Evidence map›Paper›PMID 29900535›Full record

ArticleJournal of periodontal research2018

High-throughput proteomic analysis of candidate biomarker changes in gingival crevicular fluid after treatment of chronic periodontitis.

Y A Guzman, D Sakellari, K Papadimitriou, C A Floudas

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of periodontal research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 2 pooled it
2.2field-weighted citation impact, top 12% 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

13 citing papers in PubMed, 2 syntheses or guidelines pooled it, 27 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

4 authors at 3 institutions in 2 countries.

Y A GuzmanArtie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, USA.
D SakellariDepartment of Preventive Dentistry, Periodontology and Implant Biology, Aristotle University of Thessaloniki, Thessaloniki, Greece.ORCID http://orcid.org/0000-0002-4365-8406
K PapadimitriouDepartment of Preventive Dentistry, Periodontology and Implant Biology, Aristotle University of Thessaloniki, Thessaloniki, Greece.
C A FloudasArtie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, USA.
Aristotle University of Thessaloniki · GRPrinceton University · USTexas A&M University · US

Funding

Peptide and Protein Identification via Tandem MS and Mixed-Integer OptimizationR01LM009338 · NLM · PRINCETON UNIVERSITY · PI FLOUDAS, CHRISTODOULOS ACHILLEUS · 2007 to 2010
$1.0M
National Institutes of Health r01lm009338National Science Foundation CBET-0941143NLM NIH HHS R01 LM009338
6 · The paper itself

Abstract

BACKGROUND AND

objectiveUntargeted, high-throughput proteomics methodologies have great potential to aid in identifying biomarkers for the diagnosis of periodontal disease. The application of such methods to the discovery of candidate biomarkers for the resolution of periodontal inflammation after periodontal therapy has been investigated. MATERIAL AND

methodsGingival crevicular fluid samples were collected from 10 patients diagnosed with chronic periodontitis at baseline and 1, 5, 9 and 13 weeks after completion of mechanical periodontal treatment. Clinical indices of periodontal disease, including probing depth, recession, clinical attachment level and bleeding on probing, were recorded at baseline and 13 weeks. Samples were analyzed using an online liquid chromatography-nanoelectrospray-hybrid ion trap-Orbitrap mass spectrometer. Spectra were processed with the PILOT_PROTEIN proteomics software suite.

resultsClinical parameters were significantly improved 13 weeks after treatment (Wilcoxon signed ranks test, P < .05). From the substantial number of identified proteins, a small subset was extracted by filter methods that included temporal pattern matching, logistic function fitting and mixed-integer linear optimization. This subset includes azurocidin, lysozyme C and myosin-9 as candidate biomarkers prominent at baseline and alpha-smooth muscle actin as prominent 13 weeks after treatment. Cross-validation studies yielded average predictive accuracy and area under the curve of 0.900 and 0.930, respectively.

conclusionHigh-throughput proteomic analysis can contribute to identifying endpoints of periodontal therapy. These candidate biomarkers should be evaluated for clinical efficacy.

Indexed as

BiomarkersChromatography, LiquidChronic PeriodontitisFemaleGingival Crevicular FluidHumansMaleMiddle AgedNanotechnologyPeriodontal IndexProteomicsSpectrometry, Mass, Electrospray IonizationBiomarkersbiomarkersgingival crevicular fluidnonsurgical periodontal therapy

Identifiers

PMID29900535
PMCPMC6135659
OpenAlexW2807871813

What OpenQuestion holds

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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.