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teer decrease » greater decrease (Expand Search)
we decrease » _ decrease (Expand Search), a decrease (Expand Search), use decreased (Expand Search)
nn decrease » _ decrease (Expand Search), a decrease (Expand Search), gy decreased (Expand Search)
teer decrease » greater decrease (Expand Search)
we decrease » _ decrease (Expand Search), a decrease (Expand Search), use decreased (Expand Search)
nn decrease » _ decrease (Expand Search), a decrease (Expand Search), gy decreased (Expand Search)
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2901
PCA-CGAN model convergence curve.
Published 2025“…Experiments demonstrate that PCA-CGAN not only achieves stable convergence on a large-scale heterogeneous dataset comprising 43 patients for the first time but also resolves the “dilution effect” problem in data augmentation, avoiding the asymmetric phenomenon where Precision increases while Recall decreases. After data augmentation, the ResNet model’s average F1 score improved significantly, with particularly outstanding performance on rare categories such as atrial premature beats, far surpassing traditional methods like SigCWGAN and TD-GAN. …”
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2902
PCA-CGAN Structure Diagram.
Published 2025“…Experiments demonstrate that PCA-CGAN not only achieves stable convergence on a large-scale heterogeneous dataset comprising 43 patients for the first time but also resolves the “dilution effect” problem in data augmentation, avoiding the asymmetric phenomenon where Precision increases while Recall decreases. After data augmentation, the ResNet model’s average F1 score improved significantly, with particularly outstanding performance on rare categories such as atrial premature beats, far surpassing traditional methods like SigCWGAN and TD-GAN. …”
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2903
Comparison of Model Five-classification Results.
Published 2025“…Experiments demonstrate that PCA-CGAN not only achieves stable convergence on a large-scale heterogeneous dataset comprising 43 patients for the first time but also resolves the “dilution effect” problem in data augmentation, avoiding the asymmetric phenomenon where Precision increases while Recall decreases. After data augmentation, the ResNet model’s average F1 score improved significantly, with particularly outstanding performance on rare categories such as atrial premature beats, far surpassing traditional methods like SigCWGAN and TD-GAN. …”
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2904
PCAECG-GAN K-fold experiment table.
Published 2025“…Experiments demonstrate that PCA-CGAN not only achieves stable convergence on a large-scale heterogeneous dataset comprising 43 patients for the first time but also resolves the “dilution effect” problem in data augmentation, avoiding the asymmetric phenomenon where Precision increases while Recall decreases. After data augmentation, the ResNet model’s average F1 score improved significantly, with particularly outstanding performance on rare categories such as atrial premature beats, far surpassing traditional methods like SigCWGAN and TD-GAN. …”
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2905
PCA-CGAN Pseudocode Table.
Published 2025“…Experiments demonstrate that PCA-CGAN not only achieves stable convergence on a large-scale heterogeneous dataset comprising 43 patients for the first time but also resolves the “dilution effect” problem in data augmentation, avoiding the asymmetric phenomenon where Precision increases while Recall decreases. After data augmentation, the ResNet model’s average F1 score improved significantly, with particularly outstanding performance on rare categories such as atrial premature beats, far surpassing traditional methods like SigCWGAN and TD-GAN. …”
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2906
PCA-CGAN Ablation Experiment Results.
Published 2025“…Experiments demonstrate that PCA-CGAN not only achieves stable convergence on a large-scale heterogeneous dataset comprising 43 patients for the first time but also resolves the “dilution effect” problem in data augmentation, avoiding the asymmetric phenomenon where Precision increases while Recall decreases. After data augmentation, the ResNet model’s average F1 score improved significantly, with particularly outstanding performance on rare categories such as atrial premature beats, far surpassing traditional methods like SigCWGAN and TD-GAN. …”
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2907
Gene expression of neurotransmitter pathways at 6 dp.
Published 2025“…In the 24 hpf dnVDRa induced group we see a significant decrease in glyt1 expression (p < 0.05) and an increase in glyt2 expression (p < 0.05) and in slc32a1 expression (p < 0.01). …”
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2908
PCAIs inhibit NCI-H23 cell line viability.
Published 2024“…However, CRAF/RAF1 phosphorylation decreased by 40%, suggesting significant changes in the KRAS/MAPK signaling patterns. …”
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2909
PCAIs disrupt actin filaments in NCI-H23 cells.
Published 2024“…However, CRAF/RAF1 phosphorylation decreased by 40%, suggesting significant changes in the KRAS/MAPK signaling patterns. …”
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2910
PCAIs suppress 3D NCI-H23 cell invasion.
Published 2024“…However, CRAF/RAF1 phosphorylation decreased by 40%, suggesting significant changes in the KRAS/MAPK signaling patterns. …”
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2911
PCAIs suppress NCI-H23 cancer cell migration.
Published 2024“…However, CRAF/RAF1 phosphorylation decreased by 40%, suggesting significant changes in the KRAS/MAPK signaling patterns. …”
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2912
PCAIs induce apoptosis in NCI-H23 cells.
Published 2024“…However, CRAF/RAF1 phosphorylation decreased by 40%, suggesting significant changes in the KRAS/MAPK signaling patterns. …”
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2913
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2914
Validation of the optimum formula.
Published 2025“…<div><p>The goal of this study was the formulation and optimization by statistical means of bilosomal formulations of axitinib (AXT) in order to improve its anticancer efficacy in a targeted manner. …”
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2915
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2916
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2917
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2918
Parameters of VMR separated by light or dark periods run at 6 dpf.
Published 2025“…(I-K) VMR parameters in dnVDRa induced zebrafish at 72 hpf. There is a significant decrease in distance moved (p < 0.05), a significant decrease in velocity the light period (p < 0.05) and a significant decrease in activity state in the light periods (p < 0.05) in the 72 + fish. …”
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2919
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2920