Evidence map›Paper›PMID 37685360›Full record

ArticleDiagnostics (Basel, Switzerland)2023

An Evaluation of Lymphedema Using Optical Coherence Tomography: A Rat Limb Model Approach.

V V Nikolaev, I A Trimassov, D S Amirchanov, E A Shirshin, N A Krivova, S A Beliaeva, E A Sandykova, Yu V Kistenev

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Diagnostics (Basel, Switzerland), 2023. 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
–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

2 citing papers in PubMed.

  1. Article
  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.

V V NikolaevLaboratory of Laser Molecular Imaging and Machine Learning, Tomsk State University, 36, Lenin Ave., Tomsk 634050, Russia.ORCID 0000-0001-8189-0188
I A TrimassovLaboratory of Laser Molecular Imaging and Machine Learning, Tomsk State University, 36, Lenin Ave., Tomsk 634050, Russia.ORCID 0000-0002-6156-2395
D S AmirchanovLaboratory of Laser Molecular Imaging and Machine Learning, Tomsk State University, 36, Lenin Ave., Tomsk 634050, Russia.
E A ShirshinFaculty of Physics, Lomonosov Moscow State University, Moscow 119991, Russia.
N A KrivovaLaboratory of Laser Molecular Imaging and Machine Learning, Tomsk State University, 36, Lenin Ave., Tomsk 634050, Russia.
S A BeliaevaLaboratory of Laser Molecular Imaging and Machine Learning, Tomsk State University, 36, Lenin Ave., Tomsk 634050, Russia.
E A SandykovaLaboratory of Laser Molecular Imaging and Machine Learning, Tomsk State University, 36, Lenin Ave., Tomsk 634050, Russia.
Yu V KistenevLaboratory of Laser Molecular Imaging and Machine Learning, Tomsk State University, 36, Lenin Ave., Tomsk 634050, Russia.ORCID 0000-0001-5760-1462

Funding

Government of the Russian Federation No. 075-15-2021-615
6 · The paper itself

Abstract

Lymphedema is a pathology caused by poor lymphatic flow which may lead to complete disability. Currently, precise, non-invasive techniques for quantifying lymphedema are lacking. In this paper, the results of an in vivo assessment of lymphedema via a developed small-animal model using the hindlimbs of rats and an optical coherence tomography (OCT) technique are presented. This model of lymphedema was based on a surgical lymph node resection and subsequent two-step X-ray exposure. The development of lymphedema was verified via the histological examination of tissue biopsies. The properties of the lymphedematous skin were analyzed in vivo and compared with healthy skin via OCT. The main differences observed were (1) a thickening of the stratum corneum layer, (2) a thinning of the viable epidermis layer, and (3) higher signal attenuation in the dermis layer of the lymphedematous skin. Based on the distribution of the OCT signal's intensity in the skin, a machine learning algorithm was developed which allowed for a classification of normal and lymphedematous tissue sites with an accuracy of 90%. The obtained results pave the way for in vivo control over the development of lymphedema.

Indexed as

curve proximity factorhistologylymphedema diagnosislymphedematous tissueoptical coherence tomography

Identifiers

PMID37685360
PMCPMC10486677

What OpenQuestion holds

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Registered trials

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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.