ArticleMedComm2026
Multi-Omics Analysis in Autoimmunity: Identification of MicroRNA Regulatory Networks and Cell-Type-Specific Dysregulations in Multiple Sclerosis and Type 1 Diabetes.
Article in MedComm, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Autoimmune diseases (AIDs) arise from complex immune dysregulations involving multiple immune cell types, cytokines, and molecular mediators. Among these, microRNAs (miRNAs) have recently emerged as key regulators of leukocyte processes and are frequently dysregulated in AIDs. However, their role in disease pathophysiology remains poorly understood. In this study, we performed a comprehensive analysis of miRNA expression in three immune populations, namely, CD14
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Registered trials
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.