Evidence map›Paper›PMID 25520938›Full record

ArticleBioMedicine2014

Mass spectrometry-based proteomics in Chest Medicine, Gerontology, and Nephrology: subgroups omics for personalized medicine.

Shih-Yi Lin, Wu-Huei Hsu, Cheng-Chieh Lin, Chao-Jung Chen

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BioMedicine, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Observational
  2. Article
  3. Review
  4. Article
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

4 authors.

Shih-Yi LinInstitute of Clinical Medical Science, China Medical University College of Medicine, 404 Taichung, Taiwan ; Department of Internal Medicine, China Medical University Hospital, 404 Taichung, Taiwan ; Division of Nephrology and Kidney Institute, China Medical University Hospital, 404 Taichung, Taiwan.
Wu-Huei HsuInstitute of Clinical Medical Science, China Medical University College of Medicine, 404 Taichung, Taiwan ; Department of Internal Medicine, China Medical University Hospital, 404 Taichung, Taiwan ; Division of Pulmonary and Critical Care Medicine, China Medical University Hospital and China Medical University, 404 Taichung, Taiwan.
Cheng-Chieh LinInstitute of Clinical Medical Science, China Medical University College of Medicine, 404 Taichung, Taiwan ; Department of Family Medicine, China Medical University Hospital, 404 Taichung, Taiwan ; School of Medicine, College of Medicine China Medical University, No. 91, Hsueh Shih Road, 404 Taichung, Taiwan.
Chao-Jung ChenGraduate Institute of Integrated Medicine, College of Chinese Medicine, China Medical University, No. 91, Hsueh-Shih Road, 402 Taichung, Taiwan ; Proteomics Core Laboratory, Department of Medical Research, China Medical University Hospital, 404 Taichung, Taiwan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Mass spectrometry (MS) is currently the most promising tool for studying proteomics to investigate largescale proteins in a specific proteome. Emerging MS-based proteomics is widely applied to decipher complex proteome for discovering potential biomarkers. Given its growing usage in clinical medicine for biomarker discovery to predict, diagnose and confer prognosis, MS-based proteomics can benefit study of personalized medicine. In this review we introduce some fundamental MS theory and MS-based quantitative proteomic approaches as well as several representative clinical MS-based proteomics issues in Chest Medicine, Gerontology, and Nephrology.

Indexed as

Chest Medicine;GerontologyMass spectrometry;Nephrology;Personalized medicine;Proteomics;

Identifiers

PMID25520938
PMCPMC4264973

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.