Food prospects of selenium enriched-Lactobacillus acidophilus CRL 636 and Lactobacillus reuteri CRL 1101

Selenium, which is present as SeCys in selenoproteins, is involved in cancer prevention, thyroid functioning, and pathogen inhibition. Lactobacilli can biotransform inorganic Se into seleno-amino acids. Growth, Se accumulation and seleno-amino acid formation by Lactobacillus acidophilus CRL636 and L. reuteri CRL1101 in a Se-supplemented medium were studied. Moreover, survival of Se-enriched strains to different pH values and bile salts was analyzed. L. acidophilus CRL636 showed low growth rate in the presence of Se while differences were less evident for L. reuteri CRL1101, which displayed higher amounts of intracellular SeCys and SeMet than the CRL636 strain. Interestingly, both lactobacilli could produce Se-nanoparticles. Se-enriched lactobacilli showed lower growth rates than non-Se exposed cells. The adverse effect of bile salts and the ability to survive at pH 4.0 diminished for the Se-enriched L. reuteri strain. The studied lactobacilli could be used as Se-enriched probiotics or as a vehicle for manufacturing Se-containing fermented foods.Fil: Pescuma, Micaela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucuman. Centro de Referencia Para Lactobacilos; Argentina. Universidad Complutense de Madrid; EspañaFil: Gomez Gomez, Beatríz. Universidad Complutense de Madrid; EspañaFil: Perez Corona, Teresa. Universidad Complutense de Madrid; EspañaFil: Font, Graciela Maria. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucuman. Centro de Referencia Para Lactobacilos; ArgentinaFil: Madrid Albarrn, Yolanda. Universidad Complutense de Madrid; EspañaFil: Mozzi, Fernanda Beatriz. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucuman. Centro de Referencia Para Lactobacilos; Argentin

Oesophageal cancer mortality in Spain: a spatial analysis

BACKGROUND: Oesophageal carcinoma is one of the most common cancers worldwide. Its incidence and mortality rates show a wide geographical variation at a world and regional level. Geographic mapping of age-standardized, cause-specific death rates at a municipal level could be a helpful and powerful tool for providing clues leading to a better understanding of its aetiology. METHODS: This study sought to describe the geographic distribution of oesophageal cancer mortality for Spain's 8077 towns, using the autoregressive spatial model proposed by Besag, York and Mollié. Maps were plotted, depicting standardised mortality ratios, smoothed relative risk (RR) estimates, and the spatial pattern of the posterior probability of RR being greater than 1. RESULTS: Important differences associated with area of residence were observed in risk of dying from oesophageal cancer in Spain during the study period (1989-1998). Among men, excess risk appeared across the north of the country, along a band spanning the length of the Cantabrian coastline, Navarre, the north of Castile & León and the north-west of La Rioja. Excess risk was likewise observed in the provinces of Cadiz and part of Seville in Andalusia, the islands of Tenerife and Gran Canaria, and some towns in the Barcelona and Gerona areas. Among women, there was a noteworthy absence of risk along the mid-section of the Cantabrian seaboard, and increases in mortality, not observed for men, in the west of Extremadura and south-east of Andalusia. CONCLUSION: These major gender- and area-related geographical differences in risk would seem to reflect differences in the prevalence of some well-established and modifiable risk factors, including smoking, alcohol consumption, obesity and diet. In addition, excess risks were in evidence for both sexes in some areas, possibly suggesting the implication of certain local environmental or socio-cultural factors. From a public health standpoint, small-area studies could be very useful for identifying locations where epidemiological research and intervention measures ought to receive priority, given the potential for reducing risk in certain places.This study was funded by Grant No. EPY-1176/02 from the Carlos III Institute of Health (ISCIII) and RCESP FIS-C03/09 (Spanish Network for Cooperative Research in Epidemiology and Public Health)S

NAFLD and AATD Are Two Diseases with Unbalanced Lipid Metabolism: Similarities and Differences

Non-alcoholic fatty liver disease (NAFLD) is a type of steatosis commonly associated with obesity, dyslipidemia, hypertension, and diabetes. Other diseases such as inherited alpha-1 antitrypsin deficiency (AATD) have also been related to the development of liver steatosis. The primary reasons leading to hepatic lipid deposits can be genetic and epigenetic, and the outcomes range from benign steatosis to liver failure, as well as to extrahepatic diseases. Progressive hepatocellular damage and dysregulated systemic immune responses can affect extrahepatic organs, specifically the heart and lungs. In this review, we discuss the similarities and differences between the molecular pathways of NAFLD and AATD, and the putative value of hepatic organoids as novel models to investigate the physio pathological mechanisms of liver steatosis.This work was supported by grants from Instituto de Salud Carlos III (ISCIII): AESI PI20CIII/00015 and PISCIIIBB-PT20CIII/00009, and by Polish National Science Centre Grants 2015/17/B/NZ5/01370 and 2018/29/B/NZ5/02346.S

