Evidence map›Paper›PMID 40308219›Full record

ArticleFrontiers in neurology2025

Raman liquid biopsy: a new approach to the multiple sclerosis diagnostics.

Anna Neupokoeva, Ivan Bratchenko, Lyudmila Bratchenko, Elena Khivintseva, Igor Shirolapov, Natalia Shusharina, Matvei Khoimov, Valery Zakharov, Alexander Zakharov

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in neurology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

9 authors.

Anna NeupokoevaDepartment of Medical Physics, Mathematics and Computer Science, Samara State Medical University, Samara, Russia.
Ivan BratchenkoLaser and Biotechnical Systems Department, Samara National Research University, Samara, Russia.
Lyudmila BratchenkoLaser and Biotechnical Systems Department, Samara National Research University, Samara, Russia.
Elena KhivintsevaDepartment of Neurology and Neurosurgery, Samara State Medical University, Samara, Russia.
Igor ShirolapovDepartment of Physiology, Samara State Medical University, Samara, Russia.
Natalia ShusharinaBaltic Center for Neurotecnology and Artificial Intelligence, Immanuel Kant Baltic Federal University, Kaliningrad, Russia.
Matvei KhoimovBaltic Center for Neurotecnology and Artificial Intelligence, Immanuel Kant Baltic Federal University, Kaliningrad, Russia.
Valery ZakharovLaser and Biotechnical Systems Department, Samara National Research University, Samara, Russia.
Alexander ZakharovDepartment of Neurology and Neurosurgery, Samara State Medical University, Samara, Russia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background/objectives: Despite the prevalence of multiple sclerosis, there is currently no biomarker by which this disease can be reliably identified. Existing diagnostic methods are either expensive or have low specificity. Therefore, the search for a diagnostic method with high specificity and sensitivity, and at the same time not requiring complex sample processing or expensive equipment, is urgent. Methods: The article discusses the use of blood serum surface enhanced Raman spectroscopy in combination with machine learning analysis to separate persons with multiple sclerosis and healthy individuals. As a machine learning method for Raman spectra processing the projection on latent structures-discriminant analysis was used. Results: Using the above methods, we have obtained possibility to separate persons with multiple sclerosis and healthy ones with an average specificity of 0.96 and an average sensitivity of 0.89. The main Raman bands for discrimination against multiple sclerosis and healthy individuals are 632, 721-735, 1,048-1,076 cm

Indexed as

blood serumdiagnostic testmultiple sclerosisprojection on latent structures-discriminant analysissurface enhanced Raman spectroscopy

Identifiers

PMID40308219
PMCPMC12040660

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.