Evidence map›Paper›PMID 41081826›Full record

ArticleAnalytical and bioanalytical chemistry2025

Raman spectroscopy supported by machine learning reveals changes in balance of macromolecules in diabetic rat serum.

Adrianna Kryska, Joanna Depciuch, Mikolaj Krysa, Wieslaw Paja, Agnieszka Wosiak, Marcin Nicoś, Barbara Budzynska, Anna Sroka-Bartnicka

Abstract read
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In one paragraph

Article in Analytical and bioanalytical chemistry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Adrianna KryskaIndependent Unit of Spectroscopy and Chemical Imaging, Medical University of Lublin, Chodzki 4a, 20-093, Lublin, Poland.
Joanna DepciuchInstitute of Nuclear Physics, Polish Academy of Sciences, Walerego Eljasza-Radzikowskiego 152, 31-342, Kraków, Poland.
Mikolaj KrysaIndependent Unit of Spectroscopy and Chemical Imaging, Medical University of Lublin, Chodzki 4a, 20-093, Lublin, Poland.
Wieslaw PajaDepartment of Artificial Intelligence, Institute of Computer Science, University of Rzeszow, Pigonia 1, 35-310, Rzeszów, Poland.
Agnieszka WosiakInstitute of Information Technology, Lodz University of Technology, Politechniki 8, 93-590, Łódź, Poland.
Marcin NicośDepartment of Pneumonology, Oncology and Allergology, Medical University of Lublin, Jaczewskiego 8, 20-090, Lublin, Poland.
Barbara BudzynskaIndependent Laboratory of Behavioral Studies, Medical University of Lublin, Chodzki 4a, 20-093, Lublin, Poland.
Anna Sroka-BartnickaIndependent Unit of Spectroscopy and Chemical Imaging, Medical University of Lublin, Chodzki 4a, 20-093, Lublin, Poland. anna.sroka-bartnicka@umlub.pl.ORCID http://orcid.org/0000-0003-0739-3567

Funding

Narodowe Centrum Nauki UMO-2020/39/D/ST4/01604
6 · The paper itself

Abstract

In type 2 diabetes mellitus (T2DM), changes in glucose and lipid levels in serum are the first signals of disease progression. In this article, Raman spectroscopy supported with machine learning (ML) was used both to determine the level of imbalance between major metabolites caused by the disease and to investigate which metabolites were impacted the most. The principal component analysis separated the T2DM and control rats by PC1 and PC3. The bands that enabled that separation were assigned to all four main metabolite groups-amino acids (745-763 cm

Indexed as

Diabetes Mellitus, ExperimentalDiabetes Mellitus, Type 2Machine LearningMacromolecular SubstancesSpectrum Analysis, RamanAnimalsLipidsMalePrincipal Component AnalysisRatsRats, WistarLipidsMacromolecular SubstancesMetabolitesPearson correlationPrincipal component analysisType 2 diabetesVibrational spectroscopy

Identifiers

PMID41081826
PMCPMC12641036

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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.