Evidence map›Paper›PMID 32775566›Full record

ArticleData in brief2020

Human cerebrospinal fluid data for use as spectral library, for biomarker research.

Lukas M Schilde, Simone Steinbach, Bettina Serschnitzki, Fabian Maass, Mathias Bähr, Paul Lingor, Katrin Marcus, Caroline May

Abstract read
In one paragraph

Article in Data in brief, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Review
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

8 authors.

Lukas M SchildeRuhr-University Bochum, Center for Protein Diagnostics (PRODI), Medical Proteome Analysis, Germany.
Simone SteinbachRuhr-University Bochum, Center for Protein Diagnostics (PRODI), Medical Proteome Analysis, Germany.
Bettina SerschnitzkiRuhr-University Bochum, Center for Protein Diagnostics (PRODI), Medical Proteome Analysis, Germany.
Fabian MaassDepartment of Neurology, University Medical Center Göttingen, Göttingen, Germany.
Mathias BährDepartment of Neurology, University Medical Center Göttingen, Göttingen, Germany.
Paul LingorDepartment of Neurology, University Medical Center Göttingen, Göttingen, Germany.
Katrin MarcusRuhr-University Bochum, Center for Protein Diagnostics (PRODI), Medical Proteome Analysis, Germany.
Caroline MayRuhr-University Bochum, Center for Protein Diagnostics (PRODI), Medical Proteome Analysis, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Spectral libraries generated by data dependent acquisition (DDA) are a useful tool for the analysis of data created by data independent acquisition (DIA) in mass spectrometry. The quality of DIA analysis is dependent on the quality of the spectral library. We used cerebrospinal fluid (CSF) of patients with Parkinson's disease and healthy controls to create a spectral library of human CSF proteome. To this date, there is no validated CSF biomarker for Parkinson's disease. This data set may therefore be valuable for the future analysis of CSF proteins. Part of the samples consisted of fractions that were separated by gel electrophoresis. After tryptic digestion, all samples were spiked with indexed retention time (iRT) peptides and were measured using a DDA mass spectrometry approach. The here provided data set can be used as a CSF-specific spectral library. Data files generated from the described workflow are hosted in the public repository ProteomeXchange under the identifier PXD013487.

Indexed as

Cerebrospinal fluidData-independent acquisitionMass spectrometryParkinson's diseaseProteomicsSpectral library

Identifiers

PMID32775566
PMCPMC7399102

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.