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Related Concept Videos

Pulmonary Tuberculosis II01:28

Pulmonary Tuberculosis II

407
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.
Here is a detailed explanation of its pathophysiology:
Transmission: The process begins when a person inhales droplet nuclei containing M. tuberculosis. These are typically released into the air when an individual with pulmonary or...
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Pulmonary Tuberculosis I01:29

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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.
Mode of...
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Pulmonary Tuberculosis III01:31

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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:
466
Pulmonary Tuberculosis V01:28

Pulmonary Tuberculosis V

251
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.
Latent tuberculosis infection occurs when TB bacteria are present in a person's body, but are not causing illness or symptoms. It is not contagious, and preventive treatment is crucial to avoid the...
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Pulmonary Tuberculosis IV01:26

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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...
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Evaluating the performance of multilayer perceptron algorithm for tuberculosis disease Raman data.

Rahat Ullah1, Saranjam Khan2, Zahra Ali3

  • 1Agri. & Biophotonics Division, National Institute of Lasers and Optronics College, Pakistan Institute of Engineering and Applied Sciences, Islamabad 45650, Pakistan.

Photodiagnosis and Photodynamic Therapy
|May 24, 2022
PubMed
Summary

This study shows that Raman spectroscopy combined with a multilayer perceptron (MLP) algorithm can help diagnose tuberculosis (TB) by analyzing blood serum. This approach offers a promising tool for rapid infectious disease detection.

Keywords:
BiomarkerMolecular diagnosisPhenylalanineRamanTuberculosisβ-carotene

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Area of Science:

  • Biomedical Spectroscopy
  • Computational Biology
  • Infectious Disease Diagnostics

Background:

  • Rapid and accurate diagnosis of bacterial infectious diseases, like tuberculosis (TB), remains a significant challenge in clinical practice.
  • Early pathogen identification is crucial for effective treatment initiation and improved patient outcomes.
  • Current molecular diagnostic methods face limitations in speed and accessibility for widespread application.

Purpose of the Study:

  • To evaluate the performance of the multilayer perceptron (MLP) algorithm using Raman spectroscopic data for diagnosing tuberculosis.
  • To differentiate between TB-positive, TB-negative, and healthy individuals based on blood serum analysis.
  • To assess the potential of Raman spectroscopy combined with artificial intelligence for real-time disease prediction.

Main Methods:

  • Analysis of blood serum samples from active TB patients, recovered (TB-negative) individuals, and healthy controls.
  • Utilized Raman spectroscopy to measure spectral data, focusing on peak intensities at specific Raman shifts (1001, 1152, 1282, 1430, 1475, 1690 cm⁻¹).
  • Employed a multilayer perceptron (MLP) artificial neural network algorithm for classification, validated using 5-fold cross-validation.

Main Results:

  • The MLP algorithm successfully classified samples based on differences in Raman peak intensities, correlating with biomolecular concentrations (e.g., phenylalanine, β-carotene, proteins).
  • Achieved sensitivity ranging from 62% to 92% and specificity ranging from 81% to 88% across different comparison groups (Control vs. TB+, Control vs. TB-, TB+ vs. TB-).
  • Demonstrated the model's ability to distinguish between the different sample groups based on spectral signatures.

Conclusions:

  • Raman spectroscopy, when integrated with statistical algorithms like MLP, shows significant potential for the rapid diagnosis of tuberculosis.
  • The developed model provides a feasible approach for real-time prediction of unknown samples after training.
  • This technique offers a promising non-invasive diagnostic tool for infectious diseases, potentially improving early detection rates.