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Correction: Fast label-free recognition of NRBCs by deep-learning visual object detection and single-cell Raman
Teng Fang1, Pengbo Yuan2, Chen Gong2,3
1Key Laboratory for the Physics and Chemistry of Nanodevices, School of Electronics, Peking University, Beijing 100871, China. yap@pku.edu.cn.
The Analyst
|April 28, 2022
Summary
This correction clarifies a previous study on identifying nucleated red blood cells (NRBCs) using deep learning and Raman spectroscopy. The updated information ensures accurate reporting of this advanced cell detection methodology.
Area of Science:
- Biomedical Engineering
- Analytical Chemistry
- Computational Biology
Context:
- Accurate identification of nucleated red blood cells (NRBCs) is crucial for various diagnostic applications.
- Traditional methods for NRBC detection can be time-consuming and labor-intensive.
- Advancements in spectroscopy and machine learning offer potential for rapid, label-free cell analysis.
Purpose:
- To provide a correction to the original publication regarding the methodology and findings.
- To ensure the accurate representation of the deep-learning visual object detection and single-cell Raman spectroscopy techniques.
- To maintain the integrity and reproducibility of research in cell analysis.
Summary:
- The correction addresses specific details within the study on fast label-free recognition of NRBCs.
- It clarifies aspects of the deep-learning visual object detection and single-cell Raman spectroscopy methods used.
- Ensures the presented data and conclusions are accurately attributed and interpreted.
Impact:
- Facilitates more reliable use of the described NRBC detection method in research and clinical settings.
- Upholds scientific accuracy and transparency in the field of advanced cell analysis.
- Supports the continued development of label-free, high-throughput cell identification technologies.
