Consumption of the Dietary Flavonoids Quercetin, Luteolin and Kaempferol and Overall Risk of Cancer - A Review and Meta-Analysis of the Epidemiological Data

Numerous epidemiological and preclinical studies suggest that flavonoids may play an important role in the decreased risk of cancer associated with a diet rich in plant-derived foods. In this article, we have reviewed the epidemiological studies assessing the relationship between the consumption of three of the most common flavonoids, i.e. quercetin, luteolin and kaempferol, and the risk of developing cancer. We have also performed a meta-analysis on the consumption of these three flavonoids (alone and combined) and overall risk of cancer. The analysis of data from 18 case-control studies (8585 cases with cancer and 9975 control subjects) revealed that a high consumption of these three flavonoids (combined) was associated with a statistically significant reduction of overall cancer risk (OR: 0.73; 95% CI: 0.63, 0.84; p<0.01). A reduction of overall cancer risk was also observed for quercetin (OR: 0.73; 95% CI: 0.62, 0.86; p<0.01), kaempferol (OR: 0.86; 95% CI: 0.73, 1.11; p>0.05) and luteolin (OR: 0.90; 95% CI: 0.69, 1.18; p>0.05), which was statistically significant for quercetin. A high intake of these three flavonoids (combined) was also associated with a statistically significant reduction of lung cancer risk (OR: 0.67; 95% CI: 0.49, 0.91; p<0.05) and colon cancer risk (OR: 0.75; 95% CI: 0.57, 0.98; p<0.05). The analysis of data from 14 cohort studies (385033 individuals and 10809 cancer cases) showed a statistically significant reduction of overall cancer risk for the three flavonoids combined (RR: 0.89; 95% CI: 0.80, 1.00; p<0.05), for quercetin (RR: 0.82; 95% CI: 0.71, 0.96; p<0.05) and for kaempferol (RR: 0.88; 95% CI: 0.78, 0.99; p<0.05), and a non-statistically significant reduction for luteolin (RR: 0.95; 95% CI: 0.67, 1.34; p>0.05). These results suggest that consumption of foods rich in the flavonoids quercetin, kaempferol and luteolin may reduce the risk of developing cance

Propuesta de un modelo predictivo basado en Machine Learning para el diagnóstico temprano de cáncer de mama en una empresa prestadora de servicio de salud privada

El presente trabajo de investigación se elaboró con la finalidad de reducir los gastos operativos de tratamiento de cáncer de mama en los diferentes estadios (I, II, III y IV), a causa de la activación de pólizas de seguros, para dicha propuesta se tomó como referencia una Empresa Prestadora de Servicio de Salud Privada (EPSSP). Cabe señalar que el trabajo de investigación se basa en la propuesta de implementación de un modelo predictivo para el diagnóstico temprano de cáncer de mama, para la cual se revisó la literatura de diferentes modelos de Machine Learning que fueron evaluados y probados por diferentes grupos de investigadores, asimismo se compararon cual de todas era la más efectiva en cuanto a precisión y porcentaje de efectividad, en consecuencia, se seleccionó el mejor modelo predictivo (Maquina de Vector de Soporte - SVM), luego se identificaron las variables y/o características (10) de valor real para cada núcleo celular de la mamografía que formaran parte del DataSet que será entrenado el modelo seleccionado. Finalmente, para la propuesta de implementación se aplicará la metodología CRISP-DM (Proceso estándar entre industrias para la minería de datos) por ser flexible, fácil, estructurado y confiable, además utilizada en proyectos similares, asimismo para esta propuesta se utilizará los servicios de Machine Learning de Microsoft Azure. La implementación del Modelo permitirá la reducción en un 2% los costos operativos del tratamiento de cáncer de mama en la EPSSP, incrementando el valor de la compañía en el segmento oncológico.The present research work was developed with the purpose of reducing the operational expenses of breast cancer treatment in different stages (I, II, III and IV), due to the activation of insurance policies. For this proposal, a Private Health Service Provider Company (PHSPC) was taken as a reference. It should be noted that the research work is based on the proposal of implementing a predictive model for early breast cancer diagnosis, for which the literature of different Machine Learning models was reviewed and evaluated by different groups of researchers. Likewise, they compared which one was the most effective in terms of accuracy and effectiveness percentage, and as a result, the best predictive model (Support Vector Machine - SVM) was selected. Then, the real value variables or characteristics (10) for each cellular nucleus of the mammography that will be part of the DataSet trained by the selected model were identified. Finally, for the implementation proposal, the CRISP-DM methodology (standard process for data mining in different industries) will be applied, as it is flexible, easy, structured, and reliable, and also used in similar projects. Additionally, Microsoft Azure's Machine Learning services will be used for this proposal. The implementation of the Model will allow a 2% reduction in the operational costs of breast cancer treatment in the PHSPC, increasing the value of the company in the oncological segment.Trabajo de investigació

