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Related Concept Videos

Restorative Care01:19

Restorative Care

Restorative care is provided once a patient has been discharged from a healthcare facility and requires additional services. The additional services include home care, rehabilitation programs, and extended care. Restorative care centers help the patient regain their previous level of functioning or acquire a new level of functioning due to the incapacitating effects of a disease or a disability. It aims to assist patients in enhancing their quality of life by encouraging independence,...
Continuing Care01:25

Continuing Care

Continuing care describes the variety of health, personal, and social services provided over a prolonged period. The need for continuing care is increasing because people are living longer. Many people do not have families or others to care for them. Continuing care is mainly for patients who are disabled, functionally dependent, or suffering from a terminal disease. It is available within institutional settings or in homes. Examples include nursing centers or facilities, assisted living,...
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Nursing Process for Patient and Caregiver Teaching I: Assessment and Diagnosis

The nursing process provides a clinical decision-making framework for patients and families to establish and implement a personalized care plan. Since part of the nurse's duties is to teach patients, the steps of the nursing process are the most effective way to approach instruction. The nursing process and the teaching-learning process are inextricably linked.
It is critical to determine the patient's learning needs during the assessment. Determination of learning needs compounds data from the...
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Long-Term Care Facilities
Alzheimer's Disease: Overview01:26

Alzheimer's Disease: Overview

Alzheimer's Disease (AD) is a continually advancing neurodegenerative disorder, distinguished by escalating memory loss, cognitive dysfunction, and dementia. The disease unfolds in three stages: preclinical, mild cognitive impairment (MCI), and dementia. Its onset is insidious, and the progression gradual, with the cause not well explained by other disorders.
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The progression of dementia is generally gradual.

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Related Experiment Video

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Deep-Learning-Based Segmentation of Cells and Analysis (DL-SCAN).

Alok Bhattarai1, Jan Meyer2, Laura Petersilie2

  • 1Department of Physics, University of South Florida, Tampa, FL 33647, USA.

Biomolecules
|November 27, 2024
PubMed
Summary
This summary is machine-generated.

DL-SCAN, a deep learning tool, automates fluorescence microscopy image analysis for cell segmentation and ion dynamics studies. It offers consistent, unbiased results, overcoming limitations of traditional methods for analyzing low signal-to-noise ratio data.

Keywords:
cell segmentationlive cell imagingstreamlittracking ion dynamicstracking morphological changes

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Area of Science:

  • Cellular biology
  • Neuroscience
  • Biophysics

Background:

  • Fluorescence microscopy is crucial for studying cellular processes, but traditional image analysis is manual, time-consuming, and prone to user bias.
  • Low signal-to-noise ratio (SNR) in fluorophores complicates analysis, often requiring extensive training and limiting automation.
  • Existing software lacks automation for diverse datasets and struggles with low-brightness fluorophores.

Purpose of the Study:

  • To develop an automated tool, DL-SCAN, for segmenting and analyzing fluorescence microscopy images using deep learning.
  • To overcome limitations of manual analysis, including user bias and time inefficiency, especially for low SNR data.
  • To enable objective, efficient, and reproducible analysis of cellular ion dynamics in neuroscience research.

Main Methods:

  • Developed DL-SCAN, a deep learning-based software for automated segmentation of regions of interest in fluorescence microscopy images.
  • Validated DL-SCAN using synthetic image stacks with varying SNR to assess cell identification accuracy.
  • Applied DL-SCAN to analyze experimental Na+ and Ca2+ imaging data from mouse brain tissue slices under chemical ischemia.

Main Results:

  • DL-SCAN successfully automates cell identification and segmentation in fluorescence microscopy images.
  • The tool demonstrated consistent and reproducible analysis of cellular ion dynamics (Na+, Ca2+) in neuronal and astrocyte imaging data.
  • Results showed DL-SCAN is free from user bias, enabling rapid and objective data analysis.

Conclusions:

  • DL-SCAN provides an efficient, automated, and unbiased solution for analyzing fluorescence microscopy data, particularly for low SNR conditions.
  • The open-source nature of DL-SCAN allows for customization and extension to diverse cell types and ion dynamics studies.
  • This tool significantly enhances the speed and reliability of cellular imaging analysis in neuroscience and related fields.