ArticleiScience2026
Serum metabolic fingerprinting for diagnosis and therapeutic applications of ovarian endometriosis.
Article in iScience, 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.
- Microbiota-metabolite axis in endometriosis: Pathogenic mechanisms and clinical implications.Journal of biomedical research · 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
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Ovarian endometriosis (OvE) is a gynecological disorder with endometrial tissue in the ovaries, for which effective non-invasive diagnosis and curative treatments are currently lacking. Serum samples were collected from both discovery and validation cohorts to examine the metabolomic signatures. Fifty-six differential metabolites between patients with OvE and healthy controls were identified using untargeted metabolomic profiling. Weighted gene co-expression network analysis was further conducted to validate the differential metabolites. Subsequently, twenty-one metabolites were selected for further validation using targeted metabolomic profiling. Five machine learning algorithms confirmed the efficacy and stability of these metabolites for diagnosing OvE. Least absolute shrinkage and selection operator -logit regression identified six serum metabolites and two clinicopathological features with high diagnostic accuracy. Three differential metabolites were found to exhibit therapeutic potential for OvE in an
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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.