Propuesta de un modelo predictivo basado en Machine Learning para el diagnóstico temprano de cáncer de mama en una empresa prestadora de servicio de salud privada

El presente trabajo de investigación se elaboró con la finalidad de reducir los gastos operativos de tratamiento de cáncer de mama en los diferentes estadios (I, II, III y IV), a causa de la activación de pólizas de seguros, para dicha propuesta se tomó como referencia una Empresa Prestadora de Servicio de Salud Privada (EPSSP). Cabe señalar que el trabajo de investigación se basa en la propuesta de implementación de un modelo predictivo para el diagnóstico temprano de cáncer de mama, para la cual se revisó la literatura de diferentes modelos de Machine Learning que fueron evaluados y probados por diferentes grupos de investigadores, asimismo se compararon cual de todas era la más efectiva en cuanto a precisión y porcentaje de efectividad, en consecuencia, se seleccionó el mejor modelo predictivo (Maquina de Vector de Soporte - SVM), luego se identificaron las variables y/o características (10) de valor real para cada núcleo celular de la mamografía que formaran parte del DataSet que será entrenado el modelo seleccionado. Finalmente, para la propuesta de implementación se aplicará la metodología CRISP-DM (Proceso estándar entre industrias para la minería de datos) por ser flexible, fácil, estructurado y confiable, además utilizada en proyectos similares, asimismo para esta propuesta se utilizará los servicios de Machine Learning de Microsoft Azure. La implementación del Modelo permitirá la reducción en un 2% los costos operativos del tratamiento de cáncer de mama en la EPSSP, incrementando el valor de la compañía en el segmento oncológico.The present research work was developed with the purpose of reducing the operational expenses of breast cancer treatment in different stages (I, II, III and IV), due to the activation of insurance policies. For this proposal, a Private Health Service Provider Company (PHSPC) was taken as a reference. It should be noted that the research work is based on the proposal of implementing a predictive model for early breast cancer diagnosis, for which the literature of different Machine Learning models was reviewed and evaluated by different groups of researchers. Likewise, they compared which one was the most effective in terms of accuracy and effectiveness percentage, and as a result, the best predictive model (Support Vector Machine - SVM) was selected. Then, the real value variables or characteristics (10) for each cellular nucleus of the mammography that will be part of the DataSet trained by the selected model were identified. Finally, for the implementation proposal, the CRISP-DM methodology (standard process for data mining in different industries) will be applied, as it is flexible, easy, structured, and reliable, and also used in similar projects. Additionally, Microsoft Azure's Machine Learning services will be used for this proposal. The implementation of the Model will allow a 2% reduction in the operational costs of breast cancer treatment in the PHSPC, increasing the value of the company in the oncological segment.Trabajo de investigació

Optimal Analysis for the Correlation between Vibration and Temperature through an Intelligent Sensor/Transducer Based in Amorphous Nanostructures to Measure Vibrating Surfaces Temperature

