The impact of type 2 immunity and allergic diseases in atherosclerosis.

Allergic diseases are allergen-induced immunological disorders characterized by the development of type 2 immunity and IgE responses. The prevalence of allergic diseases has been on the rise alike cardiovascular disease (CVD), which affects arteries of different organs such as the heart, the kidney and the brain. The underlying cause of CVD is often atherosclerosis, a disease distinguished by endothelial dysfunction, fibrofatty material accumulation in the intima of the artery wall, smooth muscle cell proliferation, and Th1 inflammation. The opposed T-cell identity of allergy and atherosclerosis implies an atheroprotective role for Th2 cells by counteracting Th1 responses. Yet, the clinical association between allergic disease and CVD argues against it. Within, we review different phases of allergic pathology, basic immunological mechanisms of atherosclerosis and the clinical association between allergic diseases (particularly asthma, atopic dermatitis, allergic rhinitis and food allergy) and CVD. Then, we discuss putative atherogenic mechanisms of type 2 immunity and allergic inflammation including acute allergic reactions (IgE, IgG1, mast cells, macrophages and allergic mediators such as vasoactive components, growth factors and those derived from the complement, contact and coagulation systems) and late phase inflammation (Th2 cells, eosinophils, type 2 innate-like lymphoid cells, alarmins, IL-4, IL-5, IL-9, IL-13 and IL-17).post-print4164 K

Enlightening human B-cell diversity.

pre-print193 K

Oral immunotherapy be heated ovomuciod-reduced egg white in a Balb/C mouse model

Resumen del trabajo presentado al Food Allergy and Anaphylaxis Meeting (FAAM-2011) celebrado en Venecia.Peer Reviewe

Thinking small: zinc-sensing by the gut epithelium.

pre-print341 K

From 12 to 1 ECG lead: multiple cardiac condition detection mixing a hybrid machine learning approach with a one-versus-rest classification strategy

Objective. Detecting different cardiac diseases using a single or reduced number of leads is still challenging. This work aims to provide and validate an automated method able to classify ECG recordings. Performance using complete 12-lead systems, reduced lead sets, and single-lead ECGs is evaluated and compared.Approach. Seven different databases with 12-lead ECGs were provided during thePhysioNet/Computing in Cardiology Challenge2021, where 88 253 annotated samples associated with none, one, or several cardiac conditions among 26 different classes were released for training, whereas 42 896 hidden samples were used for testing. After signal preprocessing, 81 features per ECG-lead were extracted, mainly based on heart rate variability, QRST patterns and spectral domain. Next, a One-versus-Rest classification approach made of independent binary classifiers for each cardiac condition was trained. This strategy allowed each ECG to be classified as belonging to none, one or several classes. For each class, a classification model among two binary supervised classifiers and one hybrid unsupervised-supervised classification system was selected. Finally, we performed a 3-fold cross-validation to assess the system's performance.Main results. Our classifiers received scores of 0.39, 0.38, 0.39, 0.38, and 0.37 for the 12, 6, 4, 3 and 2-lead versions of the hidden test set with the Challenge evaluation metric (CM). Also, we obtained a meanG-score of 0.80, 0.78, 0.79, 0.79, 0.77 and 0.74 for the 12, 6, 4, 3, 2 and 1-lead subsets with the public training set during our 3-fold cross-validation.Significance. We proposed and tested a machine learning approach focused on flexibility for identifying multiple cardiac conditions using one or more ECG leads. Our minimal-lead approach may be beneficial for novel portable or wearable ECG devices used as screening tools, as it can also detect multiple and concurrent cardiac conditions

In vitro digestibility and allergenicity of emulsified hen egg

Whole hen egg produced a fine stable O/W emulsion. The presence of egg proteins as part of the emulsion did not change their IgE binding, but it slightly increased the digestibility of the main allergens present in the egg-white. The observation that egg white proteins, forming part of an emulsion system did not become a much more effective substrate for pepsin indicates that, in the case of egg white proteins, there were not adsorption-induced changes that would considerably increase their flexibility and proteinase susceptibility.The increased digestibility of the emulsion resulted in a slightly lower IgE-binding capacity of the in vitro gastric and duodenal digests compared to those obtained from the egg in solution.Fil: Jiménez Saiz, Rodrigo. Consejo Superior de Investigaciones Científicas; EspañaFil: Pizones Ruiz Henestrosa, Víctor Manuel. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Industrias; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: López Fandiño, Rosina. Consejo Superior de Investigaciones Científicas; EspañaFil: Molina, Elena. Consejo Superior de Investigaciones Científicas; Españ

From 12 to 1 ECG lead: multiple cardiac condition detection mixing a hybrid machine learning approach with a one-vs-rest classification strategy

