The funnel plot based on COVID-19 infection in non-health care workers (A) and health care workers (B). by Zhuoyang Xia (21354186)

Study selection criteria. by Filip Stanicic (17808254)

“…</p><p>MDMA-AT demonstrated significant improvement in BDI-II score compared to PT (19.7-point vs. 10.8-point decrease, respectively; p = 0.003). …”

PICOS criteria for the SLR. by Filip Stanicic (17808254)

“…</p><p>MDMA-AT demonstrated significant improvement in BDI-II score compared to PT (19.7-point vs. 10.8-point decrease, respectively; p = 0.003). …”

PRISMA flow chart. by Filip Stanicic (17808254)

“…</p><p>MDMA-AT demonstrated significant improvement in BDI-II score compared to PT (19.7-point vs. 10.8-point decrease, respectively; p = 0.003). …”

Subgroup analysis of BUN based on pump type. by Mohamed R. Abdelraouf (21501468)

Subgroup analysis of BUN based on surgery type. by Mohamed R. Abdelraouf (21501468)

Subgroup analysis of ICU Stay based on pump type. by Mohamed R. Abdelraouf (21501468)

Subgroup analysis of eGFR based on pump type. by Mohamed R. Abdelraouf (21501468)

Assessment of study bias in the included research. by Junyin Pan (20700411)

Supplementary Material for: Significant Dry Weight Reduction After Transition from Peritoneal Dialysis to Hemodialysis by Lin Y.-T. (17065287)

“…Background Dry weight management in dialysis patients is crucial but often subjective, primarily based on symptoms. Due to continuous fluid removal in peritoneal dialysis (PD) and intermittent ultrafiltration in hemodialysis (HD), symptom-based assessments may be biased, leading to varying results. …”

Risk of bias assessment for eligible randomized controlled trials. by Junyin Pan (20700411)

Publication bias of the risk of all groups. by Juan Gu (288925)

“…A fixed-effects model was applied if P > 0.1 and I² ≤ 50%; otherwise, a random-effects model was used to account for significant heterogeneity Publication bias was assessed using funnel plots and Egger’s test. …”

Risk of bias graph. by Xiaojuan Yang (794374)

Table 1_Absolute abundance calculation enhances the significance of microbiome data in antibiotic treatment studies.xlsx by Stefanie Wagner (743707)

“…These results were not detectable by standard 16S analysis based on relative abundances. Here, GCN correction additionally uncovered significant decreases of Lactobacillus and Faecalibacterium. …”

Table 3_Absolute abundance calculation enhances the significance of microbiome data in antibiotic treatment studies.xlsx by Stefanie Wagner (743707)

“…These results were not detectable by standard 16S analysis based on relative abundances. Here, GCN correction additionally uncovered significant decreases of Lactobacillus and Faecalibacterium. …”

Table 4_Absolute abundance calculation enhances the significance of microbiome data in antibiotic treatment studies.xlsx by Stefanie Wagner (743707)

“…These results were not detectable by standard 16S analysis based on relative abundances. Here, GCN correction additionally uncovered significant decreases of Lactobacillus and Faecalibacterium. …”

Table 8_Absolute abundance calculation enhances the significance of microbiome data in antibiotic treatment studies.xlsx by Stefanie Wagner (743707)

“…These results were not detectable by standard 16S analysis based on relative abundances. Here, GCN correction additionally uncovered significant decreases of Lactobacillus and Faecalibacterium. …”

Table 2_Absolute abundance calculation enhances the significance of microbiome data in antibiotic treatment studies.xlsx by Stefanie Wagner (743707)

“…These results were not detectable by standard 16S analysis based on relative abundances. Here, GCN correction additionally uncovered significant decreases of Lactobacillus and Faecalibacterium. …”

Table 6_Absolute abundance calculation enhances the significance of microbiome data in antibiotic treatment studies.xlsx by Stefanie Wagner (743707)

“…These results were not detectable by standard 16S analysis based on relative abundances. Here, GCN correction additionally uncovered significant decreases of Lactobacillus and Faecalibacterium. …”

Table 5_Absolute abundance calculation enhances the significance of microbiome data in antibiotic treatment studies.xlsx by Stefanie Wagner (743707)

“…These results were not detectable by standard 16S analysis based on relative abundances. Here, GCN correction additionally uncovered significant decreases of Lactobacillus and Faecalibacterium. …”