Evidence map›Paper›PMID 31382386›Full record

ArticleSensors (Basel, Switzerland)2019

Raman Analysis of Tear Fluid Alteration Following Contact Lense Use.

Angela Capaccio, Antonio Sasso, Giulia Rusciano

Open access · goldAbstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
1.6field-weighted citation impact, top 13% 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

9 citing papers in PubMed, 19 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

3 authors at 2 institutions in 1 country.

Angela CapaccioDepartment of Physics E. Pancini, University of Naples Federico II, Complesso Univesitario Monte S. Angelo, Via Cintia, I-80126 Naples, Italy. angela.capaccio@unina.it.
Antonio SassoDepartment of Physics E. Pancini, University of Naples Federico II, Complesso Univesitario Monte S. Angelo, Via Cintia, I-80126 Naples, Italy.
Giulia RuscianoDepartment of Physics E. Pancini, University of Naples Federico II, Complesso Univesitario Monte S. Angelo, Via Cintia, I-80126 Naples, Italy.ORCID 0000-0002-7152-5352
National Research Council · ITUniversity of Naples Federico II · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Tear fluid is a heterogeneous solution containing mainly proteins, lipids, mucins and electrolytes, which regulates the physiology of the human eye. The complex composition of tears can be altered in the presence of eye inflammations. The use of contact lenses is one of the most frequent causes of inflammatory responses of the eye, with the related discomfort often causing the wearer to give up using them. In this paper, we exploit the potentiality of Raman Spectroscopy to analyse the biochemical changes in tear fluid in a contact lens wearer. In particular, we analysed the tear fluid collected from a volunteer as a function of the wearing time for two types of monthly contact lenses (Hydrogel and Si-Hydrogel). Our experimental results show an alteration of the relative concentrations of proteins and lipids in both of the analysed cases. More importantly, our results highlight the diagnostic sensitivity of Raman analysis to select the proper contact lens type for each wearer and optimise the lens wearing conditions.

Indexed as

Spectrum Analysis, RamanContact LensesHumansHydrogel, Polyethylene Glycol DimethacrylateLipidsPrincipal Component AnalysisProteinsTearsHydrogel, Polyethylene Glycol DimethacrylateLipidsProteinscontact lens wearingRaman Spectroscopytear fluid

Identifiers

PMID31382386
PMCPMC6695878
OpenAlexW2966670774

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.