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Related Concept Videos

Capillary Electrophoresis: Instrumentation01:20

Capillary Electrophoresis: Instrumentation

Capillary electrophoresis instrumentation typically consists of several key components. A high-voltage power supply generates the electric field necessary for the separation by connecting to an anode (the positively charged electrode) and a cathode (the negatively charged electrode) located in buffer reservoirs at each end of the capillary tube. The system includes a sample vial, a fused silica capillary tube coated with polyimide for mechanical strength through which the sample components...
Capillary Electrophoresis: Applications01:30

Capillary Electrophoresis: Applications

Capillary electrophoretic separations offer various modes, each with unique applications. These modes include capillary zone electrophoresis, capillary gel electrophoresis, capillary array electrophoresis, capillary isoelectric focusing, capillary isotachophoresis, micellar electrokinetic chromatography, and capillary electrochromatography.
Capillary zone electrophoresis (CZE) separates ionic components based on their electrophoretic mobility. It has been used to separate proteins, amino acids,...
A Single-Component System01:24

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In the field of chemistry, the terms "component" and "phase" hold significant importance. A component refers to a chemically distinct substance in a system that has specific properties. It is chemically homogeneous, meaning it has the same properties throughout. For example, in a mixture of salt and water, both salt and water are considered separate components because they have different chemical properties.On the other hand, a phase is a form of matter that has a consistent chemical...
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Electrochemical Systems

Electrochemical systems provide a fascinating insight into the dynamic interplay of charged species within various phases. One notable example is the interaction between a membrane permeable to K⁺ ions but not to Cl⁻ ions, separating an aqueous KCl solution from pure water. As K⁺ ions diffuse through the membrane, they generate net charges on each phase, leading to a potential difference between them.Similarly, when a piece of Zn is immersed in an aqueous ZnSO₄ solution, the Zn metal, composed...
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Related Experiment Video

Updated: May 12, 2026

A Reproducible Computerized Method for Quantitation of Capillary Density using Nailfold Capillaroscopy
05:17

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YOLOv8-Based System for Nail Capillary Detection on a Single-Board Computer.

Seda Arslan Tuncer1, Muhammed Yildirim2, Taner Tuncer3

  • 1Faculty of Engineering, Software Engineering, Firat University, 23119 Elazığ, Turkey.

Diagnostics (Basel, Switzerland)
|September 14, 2024
PubMed
Summary

This study introduces an AI system using YOLOv8s for objective nail capillary analysis, aiding early diagnosis of systemic sclerosis and Raynaud's disease.

Keywords:
YOLOv8artificial intelligencenail capillaryproximal nail fold

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Dermatology

Background:

  • Nail capillaroscopy identifies capillary changes crucial for diagnosing systemic sclerosis and Raynaud's disease.
  • Manual capillaroscopic analysis is subjective, time-consuming, and yields inconsistent results.
  • Objective and efficient methods are needed to assess capillary morphology (diameter, density, etc.).

Purpose of the Study:

  • To develop and evaluate an AI-based system for automated nail bed capillary detection.
  • To quantify capillary number, thickness, and density for improved diagnostic accuracy.
  • To overcome limitations of manual capillaroscopic evaluation.

Main Methods:

  • A YOLOv8s-based artificial intelligence system was developed.
  • The system analyzes recorded microscope images of nail bed capillaries.
  • It utilizes database systems and software on a Single-Board Computer (SBC).

Main Results:

  • The AI system achieved a mean Average Precision (mAP) of 0.882.
  • The F1 score for capillary detection was 0.83.
  • The system demonstrated proficiency in detecting capillary number, thickness, and density.

Conclusions:

  • The proposed AI system offers objective and efficient nail capillary analysis.
  • It shows promise for improving early diagnosis and monitoring of systemic sclerosis and Raynaud's disease.
  • Automated capillaroscopy can enhance diagnostic consistency and reduce labor.