This study aimed to develop a machine learning-based 2-year risk prediction model for early identification of patients with rapid progressive immunoglobulin A nephropathy (IgAN). We also assessed the model’s performance to predict the long-term kidney-related outcome of patients. Methods: A retrospective cohort of 1,301 patients with biopsy-proven IgAN from two tertiary hospitals was used to derive and externally validate a random forest-based prediction model predicting primary outcome (30% decline in estimated glomerular filtration rate from baseline or end-stage kidney disease requiring renal replacement therapy) and secondary outcome (improvement of proteinuria) within 2 years after kidney biopsy. Results: For the 2-year prediction of primary outcomes, precision, recall, area-under-the-curve, precision-recall-curve, F1, and Brier score were 0.259, 0.875, 0.771, 0.242, 0.400, and 0.309, respectively. The values for the secondary outcome were 0.904, 0.971, 0.694, 0.903, 0.955, and 0.113, respectively. From Shapley Additive exPlanations analysis, the most informative feature identifying both outcomes was baseline proteinuria. When Kaplan-Meier analysis for 10-year kidney outcome risk was performed with three groups by predicting probabilities derived from the 2-year primary outcome prediction model (low, moderate, and high), high (hazard ratio [HR], 13.00; 95% confidence interval [CI], 9.52–17.77) and moderate (HR, 12.90; 95% CI, 9.92–16.76) groups showed higher risks compared with the low group. From the 2-year secondary outcome prediction model, low (HR, 1.66; 95% CI, 1.42–1.95) and moderate (HR, 1.42; 95% CI, 0.99–2.03) groups were at greater risk for 10-year prognosis than the high group. Conclusion: Our machine learning-based 2-year risk prediction models for the progression of IgAN showed reliable performance and effectively predicted long-term kidney outcome.

Background: Sacubitril-valsartan reduces the risk of cardiovascular mortality among patients with heart failure with reduced ejection fraction (HFrEF). However, its long-term protective effects on cardiac function with concurrent acute kidney injury (AKI) remain unclear. This study investigated the recovery of cardiac function relative to kidney function decline. Methods: A total of 512 patients with HFrEF who started sacubitril-valsartan or valsartan treatment were enrolled in cohort 1. Additionally, patients who experienced AKI and underwent follow-up transthoracic echocardiography were enrolled in cohort 2. In cohort 1, short- and long-term kidney outcomes were analyzed. For cohort 2, changes in cardiac function in relation to changes in kidney function after drug initiation were analyzed. Results: The mean age of the patients was 68.3 ± 15.1 years, and 57.4% of the patients were male. AKI occurred in 15.9% of the sacubitril-valsartan group and 12.5% of the valsartan group. After AKI, 78.4% of patients in the sacubitril-valsartan group and 71.4% of those in the valsartan group underwent recovery. Furthermore, cardiovascular outcomes in patients who developed AKI after drug initiation were analyzed in cohort 2. The sacubitril-valsartan group showed a greater improvement in cardiac function compared with the valsartan group (12.4% ± 15.4% vs. 1.4% ± 5.7%, p = 0.046). The ratio of deltas of cardiac and kidney function in the sacubitril-valsartan and valsartan groups were –1.76 ± 2.58 and –0.20 ± 0.58, respectively (p = 0.03). Conclusion Patients with HFrEF treated with sacubitril-valsartan exhibited significant improvements in cardiovascular outcomes despite AKI.

This study aimed to develop a machine learning-based 2-year risk prediction model for early identification of patients with rapid progressive immunoglobulin A nephropathy (IgAN). We also assessed the model’s performance to predict the long-term kidney-related outcome of patients. Methods: A retrospective cohort of 1,301 patients with biopsy-proven IgAN from two tertiary hospitals was used to derive and externally validate a random forest-based prediction model predicting primary outcome (30% decline in estimated glomerular filtration rate from baseline or end-stage kidney disease requiring renal replacement therapy) and secondary outcome (improvement of proteinuria) within 2 years after kidney biopsy. Results: For the 2-year prediction of primary outcomes, precision, recall, area-under-the-curve, precision-recall-curve, F1, and Brier score were 0.259, 0.875, 0.771, 0.242, 0.400, and 0.309, respectively. The values for the secondary outcome were 0.904, 0.971, 0.694, 0.903, 0.955, and 0.113, respectively. From Shapley Additive exPlanations analysis, the most informative feature identifying both outcomes was baseline proteinuria. When Kaplan-Meier analysis for 10-year kidney outcome risk was performed with three groups by predicting probabilities derived from the 2-year primary outcome prediction model (low, moderate, and high), high (hazard ratio [HR], 13.00; 95% confidence interval [CI], 9.52–17.77) and moderate (HR, 12.90; 95% CI, 9.92–16.76) groups showed higher risks compared with the low group. From the 2-year secondary outcome prediction model, low (HR, 1.66; 95% CI, 1.42–1.95) and moderate (HR, 1.42; 95% CI, 0.99–2.03) groups were at greater risk for 10-year prognosis than the high group. Conclusion: Our machine learning-based 2-year risk prediction models for the progression of IgAN showed reliable performance and effectively predicted long-term kidney outcome.

