Enhanced multi-component module. by Yuanming Ding (12842858)

“…The model achieves a significant enhancement in prediction accuracy through the introduction of the attention-based Personalized-enhanced Fusion Graph Convolutional Network (aPFGCN) and the Temporal Convolutional Bidirectional Long Short-Term Memory (TCBiL) module. …”

The architecture of the TCBiL. by Yuanming Ding (12842858)

“…The model achieves a significant enhancement in prediction accuracy through the introduction of the attention-based Personalized-enhanced Fusion Graph Convolutional Network (aPFGCN) and the Temporal Convolutional Bidirectional Long Short-Term Memory (TCBiL) module. …”

Detail of the encoder. by Yuanming Ding (12842858)

“…The model achieves a significant enhancement in prediction accuracy through the introduction of the attention-based Personalized-enhanced Fusion Graph Convolutional Network (aPFGCN) and the Temporal Convolutional Bidirectional Long Short-Term Memory (TCBiL) module. …”

Detail of the Fourier transform. by Yuanming Ding (12842858)

“…The model achieves a significant enhancement in prediction accuracy through the introduction of the attention-based Personalized-enhanced Fusion Graph Convolutional Network (aPFGCN) and the Temporal Convolutional Bidirectional Long Short-Term Memory (TCBiL) module. …”

Detail of the decoder. by Yuanming Ding (12842858)

“…The model achieves a significant enhancement in prediction accuracy through the introduction of the attention-based Personalized-enhanced Fusion Graph Convolutional Network (aPFGCN) and the Temporal Convolutional Bidirectional Long Short-Term Memory (TCBiL) module. …”

Encoder-decoder architecture. by Yuanming Ding (12842858)

“…The model achieves a significant enhancement in prediction accuracy through the introduction of the attention-based Personalized-enhanced Fusion Graph Convolutional Network (aPFGCN) and the Temporal Convolutional Bidirectional Long Short-Term Memory (TCBiL) module. …”

Dataset description. by Yuanming Ding (12842858)

“…The model achieves a significant enhancement in prediction accuracy through the introduction of the attention-based Personalized-enhanced Fusion Graph Convolutional Network (aPFGCN) and the Temporal Convolutional Bidirectional Long Short-Term Memory (TCBiL) module. …”

Determination of bladder blood flow in LETO and OLETF rats. by Takafumi Kabuto (14797727)

Study design. by Moazzam Tanveer (20775814)

“…<div><p>Background</p><p>Childhood obesity poses a significant public health challenge, yet effective school-based physical activity (PA) interventions remain scarce, especially in Pakistan. …”

Measurements of the amount of ATP released by bladder distention and VNUT expression in the bladder. by Takafumi Kabuto (14797727)

Prevaelnce of different pathogens by location. by Amete Mihret Teshale (12072758)

“…<div><p>Diarrheal illness remains a major global health challenge, causing millions of deaths annually. …”

Frontier Analysis Based on ASDR and SDI. by Guanghui Yu (423945)

“…<div><p>Background and Objectives</p><p>Hypertension is a major risk factor for aortic aneurysm (AA), but the global, regional, and national patterns of its related disease burden are not well studied. …”

Model vs. WKY group KEGG pathways (Top 15). by Xiao Zhang (152326)

“…</p><p>Results</p><p>From Day 1–5, compared with the WKY group, both the Model and SHR groups exhibited shortened incubation periods and slower average swimming speeds (<i><i>P</i></i> < 0.01); On day 6, compared with the WKY group, the Model group showed a significant decrease in the number of platform crossings (<i><i>P</i></i> < 0.05), time spent in the target quadrant (<i><i>P</i></i> < 0.05), and total distance traveled in the target quadrant (<i><i>P</i></i> < 0.05). …”

Behavioral data. by Xiao Zhang (152326)

“…</p><p>Results</p><p>From Day 1–5, compared with the WKY group, both the Model and SHR groups exhibited shortened incubation periods and slower average swimming speeds (<i><i>P</i></i> < 0.01); On day 6, compared with the WKY group, the Model group showed a significant decrease in the number of platform crossings (<i><i>P</i></i> < 0.05), time spent in the target quadrant (<i><i>P</i></i> < 0.05), and total distance traveled in the target quadrant (<i><i>P</i></i> < 0.05). …”

OPLS-DA model parameter. by Xiao Zhang (152326)

“…</p><p>Results</p><p>From Day 1–5, compared with the WKY group, both the Model and SHR groups exhibited shortened incubation periods and slower average swimming speeds (<i><i>P</i></i> < 0.01); On day 6, compared with the WKY group, the Model group showed a significant decrease in the number of platform crossings (<i><i>P</i></i> < 0.05), time spent in the target quadrant (<i><i>P</i></i> < 0.05), and total distance traveled in the target quadrant (<i><i>P</i></i> < 0.05). …”

Model vs. SHR group KEGG pathways (Top 15). by Xiao Zhang (152326)

“…</p><p>Results</p><p>From Day 1–5, compared with the WKY group, both the Model and SHR groups exhibited shortened incubation periods and slower average swimming speeds (<i><i>P</i></i> < 0.01); On day 6, compared with the WKY group, the Model group showed a significant decrease in the number of platform crossings (<i><i>P</i></i> < 0.05), time spent in the target quadrant (<i><i>P</i></i> < 0.05), and total distance traveled in the target quadrant (<i><i>P</i></i> < 0.05). …”

Network pharmacology analysis predicted the targets of NS on atherosclerosis. by Yihua Wang (491766)

Primers used for RT-qPCR. by Yihua Wang (491766)

“…The results indicated that NS combination promoted autophagy by inhibiting the PI3K/Akt/mTOR pathway. This significantly alleviated inflammation, reduced apoptosis, and decreasing lipid accumulation, thereby improving the pathological progression of atherosclerosis. …”

Characteristics of the included studies. by Si He (3236646)

“…The overall results revealed that NPWT significantly decreased the sternal wound reinfection (SWRI) rate (RR [95% CI] = 0.179 [0.099 to 0.323], 95% prediction interval [PI]: 0.082 to 0.442), in-hospital mortality (RR [95% CI] = 0.242 [0.149 to 0.394], 95% PI: 0.144 to 0.461), and shortened the length of intensive care unit (ICU) stay (SMD [95% CI] = −0.601 [−0.820 to −0.382], 95% PI: −1.317 to 0.128) compared with conventional wound care. …”

Extracted data and used for analysis. by Si He (3236646)

“…The overall results revealed that NPWT significantly decreased the sternal wound reinfection (SWRI) rate (RR [95% CI] = 0.179 [0.099 to 0.323], 95% prediction interval [PI]: 0.082 to 0.442), in-hospital mortality (RR [95% CI] = 0.242 [0.149 to 0.394], 95% PI: 0.144 to 0.461), and shortened the length of intensive care unit (ICU) stay (SMD [95% CI] = −0.601 [−0.820 to −0.382], 95% PI: −1.317 to 0.128) compared with conventional wound care. …”