Treating COVID-19: Review of Drug Hypersensitivity Reactions.

The disease caused by the new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), ie, coronavirus disease 2019 (COVID-19), has become a global pandemic since it was first reported in Wuhan, China in December 2019. Its severe clinical manifestations, which often necessitate admission to intensive care units, and high mortality rate represent a therapeutic challenge for the medical community. To date, no drugs have been approved for its treatment, and various therapeutic options are being assayed to address the pathophysiological processes underlying the clinical manifestations experienced by patients. New and old drugs administered as monotherapy or in combination to immunologically compromised patients may favor the development of adverse drug reactions, including drug hypersensitivity reactions, which must be identified and managed accordingly. Given the lack of herd immunity and the high rate of viral contagion, new cases are expected to emerge in the coming months. Thus, the probability of more adverse reactions or even new clinical manifestations may increase in parallel. Allergists must receive updated information on these treatments, as well as on the management of possible drug hypersensitivity reactions.

Treating COVID-19: review of drug hypersensitivity reactions

The disease caused by the new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), ie, coronavirus disease 2019 (COVID-19), has become a global pandemic since it was first reported in Wuhan, China in December 2019. Its severe clinical manifestations, which often necessitate admission to intensive care units, and high mortality rate represent a therapeutic challenge for the medical community. To date, no drugs have been approved for its treatment, and various therapeutic options are being assayed to address the pathophysiological processes underlying the clinical manifestations experienced by patients. New and old drugs administered as monotherapy or in combination to immunologically compromised patients may favor the development of adverse drug reactions, including drug hypersensitivity reactions, which must be identified and managed accordingly. Given the lack of herd immunity and the high rate of viral contagion, new cases are expected to emerge in the coming months. Thus, the probability of more adverse reactions or even new clinical manifestations may increase in parallel. Allergists must receive updated information on these treatments, as well as on the management of possible drug hypersensitivity reactions

La enfermedad causada por el nuevo Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), Coronavirus Disease 2019 (COVID-19), se ha expandido en forma de pandemia global desde su inicio en Wuhan (China) en diciembre de 2019. La aparición de formas clínicas graves asociadas a la necesidad de ingreso en unidades de Cuidados Intensivos, con un alto índice de letalidad, ha supuesto un reto terapéutico para la comunidad médica. Actualmente no hay ningún fármaco aprobado para su tratamiento y se están ensayando diversas opciones terapéuticas para abordar los procesos fisiopatológicos responsables de las manifestaciones clínicas que experimentan los pacientes. Tanto el uso de viejos como de nuevos principios activos como tratamiento único o en combinación, en pacientes inmunológicamente comprometidos, puede favorecer la aparición de efectos adversos, entre ellos reacciones de hipersensibilidad de mecanismo inmunológico, que habrá que saber identificar y manejar correctamente. Es de prever que, en los próximos meses, dada la falta de inmunidad comunitaria y el elevado índice de contagiosidad del virus, sigan surgiendo nuevos casos y, con ello, la probabilidad de que aparezcan más reacciones adversas o incluso nuevas manifestaciones clínicas. Es importante que los alergólogos estén al día de las opciones terapéuticas que se están utilizando, así como de sus posibles reacciones adversas, inclusive reacciones de hipersensibilidad y cómo manejarlas

In vivo and in vitro antigenotoxic effect of nordihydroguaiaretic acid against SCEs induced by methyl methanesulfonate.

Nordihydroguaiaretic acid (NDGA) is a phenolic lignan which has shown to cause a variety of actions potentially useful for human health; therefore, in this investigation we determined its capacity for inhibiting the rate of sister chromatid exchanges (SCEs) induced by methyl methanesulfonate (MMS). We tested the effect of 0.25, 0.50, 1.0, and 2.0 microM of NDGA on the damage exerted by 55 microM of MMS. Cultured human lymphocytes from two female donors were used for the experiment. The best result concerning its modulatory action was obtained with 1.0 microM of NDGA; with this dose the mean inhibitory index including both donors reached 68.2%. The values obtained for the mitotic and proliferative indexes were not significantly modified with respect to the basal data. We also used the mouse bone marrow in vivo system to evaluate the inhibitory effect of the chemical. In this study we tested 1.0, 6.0, and 11.0 mg/kg of NDGA intraperitoneally (i.p.) administered 1 h before an i.p. injection of MMS (40 mg/kg). The best inhibitory index in this model corresponded to the dose of 11 mg/kg of NDGA (86.9%). The mitotic index and the average generation time showed no significant variation with respect to the control data. Our study established that NDGA produces antigenotoxic action in mammalian cells in vitro and in vivo.

Using Bayesian networks in the construction of a bi-level multi-classifier. A case study using intensive care unit patients data.

Combining the predictions of a set of classifiers has shown to be an effective way to create composite classifiers that are more accurate than any of the component classifiers. There are many methods for combining the predictions given by component classifiers. We introduce a new method that combine a number of component classifiers using a Bayesian network as a classifier system given the component classifiers predictions. Component classifiers are standard machine learning classification algorithms, and the Bayesian network structure is learned using a genetic algorithm that searches for the structure that maximises the classification accuracy given the predictions of the component classifiers. Experimental results have been obtained on a datafile of cases containing information about ICU patients at Canary Islands University Hospital. The accuracy obtained using the presented new approach statistically improve those obtained using standard machine learning methods.

Death due to live bee acupuncture apitherapy

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Prevalence of clonal mast cell disorders in patients presenting with hymenoptera venom anaphylaxis might be higher than expected

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Elefantiasis nostras verrucosa / Elephantiasis nostras verrucosa

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Pronóstico de una cohorte de enfermos en ventilación mecánica en 72 unidades de cuidados intensivos en España / Prognosis in a cohort of patients undergoing mechanical ventilation in 72 intensive care units in Spain

La utilización de sistemas para establecer el pronóstico de los pacientes es habitual en las unidades de cuidados intensivos. Su capacidad predictiva es variable y depende de la población a la que se aplica. Entre las poblaciones en las que han demostrado una menor exactitud se encuentra la de los enfermos ventilados mecánicamente. Estudio de cohortes de 1.103 pacientes ventilados mecánicamente. Con un análisis de particiones recursivas se determinaron las variables asociadas a la mortalidad, y con un análisis de regresión logística se construyeron dos modelos predictivos: el primero con el SAPS II y las variables previas al inicio de la ventilación mecánica, y el segundo con el SAPS II y las variables previas y aparecidas durante la ventilación mecánica. Para evaluar la predicción de la mortalidad se realizó una medida de calibración con el método de Lemeshow y Hosmer y una medida de discriminación calculando el área bajo la curva ROC.La mortalidad observada fue de un 42 por ciento (IC del 95 por ciento, 39-45) frente a una predicha por el SAPS II de un 36 por ciento. En el análisis de particiones recursivas, las variables asociadas a la mortalidad fueron: fracaso renal agudo, shock, PaO2/FiO2 < 150, SAPS II, coma y actividad limitada. Los modelos obtenidos tuvieron mejores discriminación y calibración que el SAPS II.En una cohorte de enfermos ventilados mecánicamente, el SAPS II fue un mal predictor de la mortalidad hospitalaria. La inclusión de factores previos al inicio y aparecidos durante la ventilación mecánica mejora discretamente la exactitud predictiva (AU)