Cell recognition based on atomic force microscopy and modified residual neural network

Wang, Junxi; Gao, Mingyan; Yang, L.; Huang, Yu-Xi; Wang, Jiahe; Wang, Bowei; Song, Guicai; Wang, Zuobin

doi:10.1016/j.jsb.2023.107991

Cited by 2 publications

(1 citation statement)

References 41 publications

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“…13 To overcome this problem, AFM is used to characterize the structural and mechanical properties of cells and further identify the differences between tumor cells and normal cells. 14 It enables researchers to perform high-resolution visualization and quantitative studies of biological samples in situ in a liquid environment. It is very important to establish appropriate physiological conditions (such as the buffer solution and temperature) when characterizing the functional and mechanical properties of biological samples.…”

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confidence: 99%

Determining the degree of chromosomal instability in breast cancer cells by atomic force microscopy

Wang,

Dong,

Yang

et al. 2024

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Section: Introductionmentioning

confidence: 99%

Determining the degree of chromosomal instability in breast cancer cells by atomic force microscopy

Wang,

Dong,

Yang

et al. 2024

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Utilization of convolutional neural networks to analyze microscopic images for high-throughput screening of mesenchymal stem cells

Liu,

Du,

et al. 2024

Open Life Sciences

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This work investigated the high-throughput classification performance of microscopic images of mesenchymal stem cells (MSCs) using a hyperspectral imaging-based separable convolutional neural network (CNN) (H-SCNN) model. Human bone marrow mesenchymal stem cells (hBMSCs) were cultured, and microscopic images were acquired using a fully automated microscope. Flow cytometry (FCT) was employed for functional classification. Subsequently, the H-SCNN model was established. The hyperspectral microscopic (HSM) images were created, and the spatial-spectral combined distance (SSCD) was employed to derive the spatial-spectral neighbors (SSNs) for each pixel in the training set to determine the optimal parameters. Then, a separable CNN (SCNN) was adopted instead of the classic convolutional layer. Additionally, cultured cells were seeded into 96-well plates, and high-functioning hBMSCs were screened using both manual visual inspection (MV group) and the H-SCNN model (H-SCNN group), with each group consisting of 96 samples. FCT served as the benchmark to compare the area under the curve (AUC), F1 score, accuracy (Acc), sensitivity (Sen), specificity (Spe), positive predictive value (PPV), and negative predictive value (NPV) between the manual and model groups. The best classification Acc was 0.862 when using window size of 9 and 12 SSNs. The classification Acc of the SCNN model, ResNet model, and VGGNet model gradually increased with the increase in sample size, reaching 89.56 ± 3.09, 80.61 ± 2.83, and 80.06 ± 3.01%, respectively at the sample size of 100. The corresponding training time for the SCNN model was significantly shorter at 21.32 ± 1.09 min compared to ResNet (36.09 ± 3.11 min) and VGGNet models (34.73 ± 3.72 min) (P < 0.05). Furthermore, the classification AUC, F1 score, Acc, Sen, Spe, PPV, and NPV were all higher in the H-SCNN group, with significantly less time required (P < 0.05). Microscopic images based on the H-SCNN model proved to be effective for the classification assessment of hBMSCs, demonstrating excellent performance in classification Acc and efficiency, enabling its potential to be a powerful tool in future MSCs research.

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Cell recognition based on atomic force microscopy and modified residual neural network

Cited by 2 publications

References 41 publications

Determining the degree of chromosomal instability in breast cancer cells by atomic force microscopy

Determining the degree of chromosomal instability in breast cancer cells by atomic force microscopy

Utilization of convolutional neural networks to analyze microscopic images for high-throughput screening of mesenchymal stem cells

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