Background: Sacubitril-valsartan reduces the risk of cardiovascular mortality among patients with heart failure with reduced ejection fraction (HFrEF). However, its long-term protective effects on cardiac function with concurrent acute kidney injury (AKI) remain unclear. This study investigated the recovery of cardiac function relative to kidney function decline. Methods: A total of 512 patients with HFrEF who started sacubitril-valsartan or valsartan treatment were enrolled in cohort 1. Additionally, patients who experienced AKI and underwent follow-up transthoracic echocardiography were enrolled in cohort 2. In cohort 1, short- and long-term kidney outcomes were analyzed. For cohort 2, changes in cardiac function in relation to changes in kidney function after drug initiation were analyzed. Results: The mean age of the patients was 68.3 ± 15.1 years, and 57.4% of the patients were male. AKI occurred in 15.9% of the sacubitril-valsartan group and 12.5% of the valsartan group. After AKI, 78.4% of patients in the sacubitril-valsartan group and 71.4% of those in the valsartan group underwent recovery. Furthermore, cardiovascular outcomes in patients who developed AKI after drug initiation were analyzed in cohort 2. The sacubitril-valsartan group showed a greater improvement in cardiac function compared with the valsartan group (12.4% ± 15.4% vs. 1.4% ± 5.7%, p = 0.046). The ratio of deltas of cardiac and kidney function in the sacubitril-valsartan and valsartan groups were –1.76 ± 2.58 and –0.20 ± 0.58, respectively (p = 0.03). Conclusion Patients with HFrEF treated with sacubitril-valsartan exhibited significant improvements in cardiovascular outcomes despite AKI.

This study aimed to develop a machine learning-based 2-year risk prediction model for early identification of patients with rapid progressive immunoglobulin A nephropathy (IgAN). We also assessed the model’s performance to predict the long-term kidney-related outcome of patients. Methods: A retrospective cohort of 1,301 patients with biopsy-proven IgAN from two tertiary hospitals was used to derive and externally validate a random forest-based prediction model predicting primary outcome (30% decline in estimated glomerular filtration rate from baseline or end-stage kidney disease requiring renal replacement therapy) and secondary outcome (improvement of proteinuria) within 2 years after kidney biopsy. Results: For the 2-year prediction of primary outcomes, precision, recall, area-under-the-curve, precision-recall-curve, F1, and Brier score were 0.259, 0.875, 0.771, 0.242, 0.400, and 0.309, respectively. The values for the secondary outcome were 0.904, 0.971, 0.694, 0.903, 0.955, and 0.113, respectively. From Shapley Additive exPlanations analysis, the most informative feature identifying both outcomes was baseline proteinuria. When Kaplan-Meier analysis for 10-year kidney outcome risk was performed with three groups by predicting probabilities derived from the 2-year primary outcome prediction model (low, moderate, and high), high (hazard ratio [HR], 13.00; 95% confidence interval [CI], 9.52–17.77) and moderate (HR, 12.90; 95% CI, 9.92–16.76) groups showed higher risks compared with the low group. From the 2-year secondary outcome prediction model, low (HR, 1.66; 95% CI, 1.42–1.95) and moderate (HR, 1.42; 95% CI, 0.99–2.03) groups were at greater risk for 10-year prognosis than the high group. Conclusion: Our machine learning-based 2-year risk prediction models for the progression of IgAN showed reliable performance and effectively predicted long-term kidney outcome.

Background: Sacubitril-valsartan reduces the risk of cardiovascular mortality among patients with heart failure with reduced ejection fraction (HFrEF). However, its long-term protective effects on cardiac function with concurrent acute kidney injury (AKI) remain unclear. This study investigated the recovery of cardiac function relative to kidney function decline. Methods: A total of 512 patients with HFrEF who started sacubitril-valsartan or valsartan treatment were enrolled in cohort 1. Additionally, patients who experienced AKI and underwent follow-up transthoracic echocardiography were enrolled in cohort 2. In cohort 1, short- and long-term kidney outcomes were analyzed. For cohort 2, changes in cardiac function in relation to changes in kidney function after drug initiation were analyzed. Results: The mean age of the patients was 68.3 ± 15.1 years, and 57.4% of the patients were male. AKI occurred in 15.9% of the sacubitril-valsartan group and 12.5% of the valsartan group. After AKI, 78.4% of patients in the sacubitril-valsartan group and 71.4% of those in the valsartan group underwent recovery. Furthermore, cardiovascular outcomes in patients who developed AKI after drug initiation were analyzed in cohort 2. The sacubitril-valsartan group showed a greater improvement in cardiac function compared with the valsartan group (12.4% ± 15.4% vs. 1.4% ± 5.7%, p = 0.046). The ratio of deltas of cardiac and kidney function in the sacubitril-valsartan and valsartan groups were –1.76 ± 2.58 and –0.20 ± 0.58, respectively (p = 0.03). Conclusion Patients with HFrEF treated with sacubitril-valsartan exhibited significant improvements in cardiovascular outcomes despite AKI.

