Fig. 5: CD35-uEV discriminates SA-AKI from non-AKI patients and correlates with AKI severity.

A Sample processing and detection workflow of SA-AKI cohort (n = 134, collected within 24 h after clinical diagnosis of AKI) (Image was created in BioRender. Li, N. (2025) https://BioRender.com/d94efgw); B Expression differences of CD35-uEV among various groups (Mann–Whitney U two-sided statistics). CD35 at single EV (CD35-uEV) was calculated by total CD35 measured by ELISA normalized to EV account; C ROC curve illustrating the ability of CD35-uEV to discriminate AKI from non-AKI sepsis patients (AUC-ROC 0.89, 95% CI 0.83–0.95); D CD35-uEV levels across AKI severity stages were analyzed using Mann–Whitney U two-sided statistics (Compared in pairs) (Non-AKI: n = 44, Stage1: n = 29, Stage2: n = 31, Stage3: n = 10); E Comparative analysis of CD35-uEV concentrations between transient versus persistent AKI was conducted with Mann–Whitney U two-sided statistics (p = 6.1E−5, Transient-AKI: n = 27, Persistent-AKI: n = 43); F ROC curve for CD35-uEV in predicting persistent AKI; G Correlation between CD35-uEV and median recovery time from AKI (log-rank test, p-value: 0.037). The blue and pink translucent bands indicate the 95% confidence intervals (error bands) for the low-risk and high-risk groups, respectively; H–J Correlation (general linear regression) between CD35-uEV and peak serum creatinine (Max_Scr) (r = −0.44, 1.5E−6) (H), procalcitonin levels (r = −0.26, p = 0.009) (I) and hypersensitive C-reactive protein (hs-CRP) levels (r = −0.27, p = 0.006) (J); K The General linear regression based restricted cubic spline curve revealed that CD35-uEV are an independent predictor of the lowest eGFR. Source data are provided as a Source Data file.