AIMC Topic: Eosinophils
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The Role of Eosinophils, Eosinophil-Related Cytokines and AI in Predicting Immunotherapy Efficacy in NSCLC Cancer.
Biomolecules
Mar 27, 2025
Immunotherapy and chemoimmunotherapy are standard treatments for non-oncogene-addicted advanced non-small cell lung cancer (NSCLC). Currently, a limited number of biomarkers, including programmed death-ligand 1 (PD-L1) expression, microsatellite inst...
A preliminary review of the utility of artificial intelligence to detect eosinophilic chronic rhinosinusitis.
International forum of allergy & rhinology
Oct 10, 2024
While typically diagnosed with biopsy, ECRS may be predicted preoperatively with the use of AI. Various AI models have been used, with pooled sensitivity of 0.857 and specificity of 0.850. We found no statistically significant difference between the ...
Quantitative characterization of eosinophilia in nasal polyps with AI-based single cell classification.
International forum of allergy & rhinology
Sep 20, 2024
Eosinophilic granulocytes have characteristic morphological features. This makes them prime candidates for utilization of a single cell binary classification network. Single cell binary classification networks can reliably help quantify eosinophils i...
Machine learning-based identification and characterization of mast cells in eosinophilic esophagitis.
The Journal of allergy and clinical immunology
Feb 22, 2024
BACKGROUND: Eosinophilic esophagitis (EoE) is diagnosed and monitored using esophageal eosinophil levels; however, EoE also exhibits a marked, understudied esophageal mastocytosis.
Addressing diagnostic dilemmas in eosinophilic esophagitis using esophageal epithelial eosinophil-derived neurotoxin.
Journal of pediatric gastroenterology and nutrition
Dec 27, 2023
OBJECTIVES: Eosinophil-derived neurotoxin (EDN) is a viable marker of eosinophilic esophagitis (EoE) disease activity. We studied the utility of measuring EDN from esophageal epithelial brushings for diagnosing EoE, focusing on two scenarios: (1) cas...
Automatic generation of artificial images of leukocytes and leukemic cells using generative adversarial networks (syntheticcellgan).
Computer methods and programs in biomedicine
Dec 15, 2022
BACKGROUND AND OBJECTIVES: Visual analysis of cell morphology has an important role in the diagnosis of hematological diseases. Morphological cell recognition is a challenge that requires experience and in-depth review by clinical pathologists. Withi...
Deep learning-based prediction of treatment prognosis from nasal polyp histology slides.
International forum of allergy & rhinology
Sep 18, 2022
BACKGROUND: Histopathology of nasal polyps contains rich prognostic information, which is difficult to extract objectively. In the present study, we aimed to develop a prognostic indicator of patient outcomes by analyzing scanned conventional hematox...
COVID-19 diagnosis by routine blood tests using machine learning.
Scientific reports
May 24, 2021
Physicians taking care of patients with COVID-19 have described different changes in routine blood parameters. However, these changes hinder them from performing COVID-19 diagnoses. We constructed a machine learning model for COVID-19 diagnosis that ...
Host-dependent molecular factors mediating SARS-CoV-2 infection to gain clinical insights for developing effective targeted therapy.
Molecular genetics and genomics : MGG
Mar 20, 2021
Coronavirus disease 2019 (COVID-19), a recent viral pandemic that first began in December 2019, in Hunan wildlife market, Wuhan, China. The infection is caused by a coronavirus, SARS-CoV-2 and clinically characterized by common symptoms including fev...
A modern approach to identifying and characterizing child asthma and wheeze phenotypes based on clinical data.
PloS one
Dec 30, 2019
'Asthma' is a complex disease that encapsulates a heterogeneous group of phenotypes and endotypes. Research to understand these phenotypes has previously been based on longitudinal wheeze patterns or hypothesis-driven observational criteria. The aim ...
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