Validation of the geographic position of EPER-Spain industries

BACKGROUND: The European Pollutant Emission Register in Spain (EPER-Spain) is a public inventory of pollutant industries created by decision of the European Union. The location of these industries is geocoded and the first published data correspond to 2001. Publication of these data will allow for quantification of the effect of proximity to one or more such plant on cancer and all-cause mortality observed in nearby towns. However, as errors have been detected in the geocoding of many of the pollutant foci shown in the EPER, it was decided that a validation study should be conducted into the accuracy of these co-ordinates. EPER-Spain geographic co-ordinates were drawn from the European Environment Agency (EEA) server and the Spanish Ministry of the Environment (MOE). The Farm Plot Geographic Information System (Sistema de Información Geográfica de Parcelas Agrícolas) (SIGPAC) enables orthophotos (digitalized aerial images) of any territorial point across Spain to be obtained. Through a search of co-ordinates in the SIGPAC, all the industrial foci (except farms) were located. The quality criteria used to ascertain possible errors in industrial location were high, medium and low quality, where industries were situated at a distance of less than 500 metres, more than 500 metres but less than 1 kilometre, and more than 1 kilometre from their real locations, respectively. RESULTS: Insofar as initial registry quality was concerned, 84% of industrial complexes were inaccurately positioned (low quality) according to EEA data versus 60% for Spanish MOE data. The distribution of the distances between the original and corrected co-ordinates for each of the industries on the registry revealed that the median error was 2.55 kilometres for Spain overall (according to EEA data). The Autonomous Regions that displayed most errors in industrial geocoding were Murcia, Canary Islands, Andalusia and Madrid. Correct co-ordinates were successfully allocated to 100% of EPER-Spain industries. CONCLUSION: Knowing the exact location of pollutant foci is vital to obtain reliable and valid conclusions in any study where distance to the focus is a decisive factor, as in the case of the consequences of industrial pollution on the health of neighbouring populations.This study was funded by grant FIS 040041 from the Health Research Fund (Fondo de Investigación Sanitaria)S

Municipal distribution of bladder cancer mortality in Spain: Possible role of mining and industry

BACKGROUND: Spain shows the highest bladder cancer incidence rates in men among European countries. The most important risk factors are tobacco smoking and occupational exposure to a range of different chemical substances, such as aromatic amines. METHODS: This paper describes the municipal distribution of bladder cancer mortality and attempts to "adjust" this spatial pattern for the prevalence of smokers, using the autoregressive spatial model proposed by Besag, York and Molliè, with relative risk of lung cancer mortality as a surrogate. RESULTS: It has been possible to compile and ascertain the posterior distribution of relative risk for bladder cancer adjusted for lung cancer mortality, on the basis of a single Bayesian spatial model covering all of Spain's 8077 towns. Maps were plotted depicting smoothed relative risk (RR) estimates, and the distribution of the posterior probability of RR>1 by sex. Towns that registered the highest relative risks for both sexes were mostly located in the Provinces of Cadiz, Seville, Huelva, Barcelona and Almería. The highest-risk area in Barcelona Province corresponded to very specific municipal areas in the Bages district, e.g., Suría, Sallent, Balsareny, Manresa and Cardona. CONCLUSION: Mining/industrial pollution and the risk entailed in certain occupational exposures could in part be dictating the pattern of municipal bladder cancer mortality in Spain. Population exposure to arsenic is a matter that calls for attention. It would be of great interest if the relationship between the chemical quality of drinking water and the frequency of bladder cancer could be studied

Human placenta and markers of heavy metals exposure: Esteban-Vasallo et al. Respond

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Breast Dense Tissue Segmentation with Noisy Labels: A Hybrid Threshold-Based and Mask-Based Approach

