ArticleAnalytical chemistry2024
Sensitive Multiplexed MicroRNA Spatial Profiling and Data Classification Framework Applied to Murine Breast Tumors.
Article in Analytical chemistry, 2024. 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
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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.
- In-situ growth of heterogeneous Au on MoSScientific reports · 2026Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
MicroRNAs (miRNAs) are small RNAs that are often dysregulated in many diseases, including cancers. They are highly tissue-specific and stable, thus, making them particularly useful as biomarkers. As the spatial transcriptomics field advances, protocols that enable highly sensitive and spatially resolved detection become necessary to maximize the information gained from samples. This is especially true of miRNAs where the location their expression within tissue can provide prognostic value with regard to patient outcome. Equally as important as detection are ways to assess and visualize the miRNA's spatial information in order to leverage the power of spatial transcriptomics over that of traditional nonspatial bulk assays. We present a highly sensitive methodology that simultaneously quantitates and spatially detects seven miRNAs
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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 OpenQuestion graph.