Allergy

Stem-cell based, machine learning approach for optimizing natural killer cell-based personalized immunotherapy for high-grade ovarian cancer.

Advanced high-grade serous ovarian cancer continues to be a therapeutic challenge for those affected using the current therapeutic interventions. There is an increasing interest in personalized cancer immunotherapy using activated natural killer (NK) cells. NK cells account for approximately 15% of circulating white blood cells. They are also an important element of the tumor microenvironment (TME...

Automation of penicillin adverse drug reaction categorisation and risk stratification with machine learning natural language processing.

BACKGROUND: The penicillin adverse drug reaction (ADR) label is common in electronic health records (EHRs). However, there is significant misclassification between allergy and intolerance within the EHR and most patients can be delabelled after an immunologic assessment. Machine learning natural language processing may be able to assist with the categorisation and risk stratification of penicillin...

Risk factor assessments of temporomandibular disorders via machine learning.

This study aimed to use artificial intelligence to determine whether biological and psychosocial factors, such as stress, socioeconomic status, and wo...

Rapid, label-free classification of tumor-reactive T cell killing with quantitative phase microscopy and machine learning.

Quantitative phase microscopy (QPM) enables studies of living biological systems without exogenous labels. To increase the utility of QPM, machine-lea...

Allergen Risk Assessment for Specific Allergy to Small Ruminant's Milk: Development of Sensitive Immunoassays to Detect Goat's and Sheep's Milk Contaminations in Dairy Food Matrices.

Despite a high level of sequence identity between cow's, goat's, and sheep's milk (CM, GM, and SM, respectively) proteins, some patients tolerant to C...

T Cell Epitope Prediction and Its Application to Immunotherapy.

T cells play a crucial role in controlling and driving the immune response with their ability to discriminate peptides derived from healthy as well as...

Diagnostic classification of coronavirus disease 2019 (COVID-19) and other pneumonias using radiomics features in CT chest images.

We propose a classification method using the radiomics features of CT chest images to identify patients with coronavirus disease 2019 (COVID-19) and o...

Deep Learning-Based High-Frequency Ultrasound Skin Image Classification with Multicriteria Model Evaluation.

This study presents the first application of convolutional neural networks to high-frequency ultrasound skin image classification. This type of imagin...

Deep learning-based detection of eosinophilic esophagitis.

BACKGROUND: For eosinophilic esophagitis (EoE), a substantial diagnostic delay is still a clinically relevant phenomenon. Deep learning-based algorith...

NIgPred: Class-Specific Antibody Prediction for Linear B-Cell Epitopes Based on Heterogeneous Features and Machine-Learning Approaches.

Upon invasion by foreign pathogens, specific antibodies can identify specific foreign antigens and disable them. As a result of this ability, antibodi...

Systems biology informed neural networks (SBINN) predict response and novel combinations for PD-1 checkpoint blockade.

Anti-PD-1 immunotherapy has recently shown tremendous success for the treatment of several aggressive cancers. However, variability and unpredictabili...

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...

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...

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...

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...

Aging-related markers in rat urine revealed by dynamic metabolic profiling using machine learning.

The process of aging and metabolism is intimately intertwined; thus, developing biomarkers related to metabolism is critical for delaying aging. Howev...

Deep Learning Methods for Improving Pollen Monitoring.

The risk of pollen-induced allergies can be determined and predicted based on data derived from pollen monitoring. Hirst-type samplers are sensors tha...

Deep learning for predicting COVID-19 malignant progression.

As COVID-19 is highly infectious, many patients can simultaneously flood into hospitals for diagnosis and treatment, which has greatly challenged publ...

Conjunctival Provocation Test With .

Conjunctival provocation test (CPT) is used to demonstrate clinical relevance to a specific allergen. (Bt) is a prevalent allergen in tropical regio...

Prioritizing Molecular Biomarkers in Asthma and Respiratory Allergy Using Systems Biology.

Highly prevalent respiratory diseases such as asthma and allergy remain a pressing health challenge. Currently, there is an unmet need for precise dia...