ArticleChinese journal of integrative medicine2026
Serum Biomarker Identification of Damp-Heat Pattern in Patients with Chronic Liver Diseases.
Article in Chinese journal of integrative medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo identify serum diagnostic biomarker for damp-heat (DH) pattern in chronic liver diseases using transcriptomics and metabolomics.
methodsPatients with chronic hepatitis B (CHB) or metabolic dysfunction-associated fatty liver disease (MAFLD) were categorized into DH and non-DH pattern groups. Serum RNA profiles were analyzed via RNA-seq/microarray, and metabolites were quantified by ultraperformance liquid chromatography coupled with tandem mass spectrometry (UPLC-MS/MS). Biomarker screening and validation employed discovery (88 cases) and validation (85 cases) cohorts, utilizing Rank-in analysis and Least Absolute Shrinkage and Selection Operator (LASSO) regression for gene selection; reverse transcription-polymerase chain reaction (RT-PCR) and targeted UPLC-MS/MS for expression validation; random forest and receiver operating characteristic (ROC) curve analysis (with area under the curve, AUC) for diagnostic assessment.
resultsCompared with the non-DH pattern group, patients with DH pattern showed significantly elevated liver injury markers and reduced apolipoprotein A1 (P<0.05). Integrated transcriptome analysis (Rank-in) identified 315 dysregulated gene sets, primarily enriched in chemokine signaling. LASSO selected 27 genes for RT-PCR validation, confirming 5 differential genes. Metabolomics revealed 25 differential metabolites (discovery cohort), with 7 showing ⩾ 2-fold change; 6 maintained consistent trends in validation. A random forest model combining 4 genes (PTPN22, CTSD, TBX21, STAT4) and 2 metabolites (pyroglutamic acid and glutamic acid) achieved a validation AUC of 0.828 for DH diagnosis.
conclusionA multi-omics diagnostic model incorporating 4 genes and 2 metabolites demonstrates promising diagnostic potential for DH pattern in patients with chronic liver diseases. (registration No. ChiCTR2000037248).
Indexed as
Identifiers
41843023What OpenQuestion holds
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.