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Related Concept Videos

Sound Intensity00:58

Sound Intensity

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The loudness of a sound source is related to how energetically the source is vibrating, consequently making the molecules of the propagation medium vibrate. To measure the loudness of a source, the physical quantity of interest is the intensity. This is defined as the energy emitted per unit of time per unit of area perpendicular to the sound wave's propagation direction. Since the total energy is greater if the source vibrates for a longer duration and over a larger area, dividing the...
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Sound Intensity Level00:53

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Humans perceive sound by hearing. The human ear helps sound waves reach the brain, which then interprets the waves and creates the perception of hearing. The loudness of the environment in which a person is located determines whether they can distinguish between different sound sources.
The human ear can perceive an extensive range of sound intensity, necessitating the use of the logarithmic scale to define a physical quantity—the intensity level. It is a ratio of two intensities and...
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Nursing Diagnosis01:22

Nursing Diagnosis

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Following assessment, a nursing diagnosis is the next step in the nursing process. It begins after the nurse has collected and recorded the patient data. The purpose of diagnosing is to identify how the client responds to actual or potential health processes, identify factors that bestow or that cause health problems, the etiologies, and identify resources or strengths the individual, group, or community can draw on to prevent or resolve problems.
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Force Classification01:22

Force Classification

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Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
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Classification of Neurotransmitters01:30

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Neurotransmitters play a crucial role in the communication between neurons in the autonomic nervous system. Neurons in the autonomic nervous system can be cholinergic or adrenergic depending on the neurotransmitters synthesized. Cholinergic neurons use acetylcholine as their primary neurotransmitter. This includes all the preganglionic fibers of the sympathetic and pre- and postganglionic fibers of the parasympathetic nervous systems. In addition, neurons of the somatic nervous system also use...
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Classification of Leukocytes01:30

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Leukocytes are classified into two groups based on the presence or absence of cytoplasmic granules. Granular leukocytes, which contain granules, belong to the myeloid lineage and are divided into three subtypes: neutrophils, eosinophils, and basophils. These cells are roughly spherical and characterized by the granules in their cytoplasm.
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Related Experiment Video

Updated: Feb 1, 2026

Simultaneous Distinction of Monospecific and Mixed DFS70 Patterns During ANA Screening with a Novel HEp-2 ELITE/DFS70 Knockout Substrate
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Simultaneous Distinction of Monospecific and Mixed DFS70 Patterns During ANA Screening with a Novel HEp-2 ELITE/DFS70 Knockout Substrate

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A computer-aided diagnosis system for HEp-2 fluorescence intensity classification.

Mario Merone1, Carlo Sansone2, Paolo Soda1

  • 1Unit of Computer Systems and Bioinformatics, Department of Engineering, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128 Rome, Italy.

Artificial Intelligence in Medicine
|December 4, 2018
PubMed
Summary
This summary is machine-generated.

A new computer-aided diagnosis (CAD) system effectively classifies antinuclear antibody (ANA) detection using HEp-2 cells, matching expert performance. This automated approach overcomes limitations of traditional indirect immunofluorescence (IIF) testing.

Keywords:
Computer-aided diagnosisDeep learningHEp-2 samplesIndirect immunofluorescenceInvariant Scattering Convolutional Networks

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Anti-Nuclear Antibody Screening Using HEp-2 Cells
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Related Experiment Videos

Last Updated: Feb 1, 2026

Simultaneous Distinction of Monospecific and Mixed DFS70 Patterns During ANA Screening with a Novel HEp-2 ELITE/DFS70 Knockout Substrate
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Simultaneous Distinction of Monospecific and Mixed DFS70 Patterns During ANA Screening with a Novel HEp-2 ELITE/DFS70 Knockout Substrate

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Anti-Nuclear Antibody Screening Using HEp-2 Cells
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Anti-Nuclear Antibody Screening Using HEp-2 Cells

Published on: June 23, 2014

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Area of Science:

  • Immunology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Indirect immunofluorescence (IIF) on HEp-2 cells is standard for antinuclear antibody (ANA) detection.
  • IIF is time-consuming, subjective, and requires specialized personnel.
  • Deep neural networks offer automated feature extraction for complex data.

Purpose of the Study:

  • To develop a computer-aided diagnosis (CAD) system to automate HEp-2 cell fluorescence intensity classification.
  • To address limitations of traditional IIF methods for ANA detection.

Main Methods:

  • A computer-aided diagnosis (CAD) system utilizing an Invariant Scattering Convolutional Network (Scatnet) for HEp-2 image representation.
  • Development of a gold standard computation method to handle inter-observer variability in expert annotations.
  • Training a Support Vector Machine (SVM) using Scatnet features and gold standard reliability scores.

Main Results:

  • The CAD system was evaluated on a dataset of 1771 images from three medical centers.
  • Performance was compared against existing literature approaches on new and public datasets (MIVIA, I3Asel).
  • The system demonstrated effectiveness in recognizing positive, weak positive, and negative samples.

Conclusions:

  • The proposed CAD system achieves performance comparable to that of medical experts.
  • This automated approach offers a reliable and efficient alternative for ANA detection using HEp-2 cells.