Identification of effective diagnostic biomarker and immune cell infiltration characteristics in acute liver failure by integrating bioinformatics analysis and machine-learning strategies.

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, 2 citations in OpenAlex.

1. Integrated transcriptomic and experimental validation reveal that N‑acetylcysteine ameliorates acute liver failure through multi‑target synergistic regulation of pyroptosis and related signaling networks.Naunyn-Schmiedeberg's archives of pharmacology · 2026
   Article
2. WGCNA combined with machine learning to explore potential biomarkers and treatment strategies for acute liver failure, with experimental validation.iLIVER · 2024
   Article
3. Diagnostic model constructed by five EMT-related genes for renal fibrosis and reflecting the condition of immune-related cells.Frontiers in immunology · 2023
   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

5 authors at 2 institutions in 1 country.

Funding

No grant is acknowledged in the PubMed record.

Abstract

PubMed holds no abstract for this paper.

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