The vibration is an oscillatory movement caused by a propagation of waves through fluids or solids, and this consequence is achieved in many mechanic systems by the energy transmission between the movement source with the machine that needs the transmission movement, such as the vibration produced by a combustion engine, by a compressor system and by a result of movement transmission over rotor systems. However, if it is not a controlled mechanism to moderate the produced decibels, the main system that is affected by the vibration can reduce its performance; moreover, it can increase the surface temperature of the vibrating source and systems around. In spite of this, when it uses contact sensors to measure the vibration and temperature over the surface vibrating system, the measured data are under disturbance caused by the vibration source. Therefore, in this research is proposed an intelligent sensor/transducer based in amorphous nanostructures owing to measure the vibration of the surface through infrared (IR) emitter/receiver and the absorbance of the receiver sample has a quite range of work and robustness under disturbance of vibrating signals. This proposed sensor also has the possibility to charge energy by itself because of sun/warmth energy conversion

Numbers and language : what's new in the past 25 years?

Numerous studies in psychology, cognitive neuroscience and psycholinguistics have used pictures of objects as stimulus materials. Currently, authors engaged in cross-linguistic work or wishing to run parallel studies at multiple sites where different languages are spoken must rely on rather small sets of black-and-white or colored line drawings. These sets are increasingly experienced as being too limited. Therefore, we constructed a new set of 750 colored pictures of concrete concepts. This set, MultiPic, constitutes a new valuable tool for cognitive scientists investigating language, visual perception, memory and/or attention in monolingual or multilingual populations. Importantly, the MultiPic databank has been normed in six different European languages (British English, Spanish, French, Dutch, Italian and German). All stimuli and norms are freely available at http://www.bcbl.eu/databases/multipi

Nurses' perceptions of aids and obstacles to the provision of optimal end of life care in ICU

Contains fulltext : 172380.pdf (publisher's version ) (Open Access

Calcification of the main reef-building coral species on the Pacific coast of southern Mexico

Global warming and ocean acidification affect coral calcification. Nevertheless, there is not enough information regarding the growth parameters of the main reef-building coral species in marginal growth areas such as the Pacific coast of southern Mexico. In order to fill this gap, coral growth parameters of 8 hermatypic coral species (massive species: Porites panamensis, Porites lobata, Pavona gigantea, and Pavona varians; branching species: Pocillopora meandrina, Pocillopora damicornis, Pocillopora verrucosa, and Pocillopora capitata) were estimated in 2 areas of the southern Mexican Pacific. Branching coral species had a higher calcification rate (2.99–5.23 g CaCO3 cm–2 yr–1) than massive species (0.34–1.13 g CaCO3 cm–2 yr–1). A significant relation between sea surface temperature (SST) and skeletal density was observed in all massive coral species. Also, 2 massive species (P. gigantea and P. varians) showed a significant relation between SST and calcification rate. Upwelling in the Gulf of Tehuantepec transports deep water with low pH and low aragonite saturation, and may be affecting the calcification rate of stony corals in the studied area.

Outbreak of acute hepatitis A in the health area served by the Hospital Universitario Virgen de la Victoria (HUVV): a change in epidemiology.

In 2016, an outbreak of hepatitis A was identified in the Malaga province among patients with specific epidemiological characteristics, which were predominantly males. This is a report of 51 subjects with acute hepatitis A and a mean age of 35.7 years, 90% were male and 55% of cases were men who had had sex with other men within the last two months. Half of them required hospitalization for significant coagulopathy at diagnosis and no cases progressed to fulminant failure or encephalopathy. Four patients had ascites at the time of diagnosis. This outbreak adds to those reported in the United Kingdom and the Netherlands with a similar number of cases and epidemiology. These studies highlight the importance of epidemiological surveillance, the need for vaccination in this particular at risk population and the need for informative campaigns in order to prevent this disease