Evidence map›Paper›PMID 40791702›Full record

ArticlemedRxiv : the preprint server for health sciences2025

Three-dimensional high-content imaging of unstained soft tissue with subcellular resolution using a laboratory-based multi-modal X-ray microscope.

Michela Esposito, Alberto Astolfo, Yang Zhou, Ian Buchanan, Alexei Teplov, Marco Endrizzi, Alexandra Egido Vinogradova, Olga Makarova, Ralu Divan, Cha-Mei Tang and 5 more

Abstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

15 authors.

Alberto Astolfo
Yang Zhou
Ian Buchanan
Alexei Teplov
Marco Endrizzi
Alexandra Egido Vinogradova
Olga Makarova
Ralu Divan
Cha-Mei Tang
Yukako Yagi
Peter D Lee
Claire L Walsh
Joseph D Ferrara
Alessandro Olivo

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With increasing interest in studying biological systems across spatial scales-from centimetres down to nanometres-histology continues to be the gold standard for tissue imaging at cellular resolution, providing an essential bridge between macroscopic and nanoscopic analysis. However, its inherently destructive and two-dimensional nature limits its ability to capture the full three-dimensional complexity of tissue architecture. Here we show that phase-contrast X-ray microscopy can enable three-dimensional virtual histology with subcellular resolution. This technique provides direct quantification of electron density without restrictive assumptions, allowing for direct characterisation of cellular nuclei in a standard laboratory setting. By combining high spatial resolution and soft tissue contrast, with automated segmentation of cell nuclei, we demonstrated virtual H&E staining using machine learning-based style transfer, yielding volumetric datasets compatible with existing histopathological analysis tools. Furthermore, by integrating electron density and the sensitivity to nanometric features of the dark field contrast channel, we achieve stain-free, high-content imaging capable of distinguishing nuclei and extracellular matrix.

Identifiers

PMID40791702
PMCPMC12338900

What OpenQuestion holds

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Registered trials

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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.