Evidence map›Paper›PMID 42763854›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2027

An Integrated Workflow for Culturing and AI-Based Profiling of Mouse Intestinal Epithelium.

Farah Qaderi, U Kaicheng, Yeganeh Farsi, Junhan Zhao, Nima Saeidi

Abstract read
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In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2027. 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

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Farah QaderiDivision of Gastrointestinal and Oncologic Surgery, Department of Surgery, Massachusetts General Hospital, Boston, MA, 02114, USA.
U KaichengTri-Institutional Computational Biology & Medicine, Weill Cornell Medicine, New York, NY, 10065, USA.
Yeganeh FarsiLaboratory of Ocular Immunology, Transplantation and Regeneration, Schepens Eye Research Institute of Massachusetts Eye and Ear, Department of Ophthalmology, Harvard Medical School, Boston, MA, USA.
Junhan ZhaoDepartment of Pediatrics, the University of Chicago, Chicago, IL, 60637, USA. junhanzv@uchicago.edu.
Nima SaeidiDivision of Gastrointestinal and Oncologic Surgery, Department of Surgery, Massachusetts General Hospital, Boston, MA, 02114, USA. nsaeidi@mgh.harvard.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Two-dimensional (2D) culture systems are powerful in vitro tools that have revolutionized the study of intestinal stem cell biology. These culture systems derived from mouse intestinal and colonic epithelia enable the modeling of tissue architecture, barrier function, and disease mechanisms under physiologically relevant conditions. This protocol details methods for isolating and maintaining mouse intestinal crypts as 2D monolayers. Monolayers are established via enzymatic dissociation and seeding onto ECM-functionalized hydrogels. This protocol also describes a deep learning pipeline that performs virtual fluorescent staining of label-free phase-contrast images of live organoids. This protocol enables the reproducible generation and computational analysis of murine intestinal epithelial cultures for studies in epithelial biology, drug testing, and disease modeling. This integrated system is designed for facilitating high-throughput imaging, immunolabeling, and functional analysis, expanding its use in regenerative medicine, tissue engineering, and gastrointestinal research.

Indexed as

Cell Culture TechniquesIntestinal MucosaAnimalsMiceOrganoidsTissue EngineeringWorkflowAIBioinformaticsImagingIntestineMonolayersPipeline

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.