Evidence map›Paper›PMID 29726123›Full record

ArticleJournal of biophotonics2018

Label-free spectrochemical probe for determination of hemoglobin glycation in clinical blood samples.

Rishikesh Pandey, Surya P Singh, Chi Zhang, Gary L Horowitz, Niyom Lue, Luis Galindo, Ramachandra R Dasari, Ishan Barman

Open access · greenAbstract read
In one paragraph

Article in Journal of biophotonics, 2018. 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
0.9field-weighted citation impact, top 28% 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

2 citing papers in PubMed, 9 citations in OpenAlex.

  1. Review
  2. 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

8 authors at 4 institutions in 1 country.

Rishikesh PandeyConnecticut Children's Innovation Center, University of Connecticut School of Medicine, Farmington, Connecticut.
Surya P SinghLaser Biomedical Research Center, Massachusetts Institute of Technology, Cambridge, Massachusetts.
Chi ZhangDepartment of Mechanical Engineering, Johns Hopkins University, Baltimore, Maryland.ORCID 0000-0002-1911-8258
Gary L HorowitzDivision of Clinical Pathology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts.
Niyom LueLaser Biomedical Research Center, Massachusetts Institute of Technology, Cambridge, Massachusetts.
Luis GalindoLaser Biomedical Research Center, Massachusetts Institute of Technology, Cambridge, Massachusetts.
Ramachandra R DasariLaser Biomedical Research Center, Massachusetts Institute of Technology, Cambridge, Massachusetts.
Ishan BarmanDepartment of Mechanical Engineering, Johns Hopkins University, Baltimore, Maryland.ORCID 0000-0003-0800-0825
Massachusetts Institute of Technology · USJohns Hopkins University · USTufts Medical Center · USUniversity of Connecticut · US

Funding

Wide-Field Short-Wave Infrared (SWIR) multiphoton (MP) tissue imagingP41EB015871 · NIBIB · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI SO, PETER T. · 2012 to 2021
$11.3M
Spectroscopy assisted mechano-chemical phenotype recognition nanoscopeDP2GM128198 · NIGMS · JOHNS HOPKINS UNIVERSITY · PI BARMAN, ISHAN · 2017 to 2017
$2.5M
NIBIB NIH HHS P41 EB015871NIGMS NIH HHS DP2 GM128198
6 · The paper itself

Abstract

Glycated hemoglobin, HbA1c, is an important biomarker that reveals the average value of blood glucose over the preceding 3 months. While significant recent attention has been focused on the use of optical and direct molecular spectroscopic methods for determination of HbA1c, a facile test that minimizes sample preparation needs and turnaround time still remains elusive. Here, we report a label-free approach for identifying low, mid and high-HbA1c groups in hemolysate and in whole blood samples featuring resonance Raman (RR) spectroscopy and support vector machine (SVM)-based classification of spectral patterns. The diagnostic power of RR measurements stems from its selective enhancement of hemoglobin-specific features, which simultaneously minimizes the blood matrix spectral interference and permits detection in the native solution. In this pilot study, our spectroscopic observations reveal that glycation of hemoglobin results in subtle but reproducible changes even when detected in the whole blood matrix. Leveraging SVM analysis of the principal component scores determined from the RR spectra, we show high degree of accuracy in classifying clinical specimen. We envisage that the promising findings will pave the way for more extensive clinical specimen investigations with the ultimate goal of translating molecular spectroscopy for routine point-of-care testing.

Indexed as

Spectrum Analysis, RamanBlood Chemical AnalysisGlycated HemoglobinHemolysisHumansMultivariate AnalysisSupport Vector MachineGlycated Hemoglobindiabetesglycated hemoglobinHbA1clabel-freeRaman spectroscopy

Identifiers

PMID29726123
PMCPMC6191038
OpenAlexW2800053849

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.