ArticleBiomedical optics express2023
Dynamic light scattering microscopy sensing mitochondria dynamics for label-free detection of triple-negative breast cancer enhanced by deep learning.
Article in Biomedical optics express, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
5 citing papers in PubMed, 5 citations in OpenAlex.
- The multifaceted roles of mitochondria and their therapeutic transformation: a new perspective on triple-negative breast cancer treatment.Frontiers in oncology · 2026Article
- Label-Free Cancer Detection Methods Based on Biophysical Cell Phenotypes.Bioengineering (Basel, Switzerland) · 2025Review
- Electrochemical-assisted scattering imaging system for lymphoma cell classification using machine learning.Biomedical optics express · 2025Article
- An amplification mechanism for weak ELF magnetic fields quantum-bio effects in cancer cells.Scientific reports · 2025Article
- Dynamic Light Scattering in Biomedical Applications: feature issue introduction.Biomedical optics express · 2024Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We established a deep learning-based dynamic light scattering (DLS) microscopy sensing mitochondria dynamic for label-free identification of triple-negative breast cancer (TNBC) cells. The capacity of DLS microscopy to detect the intracellular motility of subcellular scatters was verified with the analysis of the autocorrelation function. We also conducted an in-depth examination of the impact of mitochondrial dynamics on DLS within TNBC cells, employing confocal fluorescent imaging to visualize the morphology of the mitochondria. Furthermore, we applied the DLS microscopy incorporating the two-stream deep learning method to differentiate the TNBC subtype and HER2 positive breast cancer subtype, with the classification accuracy achieving 0.89.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.