The influence of the carrier molecule on amoxicillin recognition by specific IgE in patients with immediate hypersensitivity reactions to betalactams

10 p.-4 fig.-1 tab.The optimal recognition of penicillin determinants, including amoxicillin (AX), by specific IgE antibodies is widely believed to require covalent binding to a carrier molecule. The nature of the carrier and its contribution to the antigenic determinant is not well known. Here we aimed to evaluate the specific-IgE recognition of different AX-derived structures. We studied patients with immediate hypersensitivity reactions to AX, classified as selective or cross-reactors to penicillins. Competitive immunoassays were performed using AX itself, amoxicilloic acid, AX bound to butylamine (AXO-BA) or to human serum albumin (AXO-HSA) in the fluid phase, as inhibitors, and amoxicilloyl-poli-L-lysine (AXO-PLL) in the solid-phase. Two distinct patterns of AX recognition by IgE were found: Group A showed a higher recognition of AX itself and AX-modified components of low molecular weights, whilst Group B showed similar recognition of both unconjugated and conjugated AX. Amoxicilloic acid was poorly recognized in both groups, which reinforces the need for AX conjugation to a carrier for optimal recognition. Remarkably, IgE recognition in Group A (selective responders to AX) is influenced by the mode of binding and/or the nature of the carrier; whereas IgE in Group B (cross-responders to penicillins) recognizes AX independently of the nature of the carrier.The present study has been supported by Institute of Health “Carlos III” of the Ministry of Economy and Competitiveness (grants cofunded by European Regional Development Fund (ERDF): PI12/02529, PI15/01206, CP15/00103,Red de Reacciones Adversas a Alergenos y Farmacos RD12/0013/0001, RD12/0013/0003 and RD12/0013/0008,RD09/0076/00112 for the Biobank network and PT13/0010/0006 for the Biobank platform) and by State Secretariat for Research, Development and Innovation of the Ministry of Economy and Competitiveness (grants cofunded by European Regional Development Fund (ERDF): MINECO SAF2012-36519, SAF2015-68590-R/FEDER and CTQ2013-41339-P). Andalusian Regional Ministry of Economy and Knowledge (grants cofunded by European Regional Development Fund (ERDF): CTS-06603); Andalusian Regional Ministry Health (grants:PI-0699-2011, PI-0159-2013 and PI-0179-2014) and Merck-Serono Research Grant from Fundación Salud 2000. CM holds a ‘Nicolas Monardes’ research contract by Andalusian Regional Ministry Health: C-0044-2012 SAS 2013. MIM holds a ‘Miguel Servet I’ research contract by Institute of Health “Carlos III” of the Ministry of Economy and Competitiveness (grants cofunded by European Social Fund (ESF)): CP15/00103. AA thanks “pFIS fellowship” (FI08/00385) from ISCIII and Andalucia “Talent Hub Fellowship” (TAHUB/II-004) cofunded by the Junta de Andalucia and the European Union, VII Framework Programme of the European Commission (grant agreement No. 291780).Peer reviewe

Evidence of autochthonous transmission of urinary schistosomiasis in Almeria (southeast Spain): An outbreak analysis

Background: Schistosomiasis is endemic in 78 countries belonging to tropical and subtropical areas. However, autochthonous transmission of urogenital schistosomiasis was reported in Corsica (France) in 2013. We present evidence of autochthonous transmission of urogenital schistosomiasis in Almería (Spain) in 2003. Methods: Description of the outbreak in farmers and subsequent epidemiological studies aimed at searching for Bulinus snails and their genotypic characteristics. Results: The outbreak affected 4 farmers out of a group of 5 people who repeatedly bathed that summer in an irrigation pool in the area. Two of them presented macroscopic hematuria with bilharziomas, showing the presence of Schistosoma eggs in bladder biopsies. Two others were asymptomatic but the serology for schisto somiasis was positive. In 2015, the presence of the vector Bulinus truncatus was demonstrated in Almería in water collections of appropriate characteristics. DNA sequencing proving that local B. truncatus species were base-to base identical to B. truncatus from Senegal. Conclusions: We present a new outbreak of autochthonous transmission of urogenital schistosomiasis in Europe. Although no new cases of autochthonous transmission have been reported, some other cases may have occurred at that time or later on and be unnoticed as many cases of schistosomiasis are asymptomatic or present mild and unspecific symptoms

Global parenchymal texture features based on histograms of oriented gradients improve cancer development risk estimation from healthy breasts

