Evidence map›Paper›PMID 32280222›Full record

ArticleInternational journal of nanomedicine2020

Filter-Membrane-Based Ultrafiltration Coupled with Surface-Enhanced Raman Spectroscopy for Potential Differentiation of Benign and Malignant Thyroid Tumors from Blood Plasma.

Xiaozhou Liang, Xuchao Miao, Weijin Xiao, Qin Ye, Sisi Wang, Juqiang Lin, Chao Li, Zufang Huang

Open access · goldAbstract read
In one paragraph

Article in International journal of nanomedicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
3.6field-weighted citation impact, top 7% 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

6 citing papers in PubMed, 1 synthesis or guideline pooled it, 30 citations in OpenAlex.

  1. Pooled it
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  6. Methylglyoxal Adducts Levels in Blood Measured onNanomaterials (Basel, Switzerland) · 2021
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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

8 authors at 2 institutions in 1 country.

Xiaozhou LiangFujian Normal University, Ministry of Education, Key Laboratory of Optoelectronic Science and Technology for Medicine, Fujian Provincial Key Laboratory for Photonics Technology, Fuzhou, People's Republic of China.ORCID 0000-0003-4594-0226
Xuchao MiaoFujian Normal University, Ministry of Education, Key Laboratory of Optoelectronic Science and Technology for Medicine, Fujian Provincial Key Laboratory for Photonics Technology, Fuzhou, People's Republic of China.
Weijin XiaoDepartment of Pathology, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, People's Republic of China.
Qin YeDepartment of Head and Neck Surgery, Fujian Cancer Hospital, Fujian Medical University Cancer Hospital, Fuzhou, People's Republic of China.ORCID 0000-0001-5545-3777
Sisi WangDepartment of General Surgery, Fujian Medical University Union Hospital, Fuzhou, People's Republic of China.
Juqiang LinFujian Normal University, Ministry of Education, Key Laboratory of Optoelectronic Science and Technology for Medicine, Fujian Provincial Key Laboratory for Photonics Technology, Fuzhou, People's Republic of China.
Chao LiDepartment of Pathology, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, People's Republic of China.
Zufang HuangFujian Normal University, Ministry of Education, Key Laboratory of Optoelectronic Science and Technology for Medicine, Fujian Provincial Key Laboratory for Photonics Technology, Fuzhou, People's Republic of China.
Fujian Medical University · CNFujian Normal University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThe objective of this study is to evaluate the performance and feasibility of surface-enhanced Raman spectroscopy coupled with a filter membrane and advanced multivariate data analysis on identifying and differentiating benign and malignant thyroid tumors from blood plasma. PATIENTS AND

methodsWe proposed a membrane filter SERS technology for the differentiation between benign thyroid tumor and thyroid cancer. That is to say, by using filter membranes with optimal pore size, the blood plasma samples from thyroid tumor patients were pretreated with the macromolecular proteins being filtered out prior to SERS measurement. The SERS spectra of blood plasma ultrafiltrate obtained using filter membranes from 102 patients with thyroid tumors (70 thyroid cancers and 32 benign thyroid tumors) were then analyzed and compared. Two multivariate statistical analyses, principal component analysis-linear discriminate analysis (PCA-LDA) and Lasso-partial least squares-discriminant analysis (Lasso-PLS-DA), were performed on the SERS spectral data after background subtraction and normalization, as well as the first derivative processing, to analyze and compare the differential diagnosis of benign thyroid tumors and thyroid cancer.

resultsSERS measurements were performed in blood plasma acquired from a total of 102 thyroid tumor patients (benign thyroid tumor N=32; thyroid cancer N=70). By using filter membranes, the macromolecular proteins in blood plasma were effectively filtered out to yield high-quality SERS spectra. 84.3% discrimination accuracy between benign and malignant thyroid tumor was achieved using PCA-LDA method, while Lasso-PLS-DA yields a discrimination accuracy of 90.2%.

conclusionOur results demonstrate that SERS spectroscopy, coupled with ultrafiltration and multivariate analysis has the potential of providing a non-invasive, rapid, and objective detection and differentiation of benign and malignant thyroid tumors.

Indexed as

AdultDiagnosis, DifferentialDiscriminant AnalysisHumansMembranes, ArtificialMetal NanoparticlesMiddle AgedMultivariate AnalysisPlasmaPrincipal Component AnalysisProof of Concept StudySilverSpectrum Analysis, RamanThyroid NeoplasmsUltrafiltrationMembranes, ArtificialSilverblood plasmafilter membranesilver nanoparticlessurface-enhanced Raman spectroscopythyroid tumor

Identifiers

PMID32280222
PMCPMC7132009
OpenAlexW3014637742

What OpenQuestion holds

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LicenceCC BY-NC
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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.