Potenciales aplicaciones de películas de quitosano en alimentos de origen animal: una revisión

En la actualidad se ha observado un incremento constante del número de intoxicaciones y muertes humanas alrededor del mundo debido a la contaminación de los alimentos de origen animal con patógenos perjudiciales para la salud. Es por esto que varias tecnologías se han investigado, desarrollado y aplicado para mejorar la inocuidad alimentaria. Dentro de estos métodos cabe mencionar la utilización de bioembalajes con polímeros naturales con una alta actividad antimicrobiana, como el quitosano,obtenido a partir de la desacetilación alcalina de la quitina. El quitosano presenta una gran variedad de propiedades biológicas como biodegradabilidad, biocompatibilidad, baja toxicidad, actividad antimicrobiana de amplio espectro, y capacidad de formación de películas comestibles. Esto lo convierten en un biomaterialatractivo para aplicaciones biotecnológicas e industriales. Un ejemplo de esto son las películas de quitosano,que hansido probadas en conservación de alimentos y la tecnología de envasado, ya que exhiben una alta actividad contra patógenos, como hongos, levaduras,bacterias Gram-positivas y negativas, disminuyendo el deterioro de los alimentos de origen animal y vegetal. Este artículo revisa las potenciales aplicaciones del quitosano debido a su acción antimicrobiana sobre los principales patógenos que afectan la inocuidad de los productos de origen animal como carne, cecinas, embutidos, leche, huevos, queso, pescado y mariscos, que repercuten en la mejora de la calidad y el aumento de la vida útil de éstos productos.  

Multi-Class Classifier in Parkinson’s Disease Using an Evolutionary Multi-Objective Optimization Algorithm

This work was funded by the Spanish Ministry of Sciences, Innovation and Universities under Project RTI-2018-101674-B-I00 and the projects from Junta de Andalucia B-TIC-414, A-TIC-530-UGR20 and P20-00163.In this contribution, a novel methodology for multi-class classification in the field of Parkinson’s disease is proposed. The methodology is structured in two phases. In a first phase, the most relevant volumes of interest (VOI) of the brain are selected by means of an evolutionary multi-objective optimization (MOE) algorithm. Each of these VOIs are subjected to volumetric feature extraction using the Three-Dimensional Discrete Wavelet Transform (3D-DWT). When applying 3D-DWT, a high number of coefficients is obtained, requiring the use of feature selection/reduction algorithms to find the most relevant features. The method used in this contribution is based on Mutual Redundancy (MI) and Minimum Maximum Relevance (mRMR) and PCA. To optimize the VOI selection, a first group of 550 MRI was used for the 5 classes: PD, SWEDD, Prodromal, GeneCohort and Normal. Once the Pareto Front of the solutions is obtained (with varying degrees of complexity, reflected in the number of selected VOIs), these solutions are tested in a second phase. In order to analyze the SVM classifier accuracy, a test set of 367 MRI was used. The methodology obtains relevant results in multi-class classification, presenting several solutions with different levels of complexity and precision (Pareto Front solutions), reaching a result of 97% as the highest precision in the test data.Spanish Government RTI-2018-101674-B-I00Junta de Andalucia B-TIC-414 A-TIC-530-UGR20 P20-0016

De la informática biomédica y la necesidad del dominio de habilidades digitales en las profesiones de la salud en el siglo XXI

Resume

Eifelian and Givetian (Middle Devonian) conodonts in the Iberian Chains

Depto. de Geodinámica, Estratigrafía y PaleontologíaFac. de Ciencias GeológicasFALSEMIU-Next Generation EUpu

Novel methodology for detecting and localizing cancer area in histopathological images based on overlapping patches

This work has been partially supported by the Project PID2021-128317OB-I0, funded by the MCIN/AEI/ 10.13039/501100011033 and ‘‘ERDF A way of making Europe". Funding for open access charge: Universidad de Granada / CBUA. All authors approved the final version of manuscript to be published.Cancer disease is one of the most important pathologies in the world, as it causes the death of millions of people, and the cure of this disease is limited in most cases. Rapid spread is one of the most important features of this disease, so many efforts are focused on its early-stage detection and localization. Medicine has made numerous advances in the recent decades with the help of artificial intelligence (AI), reducing costs and saving time. In this paper, deep learning models (DL) are used to present a novel method for detecting and localizing cancerous zones in WSI images, using tissue patch overlay to improve performance results. A novel overlapping methodology is proposed and discussed, together with different alternatives to evaluate the labels of the patches overlapping in the same zone to improve detection performance. The goal is to strengthen the labeling of different areas of an image with multiple overlapping patch testing. The results show that the proposed method improves the traditional framework and provides a different approach to cancer detection. The proposed method, based on applying 3x3 step 2 average pooling filters on overlapping patch labels, provides a better result with a 12.9% correction percentage for misclassified patches on the HUP dataset and 15.8% on the CINIJ dataset. In addition, a filter is implemented to correct isolated patches that were also misclassified. Finally, a CNN decision threshold study is performed to analyze the impact of the threshold value on the accuracy of the model. The alteration of the threshold decision along with the filter for isolated patches and the proposed method for overlapping patches, corrects about 20% of the patches that are mislabeled in the traditional method. As a whole, the proposed method achieves an accuracy rate of 94.6%.MCIN/AEI/ 10.13039/501100011033/ PID2021-128317OB-I0ERDF A way of making EuropeUniversidad de Granada / CBU

