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Related Concept Videos

Raman Spectroscopy Instrumentation: Overview01:26

Raman Spectroscopy Instrumentation: Overview

294
A conventional Raman spectrophotometer includes a laser source, a sample holding system, a wavelength selector, and a detector.
The monochromatic laser source, typically using visible or near-infrared radiation, generates a highly focused beam of light. This light interacts with the molecules of the sample, scattering some of the light. Liquid and gaseous samples are usually tested in ordinary glass capillaries, while solids can be analyzed as powders packed in capillaries or as potassium...
294

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Machine learning-driven Raman spectroscopy: A novel approach to lipid profiling in diabetic kidney disease.

Adrianna Kryska1, Magdalena Sawic1, Joanna Depciuch2

  • 1Independent Unit of Spectroscopy and Chemical Imaging, Medical University of Lublin, Chodźki 4a, 20-093 Lublin, Poland.

Nanomedicine : Nanotechnology, Biology, and Medicine
|January 24, 2025
PubMed
Summary

This study used Raman spectroscopy and machine learning to detect chemical changes in kidneys caused by Type 2 Diabetes Mellitus (T2DM). The findings offer a precise method for diagnosing diabetic kidney disease.

Keywords:
Diabetes, kidneyLipids metabolic profilingMachine learningRaman spectroscopy

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Area of Science:

  • Biochemistry
  • Spectroscopy
  • Machine Learning

Background:

  • Diabetes mellitus is a chronic metabolic disease with increasing global prevalence.
  • Poorly managed diabetes can lead to organ dysfunction, particularly affecting the kidneys (diabetic nephropathy).
  • Early and accurate diagnosis of diabetic kidney damage is crucial for effective clinical intervention.

Purpose of the Study:

  • To evaluate chemical composition changes in kidneys induced by Type 2 Diabetes Mellitus (T2DM).
  • To combine Raman spectroscopy, lipid profiling, and machine learning (ML) for enhanced diagnostic capabilities.
  • To identify potential spectroscopic markers for diabetic kidney damage.

Main Methods:

  • Utilized Raman spectroscopy to analyze kidney tissue and identify molecular vibrations.
  • Performed biochemical lipid profiling to quantify specific glycerophospholipids.
  • Integrated machine learning algorithms to analyze spectroscopic and lipidomic data for improved accuracy.
  • Investigated correlations between Raman data and lipid profiles in control and T2DM groups.

Main Results:

  • Raman spectroscopy revealed significant differences in lipid content and molecular vibrations, highlighting a 1777 cm⁻¹ band as a potential marker for diabetic kidney damage.
  • Lipid profiling identified distinct variations in phosphatydylocholines and acyl-alkylphosphatidylcholines.
  • Machine learning algorithms demonstrated high accuracy, selectivity, and specificity in detecting T2DM-induced kidney changes.
  • Correlations between Raman data and lipid profiles differed significantly between control and T2DM subjects.

Conclusions:

  • The combined approach of Raman spectroscopy and ML provides a low-cost, rapid, and precise method for diagnosing and monitoring diabetic nephropathy.
  • This integrated technique offers a comprehensive analysis of chemical changes in the kidney.
  • Further validation on a larger cohort is recommended due to the limited sample size in the current study.