Organophosphate intoxication is a very infrequent cause of acute deterioration of renal function but, when it does occur, it seriously aggravates the clinical status and prognosis. The pathogenesis of renal injury in this context remains unknown, although it is suspected that direct damage occurs at renal tubules. It has not been demonstrated that substitutive kidney treatment and specific antidotes provide any clear benefit. Here, we report a 58-year-old patient who underwent an accidental organophosphate intoxication, who suffered acute anuric renal failure and severe metabolic acidosis and who was successfully treated with conventional haemodialysis. The patient recovered with no sequellae and no need for other therapeutic measures.

El NASA-TLX es uno de los procedimientos subjetivos más ampliamente utilizado en la evaluación de la carga mental. La aplicación de este instrumento requiere una primera fase de ponderación de la importancia que cada dimensión de carga mental tiene para el sujeto, lo que parece ser un inconveniente ya que no sólo distingue un número excesivo de dimensiones, sino que además diversas investigaciones han puesto de manifiesto la dificultad de los sujetos para comprender esa fase, lo que dificulta su correcta realización. En este artículo, se pone de manifiesto el alto grado de correlación encontrado entre medidas ponderadas y sin ponderar de carga mental obtenidas con el NASA-TLX, lo que sugiere que la fase de ponderación no es necesaria. Esto permitiría agilizar su aplicación, evitar la pérdida de casos y añadir una ventaja más al instrumento.

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The inability to find the solution in engineering problems has led to a large part of the scientific community developing indirect and alternative techniques to find optimization problem-solving. Genetic algorithms are looking for models based on the natural and genetic selection process, which optimizes a population or set of possible solutions to deliver one that is optimal or at least very close to it in the sense of a fitting function. In this work, we derive and evaluate a method based on genetic algorithms to find the relative maximum of differentiable functions that are difficult to find by analytical methods. We build a library in Python that includes different components from genetic algorithms. The test problems include finding the maximum or minimum of functions in one and two dimensions.