Evidence map›Paper›PMID 26407607›Full record

ArticleJournal of biomolecular NMR2015

Preparation of highly and generally enriched mammalian tissues for solid state NMR.

Veronica Wai Ching Wong, David G Reid, Wing Ying Chow, Rakesh Rajan, Maggie Green, Roger A Brooks, Melinda J Duer

Open access · hybridAbstract read
PubMed Publisher
In one paragraph

Article in Journal of biomolecular NMR, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
1.2field-weighted citation impact, top 24% of its field
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

9 citing papers in PubMed, 17 citations in OpenAlex.

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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

7 authors at 2 institutions in 1 country.

Veronica Wai Ching WongDepartment of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK.
David G ReidDepartment of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK.
Wing Ying ChowDepartment of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK.
Rakesh RajanDepartment of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK.
Maggie GreenCentral Biomedical Resources, School of Clinical Medicine, University of Cambridge, West Forvie Building, Forvie Site, Robinson Way, Cambridge, CB2 0SZ, UK.
Roger A BrooksDepartment of Trauma and Orthopaedic Surgery, Addenbrooke's Hospital, University of Cambridge, Cambridge, CB2 0QQ, UK.
Melinda J DuerDepartment of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK. mjd13@cam.ac.uk.
University of Cambridge · GBAddenbrooke's Hospital · GB

Funding

Biotechnology and Biological Sciences Research Council BB/G021392/1
6 · The paper itself

Abstract

An appreciable level of isotope labelling is essential for future NMR structure elucidation of mammalian biomaterials, which are either poorly expressed, or unexpressable, using micro-organisms. We present a detailed protocol for high level (13)C enrichment even in slow turnover murine biomaterials (fur keratin), using a customized diet supplemented with commercial labelled algal hydrolysate and formulated as a gel to minimize wastage, which female mice consumed during pregnancy and lactation. This procedure produced approximately eightfold higher fur keratin labelling in pups, exposed in utero and throughout life to label, than in adults exposed for the same period, showing both the effectiveness, and necessity, of this approach.

Indexed as

Nuclear Magnetic Resonance, BiomolecularAnimalsMiceOrgan SpecificityProteinsProteinsBone collagenCarbon-13Fur keratinIn vivoLabellingMouse

Identifiers

PMID26407607
OpenAlexW1933661988

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.