[EN] Background The breast dense tissue percentage on digital mammograms is one of the most commonly used markers for breast cancer risk estimation. Geometric features of dense tissue over the breast and the presence of texture structures contained in sliding windows that scan the mammograms may improve the predictive ability when combined with the breast dense tissue percentage. Methods A case/control study nested within a screening program covering 1563 women with craniocaudal and mediolateral-oblique mammograms (755 controls and the contralateral breast mammograms at the closest screening visit before cancer diagnostic for 808 cases) aging 45 to 70 from Comunitat Valenciana (Spain) was used to extract geometric and texture features. The dense tissue segmentation was performed using DMScan and validated by two experienced radiologists. A model based on Random Forests was trained several times varying the set of variables. A training dataset of 1172 patients was evaluated with a 10-stratified-fold cross-validation scheme. The area under the Receiver Operating Characteristic curve (AUC) was the metric for the predictive ability. The results were assessed by only considering the output after applying the model to the test set, which was composed of the remaining 391 patients. Results The AUC score obtained by the dense tissue percentage (0.55) was compared to a machine learning-based classifier results. The classifier, apart from the percentage of dense tissue of both views, firstly included global geometric features such as the distance of dense tissue to the pectoral muscle, dense tissue eccentricity or the dense tissue perimeter, obtaining an accuracy of 0.56. By the inclusion of a global feature based on local histograms of oriented gradients, the accuracy of the classifier was significantly improved (0.61). The number of well-classified patients was improved up to 236 when it was 208. Conclusion Relative geometric features of dense tissue over the breast and histograms of standardized local texture features based on sliding windows scanning the whole breast improve risk prediction beyond the dense tissue percentage adjusted by geometrical variables. Other classifiers could improve the results obtained by the conventional Random Forests used in this study.This work was partially funded by Generalitat Valenciana through I+D IVACE (Valencian Institute of Business Competitiviness) and GVA (European Regional Development Fund) supports under the project IMAMCN/2018/1, and by Carlos III Institute of Health under the project DTS15/00080Pérez-Benito, FJ.; Signol, F.; Perez-Cortes, J.; Pollán, M.; Perez-Gómez, B.; Salas-Trejo, D.; Casals, M.... (2019). Global parenchymal texture features based on histograms of oriented gradients improve cancer development risk estimation from healthy breasts. Computer Methods and Programs in Biomedicine. 177:123-132. https://doi.org/10.1016/j.cmpb.2019.05.022S12313217

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

¿Quién es quién en el cementerio matemático?

Experiencia que ha permitido trabajar de forma interdisciplinar a los departamentos de inglés, francés, plástica y matemáticas del I.E.S. sierra minera de la unión (Murcia), con el objetivo de mejorar las actitudes y las capacidades de los alumnos en matemáticas. Surge como actividad a realizar en halloween y consiste en la recreación de un cementerio con la peculiaridad de que los difuntos son matemáticos famosos. Por un lado, en cada tumba se puede leer el nombre, fecha de nacimiento y fecha de muerte de un matemático, y por otro lado en las respectivas placas un epitafio en inglés y francés. Los alumnos visitan el cementerio y deben rellenar un cuestionario para saber quién es quién

High mammographic density in long-term night shift workers: DDM-Spain /Var-DDM

[EN] Background: Night-shift work (NSW) has been suggested as a possible cause of breast cancer, and its association with mammographic density (MD), one of the strongest risk factors for breast cancer, has been scarcely addressed. This study examined NSW and MD in Spanish women. Methods: The study covered 2,752 women aged 45-68 years recruited in 2007-2008 in 7 population-based public breast cancer screening centers, which included 243 women who had performed NSW for at least one year. Occupational data and information on potential confounders were collected by personal interview. Two trained radiologist estimated the percentage of MD assisted by a validated semiautomatic computer tool (DM-scan). Multivariable mixed linear regression models with random screening center-specific intercepts were fitted using log-transformed percentage of MD as the dependent variable and adjusting by known confounding variables. Results: Having ever worked in NSW was not associated with MD [e(beta):0.96; 95% confidence interval (CI), 0.86-1.06]. However, the adjusted geometric mean of the percentage of MD in women with NSW for more than 15 years was 25% higher than that of those without NSW history (MD>15 (years):20.7% vs. MDnever:16.5%; e(beta):1.25; 95% CI, 1.01-1.54). This association was mainly observed in postmenopausal participants (e(beta):1.28; 95% CI, 1.00-1.64). Among NSW-exposed women, those with <= 2 night-shifts per week had higher MD than those with 5 to 7 nightshifts per week (e(beta):1.42; 95% CI, 1.10-1.84). Conclusions: Performing NSW was associated with higherMD only in women with more than 15 years of cumulated exposure. These findings warrant replication in futures studies. (C)2017 AACR.We would like to thank the participants in the DDM-Spain/Var-DDM-Spain study for their contribution to breast cancer research. Other members of DDM-Spain/Var-DDM: Gonzalo. López-Abente, Roberto Pastor-Barriuso, Pablo Fernández-Navarro, Anna Cabanes, Soledad Laso, Manuela Alcaraz, María Casals, Inmaculada Martínez, Juan Carlos Pérez Cortés, Joaquín Antón, Nieves Ascunce, Isabel González-Román, Ana Belén Fernández, Montserrat Corujo, Soledad Abad, and Jesús Vioque. A.M. Pedraza-Flechas FI14CIII/00013 PFIS; B. Perez-Gomez FIS PS09/0790; M. Pollán FIS PI060386, EPY1306/06 collaboration agreement between Astra-Zeneca and ISCIII, and FECMA 485 EPY 1170 10; R. LLobet Gent per Gent Fund (EDEMAC Project); All authors: European Regional Development Fund (FEDER). The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.Pedraza-Flechas, AM.; Lope, V.; Sanchez-Contador, C.; Santamarina, C.; Pedraz-Pingarron, C.; Moreo, P.; Ederra, M.... (2017). High mammographic density in long-term night shift workers: DDM-Spain /Var-DDM. Cancer Epidemiology Biomarkers & Prevention. 26(6):905-913. https://doi.org/10.1158/1055-9965.EPI-16-0507S90591326

