension as feature extraction technique under high variability of the EEG signals, particularly when the training data are recorded one day and the testing data are obtained on a different day. The results are compared with those obtained by an autoregressive model, which is a technique commonly used in BCI applications. Also, the effect of properly selecting the cutoff frequencies of the filter in the preprocessing stage is evaluated. This research is supported by several experiments carried out using a public data set from the BCI international competition, specifically data set 2a from BCIIC IV, related to motor tasks. By a statistical test, it is demonstrated that the performance achieved using the fractal dimension is significantly better than that reached by the AR model. Also, it is demonstrated that the selection of the appropriate cutoff frequencies improves significantly the performance in the classification. The increase rate is approximately of 17%.

Designing Artificial Neural Networks Using Particle Swarm Optimization Algorithms.

Artificial Neural Network (ANN) design is a complex task because its performance depends on the architecture, the selected transfer function, and the learning algorithm used to train the set of synaptic weights. In this paper we present a methodology that automatically designs an ANN using particle swarm optimization algorithms such as Basic Particle Swarm Optimization (PSO), Second Generation of Particle Swarm Optimization (SGPSO), and a New Model of PSO called NMPSO. The aim of these algorithms is to evolve, at the same time, the three principal components of an ANN: the set of synaptic weights, the connections or architecture, and the transfer functions for each neuron. Eight different fitness functions were proposed to evaluate the fitness of each solution and find the best design. These functions are based on the mean square error (MSE) and the classification error (CER) and implement a strategy to avoid overtraining and to reduce the number of connections in the ANN. In addition, the ANN designed with the proposed methodology is compared with those designed manually using the well-known Back-Propagation and Levenberg-Marquardt Learning Algorithms. Finally, the accuracy of the method is tested with different nonlinear pattern classification problems.

Training spiking neural models using artificial bee colony.

Spiking neurons are models designed to simulate, in a realistic manner, the behavior of biological neurons. Recently, it has been proven that this type of neurons can be applied to solve pattern recognition problems with great efficiency. However, the lack of learning strategies for training these models do not allow to use them in several pattern recognition problems. On the other hand, several bioinspired algorithms have been proposed in the last years for solving a broad range of optimization problems, including those related to the field of artificial neural networks (ANNs). Artificial bee colony (ABC) is a novel algorithm based on the behavior of bees in the task of exploring their environment to find a food source. In this paper, we describe how the ABC algorithm can be used as a learning strategy to train a spiking neuron aiming to solve pattern recognition problems. Finally, the proposed approach is tested on several pattern recognition problems. It is important to remark that to realize the powerfulness of this type of model only one neuron will be used. In addition, we analyze how the performance of these models is improved using this kind of learning strategy.

Trasplante renal de donador vivo relacionado con edades extremas de la vida. Una frontera en expansión / Living related donor renal transplantation in extreme ages of life. An expanding frontier

Los extremos de la edad se han considerado motivo de contraindicación en el trasplante renal. Sin embargo, existen otros factores más relevantes y determinantes, cuyo cuidado y manejo con éxito de dos pacientes con insuficiencia renal crónica (IRC), que fueron llevados a trasplante de donador vivo relacionado (uno menor de dos años y otro de 73 años), en quienes la edad no se consideró como factor decisivo para el trasplante. La preparación pretrasplante, la obtención de un injerto de donador vivo relacionado y el manejo perioperatorio, fueron los factores que influyeron en el éxito y la edad quedó como factor no relevante