Breast density assessed from digital mammograms is a known biomarker related to a higher risk of developing breast cancer. Supervised learning algorithms have been implemented to determine this. However, the performance of these algorithms depends on the quality of the ground-truth information, which expert readers usually provide. These expert labels are noisy approximations to the ground truth, as there is both intra- and inter-observer variability among them. Thus, it is crucial to provide a reliable method to measure breast density from mammograms. This paper presents a fully automated method based on deep learning to estimate breast density, including breast detection, pectoral muscle exclusion, and dense tissue segmentation. We propose a novel confusion matrix (CM)-YNet model for the segmentation step. This architecture includes networks to model each radiologist's noisy label and gives the estimated ground-truth segmentation as well as two parameters that allow interaction with a threshold-based labeling tool. A multi-center study involving 1785 women whose "for presentation" mammograms were obtained from 11 different medical facilities was performed. A total of 2496 mammograms were used as the training corpus, and 844 formed the testing corpus. Additionally, we included a totally independent dataset from a different center, composed of 381 women with one image per patient. Each mammogram was labeled independently by two expert radiologists using a threshold-based tool. The implemented CM-Ynet model achieved the highest DICE score averaged over both test datasets (0.82±0.14) when compared to the closest dense-tissue segmentation assessment from both radiologists. The level of concordance between the two radiologists showed a DICE score of 0.76±0.17. An automatic breast density estimator based on deep learning exhibited higher performance when compared with two experienced radiologists. This suggests that modeling each radiologist's label allows for better estimation of the unknown ground-truth segmentation. The advantage of the proposed model is that it also provides the threshold parameters that enable user interaction with a threshold-based tool.This research was partially funded by Generalitat Valenciana through IVACE (Valencian Institute of Business Competitiveness) distributed by nomination to Valencian technological innovation centres under project expedient IMDEEA/2021/100. It was also supported by grants from Instituto de Salud Carlos III FEDER (PI17/00047).S

A deep learning framework to classify breast density with noisy labels regularization

Background and objective: Breast density assessed from digital mammograms is a biomarker for higher risk of developing breast cancer. Experienced radiologists assess breast density using the Breast Image and Data System (BI-RADS) categories. Supervised learning algorithms have been developed with this objective in mind, however, the performance of these algorithms depends on the quality of the ground-truth information which is usually labeled by expert readers. These labels are noisy approximations of the ground truth, as there is often intra- and inter-reader variability among labels. Thus, it is crucial to provide a reliable method to obtain digital mammograms matching BI-RADS categories. This paper presents RegL (Labels Regularizer), a methodology that includes different image pre-processes to allow both a correct breast segmentation and the enhancement of image quality through an intensity adjustment, thus allowing the use of deep learning to classify the mammograms into BI-RADS categories. The Confusion Matrix (CM) - CNN network used implements an architecture that models each radiologist's noisy label. The final methodology pipeline was determined after comparing the performance of image pre-processes combined with different DL architectures. Methods: A multi-center study composed of 1395 women whose mammograms were classified into the four BI-RADS categories by three experienced radiologists is presented. A total of 892 mammograms were used as the training corpus, 224 formed the validation corpus, and 279 the test corpus. Results: The combination of five networks implementing the RegL methodology achieved the best results among all the models in the test set. The ensemble model obtained an accuracy of (0.85) and a kappa index of 0.71. Conclusions: The proposed methodology has a similar performance to the experienced radiologists in the classification of digital mammograms into BI-RADS categories. This suggests that the pre-processing steps and modelling of each radiologist's label allows for a better estimation of the unknown ground truth labels.This work was partially funded by Generalitat Valenciana through IVACE (Valencian Institute of Business Competitiveness) distributed nominatively to Valencian technological innovation centres under project expedient IMAMCN/2021/1.S

Dimensiones de personalidad y calidad de vida relacionada con la salud en mujeres

Incluye: PDF de la presentación y video del seminario.El objetivo del estudio es analizar la asociación entre personalidad y calidad de vida relacionada con la salud (CVRS) en una muestra de mujeres españolas. Se quiere: explorar el papel de la edad como posible factor de confusión y el papel de la Family of Origin Socio Economic Status (FOSES) como posible modificador del efecto. Las conclusiones del estudio son: 1. Niveles altos de neuroticismo afectan negativamente los componentes físico y mental de la CVRS en mujeres españolas. 2. Extraversión alta está relacionada con mejores resultados en la CVRS mental en esta población. 3. Los niveles más altos de amabilidad se asocian con: Mujeres españolas con bajo nivel de FOSES: peores resultados de CVRS física pero mejores resultados de CVRS mental y mujeres con FOSES medio-alto: peor CVRS mental.N