Overeating, caloric restriction and mammographic density in Spanish women. DDM-Spain study

Objectives: Mammographic density (MD) is a strong risk factor for breast cancer. The present study evaluates the association between relative caloric intake and MD in Spanish women. Study design: We conducted a cross-sectional study in which 3517 women were recruited from seven breast cancer screening centers. MD was measured by an experienced radiologist using craniocaudal mammography and Boyd's semi-quantitative scale. Information was collected through an epidemiological survey. Predicted calories were calculated using linear regression models, including the basal metabolic rate and physical activity as explanatory variables. Overeating and caloric restriction were defined taking into account the 99% confidence interval of the predicted value. Odds ratios (OR) and 95% confidence intervals (95%CI) were estimated using center-specific mixed ordinal logistic regression models, adjusted for age, menopausal status, body mass index, parity, tobacco use, family history of breast cancer, previous biopsies, age at menarche and adherence to a Western diet. Main outcome measure: Mammographic density. Results: Those women with an excessive caloric intake ( > 40% above predicted) presented higher MD (OR = 1.41, 95%CI = 0.97-2.03; p = 0.070). For every 20% increase in relative caloric consumption the probability of having higher MD increased by 5% (OR = 1.05, 95%CI = 0.98-1.14; p = 0.178), not observing differences between the categories of explanatory variables. Caloric restriction was not associated with MD in our study. Conclusions: This is the first study exploring the association between MD and the effect of caloric deficit or excessive caloric consumption according to the energy requirements of each woman. Although caloric restriction does not seem to affect breast density, a caloric intake above predicted levels seems to increase this phenotype

Málaga: Desde Málaga a playa del Ejido (Málaga) Serie B: Gestión.

Peer reviewe

Sleep patterns, sleep disorders and mammographic density in spanish women: The DDM-Spain/Var-DDM study

[EN] We explored the relationship between sleep patterns and sleep disorders and mammographic density (MD), a marker of breast cancer risk. Participants in the DDM-Spain/var-DDM study, which included 2878 middle-aged Spanish women, were interviewed via telephone and asked questions on sleep characteristics. Two radiologists assessed MD in their left craneo-caudal mammogram, assisted by a validated semiautomatic-computer tool (DM-scan). We used log-transformed percentage MD as the dependent variable and fitted mixed linear regression models, including known confounding variables. Our results showed that neither sleeping patterns nor sleep disorders were associated with MD. However, women with frequent changes in their bedtime due to anxiety or depression had higher MD (e¿:1.53;95%CI:1.04¿2.26).This work was supported by grants from the Spanish Ministry of Economy and Competitiveness - Carlos III Institute of Health (ISCIII) (FI14CIII/00013, FIS PI060386 & PS09/0790), from the Spanish Federation of Breast Cancer Patients (FECMA 485 EPY 1170-10), Gent per Gent Fund (EDEMAC Project), the EPY1306/06 collaboration agreement between Astra-Zeneca and the ISCIII and partially funded by the European Regional Development Fund (FEDER)Pedraza-Flechas, AM.; Lope, V.; Moreo, P.; Ascunce, N.; Miranda-García, J.; Vidal, C.; Sánchez-Contador, C.... (2017). Sleep patterns, sleep disorders and mammographic density in spanish women: The DDM-Spain/Var-DDM study. Maturitas. 99:105-108. https://doi.org/10.1016/j.maturitas.2017.02.015S1051089