Evidence map›Paper›PMID 38929828›Full record

ArticleJournal of personalized medicine2024

Evaluation of a New Multiparametric Microdot Array-Based Immunoassay Panel for Systemic Autoimmune Disease Diagnosis.

Maria Infantino, Francesca Pavia, Valentina Grossi, Barbara Lari, Maurizio Benucci, Francesca Li Gobbi, Silvia Pancani, Mariangela Manfredi

Abstract read
In one paragraph

Article in Journal of personalized medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Maria InfantinoImmunology and Allergology Laboratory, S. Giovanni di Dio Hospital, 50143 Florence, Italy.ORCID 0000-0002-6200-4467
Francesca PaviaImmunology and Allergology Laboratory, S. Giovanni di Dio Hospital, 50143 Florence, Italy.
Valentina GrossiImmunology and Allergology Laboratory, S. Giovanni di Dio Hospital, 50143 Florence, Italy.
Barbara LariImmunology and Allergology Laboratory, S. Giovanni di Dio Hospital, 50143 Florence, Italy.
Maurizio BenucciRheumatology Unit, S. Giovanni di Dio Hospital, 50143 Florence, Italy.ORCID 0000-0001-8180-6660
Francesca Li GobbiRheumatology Unit, S. Giovanni di Dio Hospital, 50143 Florence, Italy.
Silvia PancaniIRCCS Fondazione Don Carlo Gnocchi, 50143 Florence, Italy.
Mariangela ManfrediImmunology and Allergology Laboratory, S. Giovanni di Dio Hospital, 50143 Florence, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe early reliable detection and quantification of autoantibodies play an important role in autoimmune disease diagnosis and in disease-course monitoring. New technologies, such as the multiplexed determination of autoantibodies, have recently been introduced and are being adopted more frequently. The aim of this study was to evaluate the ability of a new microdot array-based multiparametric assay (ZENIT AMiDot CTD panel, A. Menarini Diagnostics, Firenze, Italy) to correctly classify patients with autoimmune rheumatic diseases (ARDs) and compare it to a fluorescence enzyme immunoassay (FEIA) for the detection of anti-ENAs.

methodsThe study included 69 consecutive samples from patients with ARDs that were analyzed using two different methods (FEIA and AMiDot) to detect anti-CENP B and six anti-ENA antibodies: anti-Scl-70, anti-SSB/La, anti-Jo-1, anti-U1-RNP, anti-Ro52, and anti-Ro60. The control group sera came from sixty-eight blood donors. Tests were run on the automated slide processor ZENIT FLOW, and then the slides were imaged and analyzed using ZENIT fast.

resultsSince the samples were selected for at least one antibody positivity with an ARD diagnosis, we did not calculate clinical sensitivity but only specificity, which was 98.53%, ranging from 90% for anti-SSB/La antibodies to 100% for anti-CENP B ones. Mean agreement among the methods assessed by Cohen's kappa was 0.816 ± 0.240.

conclusionsThe assay demonstrated good clinical performance and may be considered a valuable aid in detecting ARD patients, offering an alternative to methods such as FEIA which are largely in use today.

Indexed as

CTD screenmicrodot arraynew methodsystemic autoimmune disease

Identifiers

PMID38929828
PMCPMC11205220

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.