Determination of drugs used in combined cardiovascular therapy

Cardiovascular diseases are nowadays the first cause of mortality worldwide, causing around the 30% of global deaths each year. The risk of suffering from cardiovascular illnesses is strongly related to some factors such as hypertension, high cholesterol levels, diabetes, obesity The combination of these different risk factors is known as metabolic syndrome and it is considered a pandemic due to the high prevalence worldwide. The pathology of the disorders implies a combined cardiovascular therapy with drugs which have different targets and mechanisms of action, to regulate each factor separately. The simultaneous analysis of these drugs turns interesting but it is a complex task since the determination of multiple substances with different physicochemical properties and physiological behavior is always a challenge for the analytical chemist. The complexity of the biological matrices and the difference in the expected concentrations of some analytes require the development of extremely sensitive and selective determination methods. The aim of this work is to fill the gap existing in this field of the drug analysis, developing analytical methods capable of quantifying the different drugs prescribed in combined cardiovascular therapy simultaneously. Liquid chromatography andem mass spectrometry (LCMS/MS) has been the technique of choice throughout the main part of this work, due to the high sensitivity and selectivity requirements.

Identification of a biomarker panel for colorectal cancer diagnosis

Background: Malignancies arising in the large bowel cause the second largest number of deaths from cancer in the Western World. Despite progresses made during the last decades, colorectal cancer remains one of the most frequent and deadly neoplasias in the western countries. Methods: A genomic study of human colorectal cancer has been carried out on a total of 31 tumoral samples, corresponding to different stages of the disease, and 33 non-tumoral samples. The study was carried out by hybridisation of the tumour samples against a reference pool of non-tumoral samples using Agilent Human 1A 60- mer oligo microarrays. The results obtained were validated by qRT-PCR. In the subsequent bioinformatics analysis, gene networks by means of Bayesian classifiers, variable selection and bootstrap resampling were built. The consensus among all the induced models produced a hierarchy of dependences and, thus, of variables. Results: After an exhaustive process of pre-processing to ensure data quality–lost values imputation, probes quality, data smoothing and intraclass variability filtering–the final dataset comprised a total of 8, 104 probes. Next, a supervised classification approach and data analysis was carried out to obtain the most relevant genes. Two of them are directly involved in cancer progression and in particular in colorectal cancer. Finally, a supervised classifier was induced to classify new unseen samples. Conclusions: We have developed a tentative model for the diagnosis of colorectal cancer based on a biomarker panel. Our results indicate that the gene profile described herein can discriminate between non-cancerous and cancerous samples with 94.45% accuracy using different supervised classifiers (AUC values in the range of 0.997 and 0.955).

The Health Impacts of Climate Change: A Study of Cholera in Tanzania

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Plataforma de investigación en el ámbito de la resucitación cardio-pulmonar basada en un maniquí de entrenamiento sensorizado

[ES]Hoy en día las muertes por parada cardiorrespiratoria superan en número a otras más mediáticas como aquellas que se producen por incendios o en accidentes de tráfico, y sin embargo su repercusión es mucho menor. Este hecho debe ser motivo de preocupación ya que, con una correcta formación de la población en materia de resucitación cardíaca, muchas de estas muertes podrían ser evitadas. Con el objetivo de reducir estas estadísticas han surgido multitud de estudios y proyectos de investigación consistentes en tratar de mejorar las herramientas disponibles tanto para personal sanitario como no sanitario. En este marco se encuadra el proyecto presentado en este documento, consistente en la sensorización de un maniquí de entrenamiento para episodios de parada cardiorrespiratoria, el cual ofrecerá la posibilidad de analizar con detalle el artifact o interferencia generada por el rescatador sobre el paciente en el momento de efectuar la maniobra de resucitación, así como la interferencia causada por el contacto electrodo-piel. Paralelamente podrá ser utilizado como mero instrumento de entrenamiento para posibles situaciones reales. El porqué de la utilización de este tipo de maniquíes reside principalmente en la imposibilidad de emplear personas debido a las posibles lesiones torácicas que pueden ocurrir por las compresiones realizadas. Finalmente debe citarse el hecho de que no es imprescindible tener conocimientos médicos para poder aplicar las técnicas básicas de resucitación cardíaca, acción que incrementa las posibilidades de supervivencia de un paciente de manera excepcional, ya que cada minuto que pasa desde la parada cardiorrespiratoria la probabilidad de supervivencia disminuye en un porcentaje significativamente elevado. Tomando como base lo descrito hasta ahora, en este documento se detalla la solución técnica de la sensorización de un maniquí genérico para la adquisición de las señales de fuerza de compresión, aceleración sufrida por el pecho en tres ejes ortogonales, profundidad de compresión, impedancia entre los dos electrodos colocados sobre el pecho del paciente y señal electrocardiográfica emitida por el corazón; además, se incluye la posibilidad de inyectar una señal electrocardiográfica previamente grabada. La base de registros obtenida de estos ensayos podrá ser utilizada posteriormente para su análisis, ya que su similitud con señales extraídas en un caso real es máxima.

Spatial-temporal excess mortality patterns of the 1918-1919 influenza pandemic in Spain

Background: The impact of socio-demographic factors and baseline health on the mortality burden of seasonal and pandemic influenza remains debated. Here we analyzed the spatial-temporal mortality patterns of the 1918 influenza pandemic in Spain, one of the countries of Europe that experienced the highest mortality burden. Methods: We analyzed monthly death rates from respiratory diseases and all-causes across 49 provinces of Spain, including the Canary and Balearic Islands, during the period January-1915 to June-1919. We estimated the influenza-related excess death rates and risk of death relative to baseline mortality by pandemic wave and province. We then explored the association between pandemic excess mortality rates and health and socio-demographic factors, which included population size and age structure, population density, infant mortality rates, baseline death rates, and urbanization. Results: Our analysis revealed high geographic heterogeneity in pandemic mortality impact. We identified 3 pandemic waves of varying timing and intensity covering the period from Jan-1918 to Jun-1919, with the highest pandemic-related excess mortality rates occurring during the months of October-November 1918 across all Spanish provinces. Cumulative excess mortality rates followed a south-north gradient after controlling for demographic factors, with the North experiencing highest excess mortality rates. A model that included latitude, population density, and the proportion of children living in provinces explained about 40% of the geographic variability in cumulative excess death rates during 1918-19, but different factors explained mortality variation in each wave. Conclusions: A substantial fraction of the variability in excess mortality rates across Spanish provinces remained unexplained, which suggests that other unidentified factors such as comorbidities, climate and background immunity may have affected the 1918-19 pandemic mortality rates. Further archeo-epidemiological research should concentrate on identifying settings with combined availability of local historical mortality records and information on the prevalence of underlying risk factors, or patient-level clinical data, to further clarify the drivers of 1918 pandemic influenza mortality.