ArticleJournal of inflammation research2024
Single-Cell Sequencing and Machine Learning Integration to Identify Candidate Biomarkers in Psoriasis:
Article in Journal of inflammation research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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
3 citing papers in PubMed.
- Macrophage INSIG1 deficiency drives psoriasiform dermatitis via the SREBP2-STAT1 axis.Nature communications · 2026Article
- Integrated transcriptomic and tissue-validation analyses identify palmitoylation-associated biomarkers linked to sphingolipid metabolism and immune remodeling in psoriasis.Frontiers in immunology · 2026Article
- Single-Cell RNA Sequencing Analysis Reveals Correlation Between Immune Cell Composition and Gene Expression in Cervical Cancer.Journal of cellular and molecular medicine · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Psoriasis represents a persistent, immune-driven inflammatory condition affecting the skin, characterized by a lack of well-established biologic treatments without adverse events. Consequently, the identification of novel targets and therapeutic agents remains a pressing priority in the field of psoriasis research. Methods: We collected single-cell RNA sequencing (scRNA-seq) datasets and inferred T cell differentiation trajectories through pseudotime analysis. Bulk transcriptome and scRNA-seq data were integrated to identify differentially expressed genes (DEGs). Machine learning was employed to screen candidate genes. Correlation analysis was used to predict the interactions between cells expressing insulin-induced gene 1 ( Results: T cells were initially categorized into seven states, with differentially expressed genes in T cells (TDEGs) identified and their functions and signaling pathways. Conclusion:
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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 Socratic graph.