During the COVID-19 pandemic, an increased frequency of peripheral facial nerve palsy has been described in adults and children. The etiology of the disease during this time remains unclear, since ...most cases occurred in patients who tested negative for SARS-CoV-2 infection.
Retrospective study of pediatric cases of facial nerve palsy treated during the first year of the pandemic in the emergency department of a children´s hospital located in one of the areas with the highest prevalence of COVID-19 in Spain. Data from this period are compared with cases from the previous three years.
Twenty-nine patients with Bell's palsy were included. Over the previous three years combined, 24 patients presented with the same condition, a more than threefold increase. No clinical differences were found between the groups apart from the fact that fewer patients received corticosteroids during the pandemic (13.8% vs 41.6%; p = 0.022). Fourteen children underwent microbiologic testing for active SARS-CoV-2 infection (12 polymerase chain reaction, two rapid antigen test); all were negative. Thirteen patients received serologic testing, two with a positive IgG (15.3%).
A substantial increase in hospital presentations for facial nerve palsy was observed among children and adolescents during the first year of the pandemic, though findings of microbiologic testing cannot confirm a direct link with SARS-CoV-2 infection in most cases. Patient characteristics did not change between the two time periods. Difficulty accessing primary-care facilities during the pandemic in Spain may have played a role in this increase.
Over the last ten years, Salamanca has been considered among the most polluted cities in Mexico, with the most important air pollutants being SO 2 and PM 10 . Currently, in Salamanca, an ...environmental monitoring network (EMN) is installed in which time series of criteria pollutants and meteorological variables are obtained. Unfortunately air pollution level is computed in each monitoring station without taking into account those meteorological variables. In this paper, we propose a novel methodology to compute air pollution levels taking the meteorological variables as a decision factor by means of data fusion and neural networks. First, in preprocessing stage two feature vectors (FV SO 2 and FV PM 10 ) are built for each monitoring station. Next, in data fusion stage, a representative feature vector by pollutant (RFV SO 2 and RFV PM 10 ) is built with the maximum value of the three FVs. Finally, an artificial neural network (ANN) is trained with the RFV in order to classify future environmental situations. Self-organizing map (SOM) is the ANN applied. In this paper, time series of pollutant concentrations and meteorological variables are obtained from the EMN. EMN is composed for the three monitoring stations in Salamanca. Data used in this study have approved according to Proaire environmental authority standards.
One of the mayor limitations associated to Neural Networks (NNs) learning algorithms is the number of input-output pattern pairs needed to obtain the desired classilication pcrlbmancc. In many ...practical cases, the number of pairs used to design the NN is much lower than necessary. Either bccause of the excessivc price of data acquisition or, simply, for availability reasons. An Importance Sampling (IS) technique is applied in this paper to NN training a in order to drastically improve the training cfticieiicy of the given training patterns. For this purpose, the Mean Square Error (MSE) objective function of a Backpropagalion algorithm has adequately been moditied hy applying a suboptimal IS fiinction. The application of the IS fiinction accelerates training convergence whereas it inaintains NN perromance.
The present study describes the design of an Artificial Neural Network to synthesize the Approximation Function of a Pedometer for the Healthy Life Style Promotion. Experimentally, the approximation ...function is synthesized using three basic digital pedometers of low cost, these pedometers were calibrated with an advanced pedometer that calculates calories consumed and computes distance travelled with personal stride input. The synthesized approximation function by means of the designed neural network will allow to reply the calibration experiment for multiple patients with Diabetes Mellitus in Healthy Life Style promotion programs. Artificial Neural Networks have been developed for a wide variety of computational problems in cognition, pattern recognition, and decision making. The Healthy Life Style refer to adequate nutrient ingest, physical activity, time to rest, stress control, and a high self-esteem. The pedometer is a technological device that helps to control the physical activity in the diabetic patient. A brief description of the Artificial Neural Network designed to synthesize the Approximation Function, the obtained Artificial Neural Network structure and results in the Approximation Function synthesis for three patients are presented. The advantages and disadvantages of the method are discussed and our conclusions are presented.
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