ArticleJournal of proteome research2026
Improving the Annotation for Spatial Proteomics: A Computational Approach to Enhance Molecular Characterization of Thyroid Nodules.
Article in Journal of proteome research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Review
- Sequential MALDI-MSI-Based Multiomics Reveals Spatial Lipid, Glycan, and Tryptic Peptide Signatures in Breast Tumor Histopathology.Analytical chemistry · 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
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The present work proposes a reproducible and automated workflow for integrating digital pathology in matrix-assisted laser-desorption ionization mass spectrometry imaging (MALDI-MSI) data analysis, using thyroid tissue as a proof-of-concept application. MALDI-MSI has shown promise in the molecular characterization of thyroid neoplasms. Yet challenges remain in minimizing signal interferents and improving diagnostic discrimination. In this study, we propose an interdisciplinary approach integrating digital pathology with spatial proteomics to enhance MALDI-MSI analysis of thyroid lesions from formalin-fixed paraffin-embedded tissue sections. We trained a pixel classifier to automatically select cell-rich regions of interest (ROIs) from hematoxylin and eosin-stained tissue microarrays, reducing interference from colloid-rich areas. The proteomics signals obtained with the pixel classifier (
Indexed as
Identifiers
What 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.