Structural Diagrams of RF Model and ResNet Model. by Chao Tang (10925)

“…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. …”

PCA-CGAN model convergence curve. by Chao Tang (10925)

“…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. …”

PCA-CGAN Structure Diagram. by Chao Tang (10925)

“…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. …”

Comparison of Model Five-classification Results. by Chao Tang (10925)

“…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. …”

PCAECG-GAN K-fold experiment table. by Chao Tang (10925)

“…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. …”

PCA-CGAN Pseudocode Table. by Chao Tang (10925)

“…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. …”

PCA-CGAN Ablation Experiment Results. by Chao Tang (10925)

“…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. …”

Data that underlies this paper. by Henan Si (21512689)

“…</p><p>Conclusions/significance</p><p>This study unveils the dual role of IL-18 in human sporotrichosis caused by <i>S. globosa</i>—amplifying both Th1 and Th2 responses but ultimately driving pathogenic Th2 polarization through IL-2 crosstalk. …”

Kaplan-Meier survival analysis comparing patients admitted before or during the COVID-19 period. by Rajkumar Rajendram (22505442)

Diagnostic criteria for Alcoholic cardiomyopathy. by Fei Yan (128878)

“…</p><p><b>Results:</b> Globally, ACM burden showed significant declines from 1990 to 2021, with age-standardized rates decreasing by 22.5-37.1% across prevalence, mortality and disability measures. …”

Fig 1 - by Seung-Jae Kim (6167222)

LC_MS/MS analysis for OGG1 interactomes. by Lang Pan (5145221)

“…Further investigation revealed that viral ribonucleoprotein complexes specifically exploit OGG1. Importantly, inhibiting OGG1’s ability to recognize 8-oxoGua significantly decreases RSV progeny production. …”

The protective effects of neferine against paracetamol-induced liver injury are associated with the activation of SIRT1/Nrf2/HO-1 signaling pathway and inhibition of NF-kappa B/TNF... by Mohammed A. Altowijri (22461945)

“…</p><p>NEF inhibited the upregulation of NF-κB, TNF-α, IL-1β, and IL-6.</p><p>NEF pretreatment decreased mRNA expression of COX-II and iNOS.…”

Differential abundance comparisons of alfalfa rhizospheres when grown alone (1 plant) versus in medium density (24 plants) and high density (48 plants) polycultures. by Derek R. Newberger (20623964)

S1 Data - by Yukai Luo (14172429)

“…The findings from the 16S rDNA assay indicate that UC-MSCs treatment effectively lower α-diversity induced such as Chao 1 and ACE, as well as β-diversity, leading to a decrease in microbiota abundance. …”

Percentages of pediatricians and obstetricians/gynecologists working long hours, based on the desired working environment. by Mai Nakagawa (20284927)

<i>SEL1L</i> variants affect proteasome inhibition sensitivity. by Travis K. Tu’ifua (22008843)

“…<b>(C)</b> <i>NGLY1 WT</i> larvae show no significant decrease in size with 1μM BTZ treatment, regardless of <i>SEL1L</i> genotype (Larval size on DMSO: <i><i>SEL1L</i></i>^<i>+/+</i> 25.75 ± 6.91, <i>SEL1L</i>^{<i>S780P/+</i>} 23.74 ± 6.94, and <i>SEL1L</i>^{<i>Δ806-809/+</i>} 26.42 ± 4.29; larval size on BTZ: <i><i>SEL1L</i></i>^<i>+/+</i> 24.75 ± 5.63, <i>SEL1L</i>^{<i>S780P/+</i>} 20.96 ± 7.38, and <i>SEL1L</i>^{<i>Δ806-809/+</i>} 25.70 ± 5.23).…”

Minimal data set. by Daan P. van den Brink (9900073)

“…Other markers of endothelial injury or inflammation were not significantly different between groups. No significant differences were observed in histologic injury scores and wet-to-dry ratios.…”

Demographic and ocular features. by Mingxi Shao (10066570)

“…Adjusting for age and gender, the serum TAS (OR = 0.07, 95% CI 0.01–0.85, <i><i>p</i></i> = 0.037), H₂O₂ (OR = 1.21, 95% CI 1.09–1.35, <i><i>p</i></i> = 0.001) and MDA (OR = 1.17, 95% CI 1.00–1.34, <i><i>p</i></i> = 0.034) were determined to be independent risk/protective factors for PCG. …”

Machine learning model to diagnose PCG. by Mingxi Shao (10066570)

“…Adjusting for age and gender, the serum TAS (OR = 0.07, 95% CI 0.01–0.85, <i><i>p</i></i> = 0.037), H₂O₂ (OR = 1.21, 95% CI 1.09–1.35, <i><i>p</i></i> = 0.001) and MDA (OR = 1.17, 95% CI 1.00–1.34, <i><i>p</i></i> = 0.034) were determined to be independent risk/protective factors for PCG. …”