Péptidos y proteínas de la leche con propiedades funcionales

La fracción proteica de la leche contiene un gran número de compuestos biológicamente activos. Además de lasproteínas de la leche, caseínas y proteínas del suero lácteo, existen también pequeñas cantidades de otras proteínasy péptidos. Estos péptidos, que son inactivos dentro de la secuencia de la proteína nativa, pueden ser liberados porhidrólisis enzimática, por ejemplo, durante la digestión gastrointestinal o durante el procesado del alimento. Estospéptidos bioactivos procedentes de proteínas lácteas presentan una actividad moduladora de numerosos procesosmetabólicos del organismo. Asimismo, las proteínas del suero lácteo representan una mezcla variada de proteínassecretadas, tales como á-lactoalbúmina, â-lactoglobulina, lactoferrina, lactoperoxidasa, inmunoglobulinas,glicomacropéptido y una gran cantidad de factores de crecimiento. Estas proteínas tienen una serie de efectosbiológicos, que van desde un efecto anticancerígenos hasta efectos en la función digestiva

Interpreting intraplate tectonics for seismic hazard : a UK historical perspective

It is notoriously difficult to construct seismic source models for probabilistic seismic hazard assessment in intraplate areas on the basis of geological information, and many practitioners have given up the task in favour of purely seismicity-based models. This risks losing potentially valuable information in regions where the earthquake catalogue is short compared to the seismic cycle. It is interesting to survey how attitudes to this issue have evolved over the past 30 years. This paper takes the UK as an example, and traces the evolution of seismic source models through generations of hazard studies. It is found that in the UK, while the earliest studies did not consider regional tectonics in any way, there has been a gradual evolution towards more tectonically based models. Experience in other countries, of course, may differ

Identifying masked uncontrolled hypertension in the community pharmacy setting

© 2015 Wolters Kluwer Health, Inc. Masked uncontrolled hypertension (MUCH) is associated with an increased cardiovascular risk. This condition is frequent in the community pharmacy (i.e., CP-MUCH), but there is no evidence on the factors associated with its presence in that setting. The aim of this analysis was to explore these factors. A sample of 98 treated hypertensive patients from the MEPAFAR study, with normal community pharmacy blood pressure (CPBP <135/85 mmHg), were analyzed. Blood pressure (BP) was also measured at home (4 days) and monitored for 24 h. CP-MUCH was identified when either ambulatory (daytime) or home BP averages were equal to or higher than 135/85 mmHg. A multivariate logistic regression analysis was carried out to identify the factors associated with CP-MUCH. The prevalence of CP-MUCH tends to be higher as systolic and diastolic CPBP increase, reaching 47% in patients with both systolicand diastolic CPBP equal to or higher than 123 mmHg and 79 mmHg, respectively.The multivariate regression analysis showed only systolic CPBP as an independent factor of CP-MUCH [≥123 mmHg: odds ratio=16.46 (P=0.012); from 115 to 122.9 mmHg: odds ratio=10.74 (P=0.036); systolic CPBP <115 mmHg as the reference]. Further assessment, using ambulatory and/or home BP monitoring, is recommended in patients with normal CPBP, but systolic CPBP equal to or higher than 115 mmHg. A more feasible approach would be evaluating patients with systolic CPBP equal to or higher than 123 mmHg and diastolic CPBP equal to or higher than 79 mmHg

Machine learning for BMS analysis and optimisation

In large buildings, linking heating, cooling or ventilation systems between themselves and to physical spaces is a very time-consuming task that requires highly skilled engineering knowledge, as all these systems are interconnected and they have a certain influence to each other (ventilation systems are often connected to heating and cooling), which often makes task of locating the sources of error or anomalies very time consuming and difficult as they are performed manually. A different approach would be to work out relationships and equipment linkage from time series data provided by the sensors, thus inferring equipment links from which anomalies can be traced back to the source more easily. This paper proposes a data-based solution to obtain equipment relationships based on cross-correlations to relate Air Handling Units (AHUs) to their respective areas of operation. We also propose a methodology, in particular for AHUs, to identify whether or not to trust correlations based on the difference between supply and return temperature. A case study is presented based a large building with 16 AHU systems.Department for Business, Energy and Industrial Strategy of the United Kingdom; College of Engineering, Design and Physical Sciences of Brunel University Londo

