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Related Concept Videos

Pulmonary Tuberculosis IV01:26

Pulmonary Tuberculosis IV

Tuberculosis, more commonly referred to as TB, is an infectious disease stemming from Mycobacterium tuberculosis. While it primarily impacts the lungs, TB can also affect other body areas. Given its severity and global impact, timely and accurate diagnosis is crucial for controlling its spread and improving patient outcomes.
Several diagnostic approaches are used to detect TB. The conventional method is the Tuberculin Skin Test (TST), also known as the Mantoux test. However, this method has...
Rapid Identification of Pathogens01:25

Rapid Identification of Pathogens

MALDI-TOF MS has transformed clinical microbiology by offering a rapid and reliable method for pathogen identification. The traditional approach to microbial identification typically involves time-consuming culture techniques and biochemical tests, which can delay the initiation of appropriate antimicrobial therapy. MALDI-TOF MS avoids these delays by using characteristic ribosomal protein mass patterns of microbial cells, enabling accurate species-level identification within minutes.Principle...
Pulmonary Tuberculosis I01:29

Pulmonary Tuberculosis I

Tuberculosis, often called TB, is a contagious illness primarily caused by Mycobacterium tuberculosis. It mainly affects the lung parenchyma but can also impact other body parts.
Causative Organism
The primary infectious agent causing tuberculosis is Mycobacterium tuberculosis, a slow-growing, acid-fast, aerobic rod that exhibits sensitivity to heat and ultraviolet light. Instances of Mycobacterium bovis and Mycobacterium avium contributing to the development of TB infection are rare.
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Pulmonary Tuberculosis V01:28

Pulmonary Tuberculosis V

Medical management of tuberculosis (TB) patients involves a comprehensive approach that includes diagnosis, treatment, and monitoring. The specific strategies can vary depending on the type of tuberculosis (latent or active), the patient's overall health status, and other considerations.
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Pulmonary Tuberculosis II01:28

Pulmonary Tuberculosis II

Tuberculosis, or TB, is a bacterial infectious disease caused by Mycobacterium tuberculosis. While its primary impact is on the lungs, leading to pulmonary tuberculosis, it can also affect various other organs, a condition referred to as extrapulmonary tuberculosis.
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Pulmonary Tuberculosis III01:31

Pulmonary Tuberculosis III

Tuberculosis (TB) is a contagious infection primarily affecting the lung parenchyma but which can also affect other body parts. TB can be classified based on disease development, presentation, and the affected anatomical site.
The first classification is based on the development of the disease, and it includes the following categories:

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Related Experiment Video

Updated: Jul 16, 2026

Exploring the Application of Surface-enhanced Raman Scattering-based Biosensing of Individual sEVs in Disease Diagnosis and Therapeutics
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Published on: March 13, 2026

TB-SERS analyzer: Analysis tool for tuberculosis prediction based on Raman spectroscopy with machine learning and

Jukgarin Eisiri1,2, Chadatan Juntagran1,2, Kanwara Trisakul2,3

  • 1Multidisciplinary Department, Graduate School, Khon Kaen University, Khon Kaen, Thailand.

Plos Computational Biology
|July 14, 2026
PubMed
Summary

A new software tool, TB-SERS Analyzer, uses machine learning and Raman spectroscopy to rapidly screen for tuberculosis (TB). This accessible tool analyzes SERS data for quicker and more efficient TB diagnosis, improving upon current methods.

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Exploring the Application of Surface-enhanced Raman Scattering-based Biosensing of Individual sEVs in Disease Diagnosis and Therapeutics
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Published on: February 16, 2020

Area of Science:

  • Biomedical Engineering
  • Computational Biology
  • Spectroscopy

Background:

  • Raman spectroscopy (RS) and surface-enhanced Raman spectroscopy (SERS) show promise for clinical diagnostics, particularly in tuberculosis (TB) detection.
  • Existing diagnostic methods for TB can be time-consuming and invasive.
  • There is a need for specialized software to analyze RS/SERS data for TB diagnosis.

Purpose of the Study:

  • To develop a user-friendly software tool, TB-SERS Analyzer, for tuberculosis prediction using SERS data.
  • To integrate machine learning (ML) and one-dimensional convolutional neural network (1D-CNN) models for automated TB diagnosis.
  • To create an accessible platform for rapid, non-invasive TB screening.

Main Methods:

  • Development of TB-SERS Analyzer, a Python-based software with a graphical user interface (GUI).
  • Establishment of a reference database of 1,000 plasma samples (500 IGRA-positive, 500 IGRA-negative) using interferon-gamma release assay (IGRA).
  • Training and optimization of ML and 1D-CNN models using five-fold stratified cross-validation, evaluating seven algorithms for TB classification.

Main Results:

  • The 1D-CNN model achieved 82.00% sensitivity and 76.00% specificity in the validation set (n=200).
  • In a blinded external test (n=20), the model demonstrated 80.00% sensitivity and 100% specificity.
  • TB-SERS Analyzer provides diagnostic reports in under 10 seconds per sample, integrating data extraction, preparation, analysis, and report generation modules.

Conclusions:

  • TB-SERS Analyzer is an effective and accessible tool for TB screening, combining SERS technology with ML and 1D-CNN models.
  • The software offers high efficiency and rapid results for TB diagnosis.
  • TB-SERS Analyzer is freely available on GitHub, facilitating wider adoption and research.