Allergy & Immunology

Improving classification of pollen grain images of the POLEN23E dataset through three different applications of deep learning convolutional neural networks.

In palynology, the visual classification of pollen grains from different species is a hard task which is usually tackled by human operators using microscopes. Its complete automatization would save a high quantity of resources and provide valuable improvements especially for allergy-related information systems, but also for other application fields as paleoclimate reconstruction, quality control o...

Study on screening potential allergenic proteins from infant milk powders based on human mast cell membrane chromatography and histamine release assays.

Cow's milk allergy is mainly observed in infants and young children. Most allergic reactions affect the skin, followed by the gastrointestinal and respiratory systems. Conventional diagnosis is based on positive allergy studies and evaluation of parameters including IgE and IgG1 levels, acute allergic skin response and anaphylactic shock reactions. We developed a cell membrane chromatographic (CMC...

Predictive pollen-based biome modeling using machine learning.

This paper investigates suitability of supervised machine learning classification methods for classification of biomes using pollen datasets. We assig...

Deep Convolutional Neural Networks for Chest Diseases Detection.

Chest diseases are very serious health problems in the life of people. These diseases include chronic obstructive pulmonary disease, pneumonia, asthma...

PIP-EL: A New Ensemble Learning Method for Improved Proinflammatory Peptide Predictions.

Proinflammatory cytokines have the capacity to increase inflammatory reaction and play a central role in first line of defence against invading pathog...

Catechin rich butanol fraction extracted from Acacia catechu L. (a thirst quencher) exhibits immunostimulatory potential.

Acacia catechu L., (Fabaceae) named as "catechu" is a plant, the decoction of heartwood of which is daily consumed as thirst quencher by a good percen...

Machine learning approaches for predicting disposition of asthma and COPD exacerbations in the ED.

OBJECTIVE: The prediction of emergency department (ED) disposition at triage remains challenging. Machine learning approaches may enhance prediction. ...

Predicted airway obstruction distribution based on dynamical lung ventilation data: A coupled modeling-machine learning methodology.

In asthma and chronic obstructive pulmonary disease, some airways of the tracheobronchial tree can be constricted, from moderate narrowing up to closu...

Identification of differentially expressed genes associated with asthma in children based on the bioanalysis of the regulatory network.

Asthma, the most common chronic respiratory tract disease in children, is characterized by allergy, recurring airway obstruction and bronchospasm. The...

A Nasal Brush-based Classifier of Asthma Identified by Machine Learning Analysis of Nasal RNA Sequence Data.

Asthma is a common, under-diagnosed disease affecting all ages. We sought to identify a nasal brush-based classifier of mild/moderate asthma. 190 subj...

Modeling asynchronous event sequences with RNNs.

Sequences of events have often been modeled with computational techniques, but typical preprocessing steps and problem settings do not explicitly addr...

Productive performance, egg quality, hematological parameters and serum chemistry of laying hens fed diets supplemented with certain fat-soluble vitamins, individually or combined, during summer season.

This present study aimed to determine the efficacy of supplementing layer diets with vitamin A (0, 8,000 and 16,000 IU/kg diet) and vitamin E (0, 250 ...

Prediction of DNase I hypersensitive sites in plant genome using multiple modes of pseudo components.

DNase I hypersensitive sites (DHSs) are accessible chromatin zones hypersensitive to DNase I endonucleases in plant genome. DHSs have been used as mar...

Multichannel lung sound analysis for asthma detection.

BACKGROUND AND OBJECTIVE: Lung sound signals convey valuable information of the lung status. Auscultation is an effective technique to appreciate the ...

Computational Strategies for Dissecting the High-Dimensional Complexity of Adaptive Immune Repertoires.

The adaptive immune system recognizes antigens an immense array of antigen-binding antibodies and T-cell receptors, the immune repertoire. The interr...

Automated chart review utilizing natural language processing algorithm for asthma predictive index.

BACKGROUND: Thus far, no algorithms have been developed to automatically extract patients who meet Asthma Predictive Index (API) criteria from the Ele...

Mixed Th2 and non-Th2 inflammatory pattern in the asthma-COPD overlap: a network approach.

INTRODUCTION: The asthma-chronic obstructive pulmonary disease (COPD) overlap (ACO) is a clinical condition that combines features of those two diseas...

Ascertainment of asthma prognosis using natural language processing from electronic medical records.

NLP algorithm successfully determined asthma prognosis (i.e., no remission, long-term remission, and intermittent remission) by taking into account as...

Dew point temperature affects ascospore release of allergenic genus Leptosphaeria.

The genus Leptosphaeria contains numerous fungi that cause the symptoms of asthma and also parasitize wild and crop plants. In search of a robust and ...

Predicting non-melanoma skin cancer via a multi-parameterized artificial neural network.

Ultraviolet radiation (UVR) exposure and family history are major associated risk factors for the development of non-melanoma skin cancer (NMSC). The ...