Allergy & Immunology

An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging.

While many diseases of aging have been linked to the immunological system, immune metrics capable of identifying the most at-risk individuals are lacking. From the blood immunome of 1,001 individuals aged 8-96 years, we developed a deep-learning method based on patterns of systemic age-related inflammation. The resulting inflammatory clock of aging (iAge) tracked with multimorbidity, immunosenesce...

Predicting HIV drug resistance using weighted machine learning method at target protein sequence-level.

Acquired immune deficiency syndrome (AIDS) is a fatal disease caused by human immunodeficiency virus (HIV). Although 23 different drugs have been available, the treatment of AIDS remains challenging because the virus mutates very quickly which can lead to drug resistance. Therefore, predicting drug resistance before treatment is crucial for individual treatments. Here, based on HIV target protein ...

Towards automatic airborne pollen monitoring: From commercial devices to operational by mitigating class-imbalance in a deep learning approach.

Allergic diseases have been the epidemic of the century among chronic diseases. Particularly for pollen allergies, and in the context of climate chang...

From everyday life predictions to suicide prevention: Clinical and ethical considerations in suicide predictive analytic tools.

Advances in artificial intelligence and machine learning have fueled growing interest in the application of predictive analytics to identify high-risk...

Immune-Based Prediction of COVID-19 Severity and Chronicity Decoded Using Machine Learning.

Expression of CCR5 and its cognate ligands have been implicated in COVID-19 pathogenesis, consequently therapeutics directed against CCR5 are being in...

Development of a fluorescent immunochromatographic assay based on quantum dots for the detection of fleroxacin.

Fleroxacin (FLE) is a broad-spectrum fluoroquinolone antibiotic widely used in animal husbandry, veterinary medicine and aquaculture. Eating animal-de...

Deep learning of HIV field-based rapid tests.

Although deep learning algorithms show increasing promise for disease diagnosis, their use with rapid diagnostic tests performed in the field has not ...

Profiling single-cell level phagocytic activity distribution with blood lactate levels.

The ability to kill infecting microbes is an essential facet of our immune response to an infection. However, phagocytic ability is often overlooked a...

Human breast milk-based nutritherapy: A blueprint for pediatric healthcare.

Human Breast Milk (HBM) is a storehouse of micronutrients, macronutrients, immune factors, microbiota and numerous other bioactive macromolecules. Ful...

Associations between trees and grass presence with childhood asthma prevalence using deep learning image segmentation and a novel green view index.

Limitations of Normalized Difference Vegetation Index (NDVI) potentially contributed to the inconsistent findings of greenspace exposure and childhood...

Artificial Intelligence and Cellular Segmentation in Tissue Microscopy Images.

With applications in object detection, image feature extraction, image classification, and image segmentation, artificial intelligence is facilitating...

Augmenting the Transplant Team With Artificial Intelligence: Toward Meaningful AI Use in Solid Organ Transplant.

Advances in systems immunology, such as new biomarkers, offer the potential for highly personalized immunosuppression regimens that could improve pati...

Does machine learning have a role in the prediction of asthma in children?

Asthma is the most common chronic lung disease in childhood. There has been a significant worldwide effort to develop tools/methods to identify childr...

The application of artificial intelligence methods to gene expression data for differentiation of uncomplicated and complicated appendicitis in children and adolescents - a proof of concept study.

BACKGROUND: Genome wide gene expression analysis has revealed hints for independent immunological pathways underlying the pathophysiologies of phlegmo...

Design Comorbidity Portfolios to Improve Treatment Cost Prediction of Asthma Using Machine Learning.

Comorbidity is an important factor to consider when trying to predict the cost of treating asthma patients. When an asthmatic patient suffered from co...

Artificial Intelligence and Machine Learning in Chronic Airway Diseases: Focus on Asthma and Chronic Obstructive Pulmonary Disease.

Chronic airway diseases are characterized by airway inflammation, obstruction, and remodeling and show high prevalence, especially in developing count...

Neural networks for increased accuracy of allergenic pollen monitoring.

Monitoring of airborne pollen concentrations provides an important source of information for the globally increasing number of hay fever patients. Air...

Developing a short-term prediction model for asthma exacerbations from Swedish primary care patients' data using machine learning - Based on the ARCTIC study.

OBJECTIVE: The ability to predict impending asthma exacerbations may allow better utilization of healthcare resources, prevention of hospitalization a...

COVID-19 diagnosis by routine blood tests using machine learning.

Physicians taking care of patients with COVID-19 have described different changes in routine blood parameters. However, these changes hinder them from...

Exploration of machine learning techniques to examine the journey to neuroendocrine tumor diagnosis with real-world data.

Machine learning reveals pathways to neuroendocrine tumor (NET) diagnosis. Patients with NET and age-/gender-matched non-NET controls were retrospec...