Evidence map›Paper›PMID 22164077›Full record

ArticleSensors (Basel, Switzerland)2011

Micro-Raman spectroscopy and univariate analysis for monitoring disease follow-up.

Carlo Camerlingo, Ines Delfino, Giuseppe Perna, Vito Capozzi, Maria Lepore

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2011. 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. Trial
  2. Multi-Wavelength Raman Differentiation of Malignant Skin Neoplasms.International journal of molecular sciences · 2024
    Article
  3. Article
  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

5 authors.

Carlo CamerlingoCNR (Consiglio Nazionale delle Ricerche), Istituto di Cibernetica E. Caianiello, 80078, Pozzuoli, Italy. c.camerlingo@cib.na.cnr.it
Ines Delfino
Giuseppe Perna
Vito Capozzi
Maria Lepore

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Micro-Raman spectroscopy is a very promising tool for medical applications, thanks to its sensitivity to subtle changes in the chemical and structural characteristics of biological specimens. To fully exploit these promises, building a method of data analysis properly suited for the case under study is crucial. Here, a linear or univariate approach using a R2 determination coefficient is proposed for discriminating Raman spectra even with small differences. The validity of the proposed approach has been tested using Raman spectra of high purity glucose solutions collected in the 600 to 1,600 cm(-1) region and also from solutions with two known solutes at different concentrations. After this validation step, the proposed analysis has been applied to Raman spectra from oral human tissues affected by Pemphigus Vulgaris (PV), a rare life-threatening autoimmune disease, for monitoring disease follow-up. Raman spectra have been obtained in the wavenumber regions from 1,050 to 1,700 cm(-1) and 2,700 to 3,200 cm(-1) from tissues of patients at different stages of pathology (active PV, under therapy and PV in remission stage) as confirmed by histopathological and immunofluorescence analysis. Differences in the spectra depending on tissue illness stage have been detected at 1,150-1,250 cm(-1) (amide III) and 1,420-1,450 cm(-1) (CH3 deformation) regions and around 1,650 cm(-1) (amide I) and 2,930 cm(-1) (CH3 symmetric stretch). The analysis of tissue Raman spectra by the proposed univariate method has allowed us to effectively differentiate tissues at different stages of pathology.

Indexed as

Follow-Up StudiesHumansMonitoring, PhysiologicSpectrum Analysis, Ramanfollow-up monitoringoral tissuesRaman microspectroscopyunivariate data analysis

Identifiers

PMID22164077
PMCPMC3231482

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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.