ArticleBioinformation2022
Evaluation of serum proteome from Indian psoriasis patients.
Article in Bioinformation, 2022. 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
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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.
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, 2 citations in OpenAlex.
- Cross-sectional study of proteomic differences between moderate and severe psoriasis.Scientific reports · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Psoriasis is a polygenic chronic skin condition, associated with many systemic disorders. Though it is most studied dermatological condition, molecular mechanism leading to its pathogenesis is still unclear. An insight into its proteome may help unrevealing some biomarkers and therapeutic targets. In this study, we carried out mass spectrometry based quantitative proteomic analysis of serum from psoriasis patients by employing Tandem Mass Tags (TMT) approach. We identified 861,887 MS/MS spectra corresponding to 493 proteins. These dysregulated proteins were further classified by Gene Ontology and protein-protein interaction of dys-regulated proteins revealed networks in psoriasis patients.
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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.