Main findings and advances in bioinformatics and biomedical engineeringIWBBIO 2018

We want to thank the great work done by the reviewers of each of the papers, together with the great interest shown by the editorial of BMC Bioinformatics in IWBBIO Conference. Special thanks to D. Omar El Bakry for his interest and great help to make this Special Issue. Thank the Ministry of Spain for the economic resources within the project with reference RTI2018-101674-B-I00.In the current supplement, we are proud to present seventeen relevant contributions from the 6th International Work-Conference on Bioinformatics and Biomedical Engineering (IWBBIO 2018), which was held during April 25-27, 2018 in Granada (Spain). These contributions have been chosen because of their quality and the importance of their findings.This research has been partially supported by the proyects with reference RTI2018-101674-B-I00 (Ministry of Spain) and B-TIC-414-UGR18 (FEDER, Junta Andalucia and UGR)

Signos neuromusculares anormales tras la anestesia con romifidina-propofol-halotano en dos algas

Dos galgas (ASA I, con edades de 1.5 y 8 años) incluídas en un estudio anestésico experimental, mostraron signos de una reacción neuromuscular epileptiforme con mioclonías, movimientos natatorios de las extremidades anteriores y opistótonos, durante el inicio de la recuperación anestésica en varios procedimientos anestésicos. Ninguna tenía antecedentes de epilepsia. Las dos fueron anestesiadas con los siguientes protocolos anestésicos: 1. Romifidina (ROM) Ilvl-Atropina (ATR)- Propofol (PRO)-Halotano (HAL), 2. ROM IV-ATRPRO-HAL. 3. Xilacina (XIL)-ATR-PRO-HAL, 4. Medetomidina (MED)-ATR-PRO-HAL y 5. ROMATR-Tiopental sódico (TIO)-HAL. Las perras no fueron sometidas a ninguna intervención quirúrgica. La anestesia se mantuvo durante 60 minutos en todos los casos. Las variables anestésicas y los parámetros sanguíneos estudiados se mantuvieron dentro de los rangos normales. La galga mayor mostró el cuadro descrito en los protocolos 1, 2 y 3 (más marcado en el 2), mientras que la más joven sólo los presentó en el 2, y con una menor intensidad en los signos clínicos. En todos los casos, éstos aparecieron de 2 a 5 minutos después de terminar la administración de halotano, y tras 15 minutos, y adoptar las pacientes el decúbito esternal, los signos cesaron. No fue necesario administrar ningún fármaco para aliviar el cuadro convulsivo. A partir de entonces, la recuperación fue normal y las pacientes no mostraron posteriormente síntomas nerviosos.Two female Greyhounds (ASA I, aged 1.5 and 8 years) showed signs of a neuromuscular epileptiforrn activity, with paddling, opisthotonus and shivering, during the anaesthetic recovery in some anaesthetic protocols. No epilepsy history was registered befare in any patient. The dogs were anaesthetized with the following procedures: 1. Romifidine (ROM) IMAtropine (ATR)-Propofol (PRO)-Halothane (HAL), 2. ROM IV-ATR-PRO-HAL. 3. Xylazine (XIL)-ATR-PROHAL, 4. Medetomidine (MED)-ATR-PRO-HAL y 5. ROM-ATR-Thiopentone (TIO)-HAL. The older Greyhound showed an epileptiforrn activity in the protocols 1, 2 and 3 (more intense in 2), however the younger one only exhibited these signs in the protocol 2. The older one suffered excitatory phenomena deeper than the other one. In all cases, signs began 2-5 minutes after the end of the halothane administration, and after 15 minutes, when the patients adopted the sternal recumbency the signs disappeared. Anaesthetic variables and haematological values were within normal limits. No drug was administered for treating the excitatory phenomena. The recovery was normal and the dogs did not exhibite nervous signs later

Nota preliminar sobre la estratigrafía y el contenido en conodontos del Prídolí-Lochkoviense inferior en la unidad de els Castells (Pirineo central)

Instituto Geológico Minero de España; DGE PB95-104