ArticleMethods in molecular biology (Clifton, N.J.)2017
Preparation of Disease-Related Protein Assemblies for Single Particle Electron Microscopy.
Article in Methods in molecular biology (Clifton, N.J.), 2017. 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
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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
2 citing papers in PubMed.
- Fibrocystin/polyductin (FPC): new functional insights into ARPKD pathogenesis revealed by informatics, comparative genomics, and model systems.Pediatric nephrology (Berlin, Germany) · 2026Review
- Differential regulation of MYC expression byFrontiers in cell and developmental biology · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Electron microscopy (EM) is a rapidly growing area of structural biology that permits us to decode biological assemblies at the nanoscale. To examine biological materials for single particle EM analysis, purified assemblies must be obtained using biochemical separation techniques. Here, we describe effective methodologies for isolating histidine (his)-tagged protein assemblies from the nucleus of disease-relevant cell lines. We further demonstrate how isolated assemblies are visualized using single particle EM techniques and provide representative results for each step in the process.
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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.