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High-throughput Detection Method for Influenza Virus

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Type I interferon signature: a quantitative standardized method for clinical application.

Alessandra Tesser¹, Paola Bocca², Massimo Ulivi³

¹Department of Pediatrics, Institute for Maternal and Child Health, IRCCS Burlo Garofolo, Trieste, Italy.

Clinical and Experimental Immunology

|March 24, 2025

Summary

A new standardized method for analyzing the Type I Interferon (IFN) signature improves diagnostic accuracy for IFN-related inflammatory conditions. This approach minimizes variability, enabling reliable data sharing and better patient care.

Keywords:

data comparison interferon inflammation interferon signature patient classification standardized method

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Development and Validation of an Ultrasensitive Single Molecule Array Digital Enzyme-linked Immunosorbent Assay for Human Interferon-α

Development and Validation of an Ultrasensitive Single Molecule Array Digital Enzyme-linked Immunosorbent Assay for Human Interferon-α

Published on: June 14, 2018

Area of Science:

Immunology
Molecular Biology
Clinical Diagnostics

Background:

Type I Interferon (IFN) signature analysis is crucial for classifying inflammatory diseases and guiding personalized therapies.
Current methods lack standardization, hindering inter-center data comparison and clinical application.
Challenges exist in differentiating IFN-related inflammation from other cytokine-mediated conditions.

Purpose of the Study:

To develop and validate a standardized method for Type I IFN signature analysis.
To address the limitations of current methods by introducing a synthetic control.
To improve the reproducibility and comparability of IFN signature measurements across different laboratories and time points.

Main Methods:

Development and validation of a standardized assay incorporating a synthetic control.
Assessment of inter-assay and inter-laboratory variability through repeated analyses.
Validation in a cohort of 39 patients with diverse inflammatory disorders, including monogenic interferonopathies, systemic lupus erythematosus, juvenile dermatomyositis, periodic fevers, and juvenile idiopathic arthritis.

Main Results:

The proposed method demonstrated high reproducibility across different centers.
The assay effectively discriminated between IFN-related and non-IFN-related inflammatory conditions.
The synthetic control significantly minimized inter-assay and inter-laboratory variability.

Conclusions:

The standardized method provides a reliable tool for Type I IFN signature analysis in clinical practice.
This approach facilitates data sharing among research centers, advancing the understanding of IFN-related inflammation.
Improved diagnostic capabilities can lead to enhanced patient care for individuals with IFN-related disorders.