Table 2_The association of diabetic retinopathy and diabetic kidney disease in patients with type 2 diabetes mellitus: a prospective observational research study.xlsx by Yutong Wu (3130584)

“…For four T2DM subgroups, the proportion of qi–yin deficiency increased with decreasing DR severity, whereas the proportion of yin–yang deficiency increased with increasing DR severity. …”

Table 3_The association of diabetic retinopathy and diabetic kidney disease in patients with type 2 diabetes mellitus: a prospective observational research study.xlsx by Yutong Wu (3130584)

“…For four T2DM subgroups, the proportion of qi–yin deficiency increased with decreasing DR severity, whereas the proportion of yin–yang deficiency increased with increasing DR severity. …”

Data Sheet 1_The association of diabetic retinopathy and diabetic kidney disease in patients with type 2 diabetes mellitus: a prospective observational research study.pdf by Yutong Wu (3130584)

“…For four T2DM subgroups, the proportion of qi–yin deficiency increased with decreasing DR severity, whereas the proportion of yin–yang deficiency increased with increasing DR severity. …”

Table 4_The association of diabetic retinopathy and diabetic kidney disease in patients with type 2 diabetes mellitus: a prospective observational research study.xlsx by Yutong Wu (3130584)

“…For four T2DM subgroups, the proportion of qi–yin deficiency increased with decreasing DR severity, whereas the proportion of yin–yang deficiency increased with increasing DR severity. …”

Table 1_The association of diabetic retinopathy and diabetic kidney disease in patients with type 2 diabetes mellitus: a prospective observational research study.xlsx by Yutong Wu (3130584)

“…For four T2DM subgroups, the proportion of qi–yin deficiency increased with decreasing DR severity, whereas the proportion of yin–yang deficiency increased with increasing DR severity. …”

Scatterplot 8. by Chao Yang (174810)

“…When the HAR was lower than 0.13, mortality within 28 days after ICU admission decreased significantly as the HAR increased. When the HAR was higher than 0.13, the HAR was not a protective factor for mortality within 28 days after ICU admission. …”

Scatterplot 5. by Chao Yang (174810)

“…When the HAR was lower than 0.13, mortality within 28 days after ICU admission decreased significantly as the HAR increased. When the HAR was higher than 0.13, the HAR was not a protective factor for mortality within 28 days after ICU admission. …”

Scatterplot 9. by Chao Yang (174810)

“…When the HAR was lower than 0.13, mortality within 28 days after ICU admission decreased significantly as the HAR increased. When the HAR was higher than 0.13, the HAR was not a protective factor for mortality within 28 days after ICU admission. …”

Image 2_The association between fluid balance trajectories and prognosis in ICU patients with cardiac arrest, a group-based trajectory model analysis.tiff by Qitian Zhang (19791372)

“…Cox proportional hazards analysis indicated that, after adjusting for various covariates, the survival rate in Trajectory 1 remained significantly higher than in other trajectory groups (Reference, Trajectory 1; Trajectory 2, HR = 1.75 [1.31–2.34], Trajectory 3, HR = 2.02 [1.53, 2.68], Trajectory 4, HR = 1.71 [1.24, 2.37]). …”

Scatterplot 1. by Chao Yang (174810)

“…When the HAR was lower than 0.13, mortality within 28 days after ICU admission decreased significantly as the HAR increased. When the HAR was higher than 0.13, the HAR was not a protective factor for mortality within 28 days after ICU admission. …”

Table 1_The association between fluid balance trajectories and prognosis in ICU patients with cardiac arrest, a group-based trajectory model analysis.xlsx by Qitian Zhang (19791372)

“…Cox proportional hazards analysis indicated that, after adjusting for various covariates, the survival rate in Trajectory 1 remained significantly higher than in other trajectory groups (Reference, Trajectory 1; Trajectory 2, HR = 1.75 [1.31–2.34], Trajectory 3, HR = 2.02 [1.53, 2.68], Trajectory 4, HR = 1.71 [1.24, 2.37]). …”

Scatterplot 6. by Chao Yang (174810)

“…When the HAR was lower than 0.13, mortality within 28 days after ICU admission decreased significantly as the HAR increased. When the HAR was higher than 0.13, the HAR was not a protective factor for mortality within 28 days after ICU admission. …”

Scatterplot 2. by Chao Yang (174810)

“…When the HAR was lower than 0.13, mortality within 28 days after ICU admission decreased significantly as the HAR increased. When the HAR was higher than 0.13, the HAR was not a protective factor for mortality within 28 days after ICU admission. …”

Raw data of this study. by Chao Yang (174810)

“…When the HAR was lower than 0.13, mortality within 28 days after ICU admission decreased significantly as the HAR increased. When the HAR was higher than 0.13, the HAR was not a protective factor for mortality within 28 days after ICU admission. …”

Image 1_The association between fluid balance trajectories and prognosis in ICU patients with cardiac arrest, a group-based trajectory model analysis.tiff by Qitian Zhang (19791372)

“…Cox proportional hazards analysis indicated that, after adjusting for various covariates, the survival rate in Trajectory 1 remained significantly higher than in other trajectory groups (Reference, Trajectory 1; Trajectory 2, HR = 1.75 [1.31–2.34], Trajectory 3, HR = 2.02 [1.53, 2.68], Trajectory 4, HR = 1.71 [1.24, 2.37]). …”

Scatterplot 4. by Chao Yang (174810)

“…When the HAR was lower than 0.13, mortality within 28 days after ICU admission decreased significantly as the HAR increased. When the HAR was higher than 0.13, the HAR was not a protective factor for mortality within 28 days after ICU admission. …”

Scatterplot 3. by Chao Yang (174810)

“…When the HAR was lower than 0.13, mortality within 28 days after ICU admission decreased significantly as the HAR increased. When the HAR was higher than 0.13, the HAR was not a protective factor for mortality within 28 days after ICU admission. …”

Image 4_The association between fluid balance trajectories and prognosis in ICU patients with cardiac arrest, a group-based trajectory model analysis.tiff by Qitian Zhang (19791372)

“…Cox proportional hazards analysis indicated that, after adjusting for various covariates, the survival rate in Trajectory 1 remained significantly higher than in other trajectory groups (Reference, Trajectory 1; Trajectory 2, HR = 1.75 [1.31–2.34], Trajectory 3, HR = 2.02 [1.53, 2.68], Trajectory 4, HR = 1.71 [1.24, 2.37]). …”

Image 5_The association between fluid balance trajectories and prognosis in ICU patients with cardiac arrest, a group-based trajectory model analysis.tif by Qitian Zhang (19791372)

“…Cox proportional hazards analysis indicated that, after adjusting for various covariates, the survival rate in Trajectory 1 remained significantly higher than in other trajectory groups (Reference, Trajectory 1; Trajectory 2, HR = 1.75 [1.31–2.34], Trajectory 3, HR = 2.02 [1.53, 2.68], Trajectory 4, HR = 1.71 [1.24, 2.37]). …”

Image 3_The association between fluid balance trajectories and prognosis in ICU patients with cardiac arrest, a group-based trajectory model analysis.tiff by Qitian Zhang (19791372)

“…Cox proportional hazards analysis indicated that, after adjusting for various covariates, the survival rate in Trajectory 1 remained significantly higher than in other trajectory groups (Reference, Trajectory 1; Trajectory 2, HR = 1.75 [1.31–2.34], Trajectory 3, HR = 2.02 [1.53, 2.68], Trajectory 4, HR = 1.71 [1.24, 2.37]). …”