Machine learning for text classification in building management systems

Copyright © 2022 The Author(s). In building management systems (BMS), a medium building may have between 200 and 1000 sensor points. Their labels need to be translated into a naming standard so they can be automatically recognised by the BMS platform. The current industrial practices often manually translate these points into labels (this is known as the tagging process), which takes around 8 hours for every 100 points. We introduce an AI-based multi-stage text classification that translates BMS points into formatted BMS labels. After comparing five different techniques for text classification (logistic regression, random forests, XGBoost, multinomial Naive Bayes and linear support vector classification), we demonstrate that XGBoost is the top performer with 90.29% of true positives, and use the prediction confidence to filter out false positives. This approach can be applied in sensors networks in various applications, where manual free-text data pre-processing remains cumbersome.Department for Business, Energy and Industrial Strategy of the United Kingdom; Brunel University Londo

Componentes biológicamente activos de la leche materna

La leche materna es un complejo fluido biológico que aporta la energía y los nutrientes esenciales para el desarrolloy crecimiento del recién nacido. Pero además, la leche materna contiene toda una serie de compuestos bioactivoscomo enzimas, hormonas, factores de crecimiento, proteínas específicas, poliaminas, nucleótidos, oligosacáridos, etc.,que ejercen efectos biológicos y que en conjunto reciben el nombre de “factores tróficos de la leche”. Estos compuestosbiactivos son considerados nutrientes potencialmente esenciales en periodos de desarrollo y en determinadasenfermedades, cuando la capacidad de síntesis no supera las necesidades de los mismos. Aunque las fórmulasinfantiles aportan todos los nutrientes para un adecuado desarrollo del recién nacido, carecen de muchos de estoscompuestos. Este artículo pretende realizar una revisión acerca de los conocimientos actuales sobre los efectosbiológicos de la lactoferrina, nucleótidos, poliaminas y oligosacáridos

An open secret in porcine acute myocardial infarction models: The relevance of anaesthetic regime and breed in ischaemic outcomes.

Large animal models of acute myocardial infarction (AMI) play a crucial role in translating novel therapeutic approaches to patients as denoted by their use in the right-before-human testing platform. At present, the porcine model of AMI is used most frequently as it mimics the human condition and its anatomopathological features accurately. We want to describe to, and share with, the translational research community our experience of how different anaesthetic protocols (sevoflurane, midazolam, ketamine+xylazine+midazolam, and propofol) and pig breeds [Large White and Landrace x Large White (LLW)] can dramatically modify the outcomes of a well-established porcine model of closed-chest AMI. Our group has extensive experience with the porcine model of reperfused AMI and, over time, we reduced the time of ischaemia used to induce the disease from 90 to 50 min to increase the salvageable myocardium for cardioprotection studies. For logistical reasons, we changed both the anaesthetic protocol and the pig breed used, but these resulted in a dramatic reduction in the size of the myocardial infarct, to almost zero in some cases (sevoflurane, 50-min ischaemia, LLW, 2.4 ± 3.9% infarct size), and the cardiac function was preserved. Therefore, we had to re-validate the model by returning to 90 min of ischaemia. Here, we report the differences in infarct size and cardiac function, measured by different modalities, for each combination of anaesthetic protocol and pig breed we have used. Furthermore, we discuss these combinations and the limited literature pertaining to how these two factors influence cardiac function and infarct size in the porcine model of AMI.This research was funded by a grant (PI18/00277) from Instituto de Salud Carlos III (ISCIII), Spain—Fondo Europeo de Desarrollo Regional (FEDER). FJ is the recipient of the Ayudas para la formación de profesorado Universitario (FPU19/04925) grant from the Spanish Ministry of Science and Innovation. IDIBAPS belongs to the CERCA Programme and receives partial funding from the Generalitat de Catalunya.S