[EN] Objective: Detecting different cardiac diseases using a single or reduced number of leads is still challenging. This work aims to provide and validate an automated method able to classify ECG recordings. Performance using complete 12-lead systems, reduced lead sets, and single-lead ECGs is evaluated and compared. Approach: Seven different databases with 12-lead ECGs were provided during the PhysioNet/Computing in Cardiology Challenge 2021, where 88,253 annotated samples associated with none, one, or several cardiac conditions among 26 different classes were released for training, whereas 42,896 hidden samples were used for testing. After signal preprocessing, 81 features per ECG-lead were extracted, mainly based on heart rate variability, QRST patterns and spectral domain. Next, a One-vs-Rest classification approach made of independent binary classifiers for each cardiac condition was trained. This strategy allowed each ECG to be classified as belonging to none, one or several classes. For each class, a classification model among two binary Supervised Classifiers and one Hybrid Unsupervised-Supervised classification system was selected. Finally, we performed a 3-fold cross-validation to assess the system's performance. Main results: Our classifiers received scores of 0.39, 0.38, 0.39, 0.38, and 0.37 for the 12, 6, 4, 3 and 2-lead versions of the hidden test set with the Challenge evaluation metric (CM). Also, we obtained a mean G-score of 0.80, 0.78, 0.79, 0.79, 0.77 and 0.74 for the 12, 6, 4, 3, 2 and 1-lead subsets with the public training set during our 3-fold cross-validation. Significance: We proposed and tested a machine learning approach focused on flexibility for identifying multiple cardiac conditions using one or more ECG leads. Our minimal-lead approach may be beneficial for novel portable or wearable ECG devices used as screening tools, as it can also detect multiple and concurrent cardiac conditions.This work was supported by PID2019-109547RB-I00 (National Research Program, Ministerio de Ciencia e Innovación, Spanish Government) and CIBERCV CB16/11/00486 (Instituto de Salud Carlos III).Jiménez-Serrano, S.; Rodrigo, M.; Calvo Saiz, CJ.; Millet Roig, J.; Castells, F. (2022). From 12 to 1 ECG lead: multiple cardiac condition detection mixing a hybrid machine learning approach with a one-vs-rest classification strategy. Physiological Measurement. 43(6):1-17. https://doi.org/10.1088/1361-6579/ac72f511743

Multiple Cardiac Disease Detection from Minimal-Lead ECG Combining Feedforward Neural Networks with a One-vs-Rest Approach

[EN] Although standard 12-lead ECG is the primary technique in cardiac diagnostic, detecting different cardiac diseases using single or reduced number of leads is still challenging. The purpose of our team, itaca-UPV, is to provide a method able to classify ECG records using minimal lead information in the context of the 2021 PhysioNet/Computing in Cardiology Challenge, also using only a single-lead. We resampled and filtered the ECG signals, and extracted 109 features mostly based on Hearth Rhythm Variability (HRV). Then, we used selected features to train one feed-forward neural network (FFNN) with one hidden layer for each class using a One-vs-Rest approach, thus allowing each ECG to be classified as belonging to none or more than one class. Finally, we performed a 3-fold cross validation to assess the model performance. Our classifiers received scores of 0.34, 0.34, 0.27, 0.30, and 0.34 (ranked 26th, 21th, 29th, 25th, and 22th out of 39 teams) for the 12, 6, 4, 3 and 2-lead versions of the hidden test set with the Challenge evaluation metric. Our minimal-lead approach may be beneficial for novel portable or wearable ECG devices used as screening tools, as it can also detect multiple and concurrent cardiac conditions. Accuracy in detection can be improved adding more disease-specific features.Jiménez-Serrano, S.; Rodrigo Bort, M.; Calvo Saiz, CJ.; Castells, F.; Millet Roig, J. (2021). Multiple Cardiac Disease Detection from Minimal-Lead ECG Combining Feedforward Neural Networks with a One-vs-Rest Approach. 1-4. https://doi.org/10.22489/CinC.2021.1091

Meglumine gadoterate induces immunoglobulin-independent human mast cell activation via MRGPRX2.

post-print2297 K

Oral Immunotherapy for Food-Allergic Children: A Pro-Con Debate.

The prevalence of food allergy has increased in recent years, especially in children. Allergen avoidance, and drugs in case of an allergic reaction, remains the standard of care in food allergy. Nevertheless, increasing attention has been given to the possibility to treat food allergy, through immunotherapy, particularly oral immunotherapy (OIT). Several OIT protocols and clinical trials have been published. Most of them focus on children allergic to milk, egg, or peanut, although recent studies developed protocols for other foods, such as wheat and different nuts. OIT efficacy in randomized controlled trials is usually evaluated as the possibility for patients to achieve desensitization through the consumption of an increasing amount of a food allergen, while the issue of a possible long-term sustained unresponsiveness has not been completely addressed. Here, we evaluated current pediatric OIT knowledge, focusing on the results of clinical trials and current guidelines. Specifically, we wanted to highlight what is known in terms of OIT efficacy and effectiveness, safety, and impact on quality of life. For each aspect, we reported the pros and the cons, inferable from published literature. In conclusion, even though many protocols, reviews and meta-analysis have been published on this topic, pediatric OIT remains a controversial therapy and no definitive generalized conclusion may be drawn so far. It should be an option provided by specialized teams, when both patients and their families are prone to adhere to the proposed protocol. Efficacy, long-term effectiveness, possible role of adjuvant therapies, risk of severe reactions including anaphylaxis or eosinophilic esophagitis, and impact on the quality of life of both children and caregivers are all aspects that should be discussed before starting OIT. Future studies are needed to provide firm clinical and scientific evidence, which should also consider patient reported outcomes.post-print6382 K