This study aimed to develop a machine learning-based 2-year risk prediction model for early identification of patients with rapid progressive immunoglobulin A nephropathy (IgAN). We also assessed the model’s performance to predict the long-term kidney-related outcome of patients. Methods: A retrospective cohort of 1,301 patients with biopsy-proven IgAN from two tertiary hospitals was used to derive and externally validate a random forest-based prediction model predicting primary outcome (30% decline in estimated glomerular filtration rate from baseline or end-stage kidney disease requiring renal replacement therapy) and secondary outcome (improvement of proteinuria) within 2 years after kidney biopsy. Results: For the 2-year prediction of primary outcomes, precision, recall, area-under-the-curve, precision-recall-curve, F1, and Brier score were 0.259, 0.875, 0.771, 0.242, 0.400, and 0.309, respectively. The values for the secondary outcome were 0.904, 0.971, 0.694, 0.903, 0.955, and 0.113, respectively. From Shapley Additive exPlanations analysis, the most informative feature identifying both outcomes was baseline proteinuria. When Kaplan-Meier analysis for 10-year kidney outcome risk was performed with three groups by predicting probabilities derived from the 2-year primary outcome prediction model (low, moderate, and high), high (hazard ratio [HR], 13.00; 95% confidence interval [CI], 9.52–17.77) and moderate (HR, 12.90; 95% CI, 9.92–16.76) groups showed higher risks compared with the low group. From the 2-year secondary outcome prediction model, low (HR, 1.66; 95% CI, 1.42–1.95) and moderate (HR, 1.42; 95% CI, 0.99–2.03) groups were at greater risk for 10-year prognosis than the high group. Conclusion: Our machine learning-based 2-year risk prediction models for the progression of IgAN showed reliable performance and effectively predicted long-term kidney outcome.

Background: Sacubitril-valsartan reduces the risk of cardiovascular mortality among patients with heart failure with reduced ejection fraction (HFrEF). However, its long-term protective effects on cardiac function with concurrent acute kidney injury (AKI) remain unclear. This study investigated the recovery of cardiac function relative to kidney function decline. Methods: A total of 512 patients with HFrEF who started sacubitril-valsartan or valsartan treatment were enrolled in cohort 1. Additionally, patients who experienced AKI and underwent follow-up transthoracic echocardiography were enrolled in cohort 2. In cohort 1, short- and long-term kidney outcomes were analyzed. For cohort 2, changes in cardiac function in relation to changes in kidney function after drug initiation were analyzed. Results: The mean age of the patients was 68.3 ± 15.1 years, and 57.4% of the patients were male. AKI occurred in 15.9% of the sacubitril-valsartan group and 12.5% of the valsartan group. After AKI, 78.4% of patients in the sacubitril-valsartan group and 71.4% of those in the valsartan group underwent recovery. Furthermore, cardiovascular outcomes in patients who developed AKI after drug initiation were analyzed in cohort 2. The sacubitril-valsartan group showed a greater improvement in cardiac function compared with the valsartan group (12.4% ± 15.4% vs. 1.4% ± 5.7%, p = 0.046). The ratio of deltas of cardiac and kidney function in the sacubitril-valsartan and valsartan groups were –1.76 ± 2.58 and –0.20 ± 0.58, respectively (p = 0.03). Conclusion Patients with HFrEF treated with sacubitril-valsartan exhibited significant improvements in cardiovascular outcomes despite AKI.

Stroke destroys neurons and their connections leading to focal neurological deficits. Although limited, many patients exhibit a certain degree of spontaneous functional recovery. Structural remodeling of the intracortical axonal connections is implicated in the reorganization of cortical motor representation maps, which is considered to be an underlying mechanism of the improvement in motor function. Therefore, an accurate assessment of intracortical axonal plasticity would be necessary to develop strategies to facilitate functional recovery following a stroke. The present study developed a machine learning-assisted image analysis tool based on multi-voxel pattern analysis in fMRI imaging. Intracortical axons originating from the rostral forelimb area (RFA) were anterogradely traced using biotinylated dextran amine (BDA) following a photothrombotic stroke in the mouse motor cortex. BDA-traced axons were visualized in tangentially sectioned cortical tissues, digitally marked, and converted to pixelated axon density maps. Application of the machine learning algorithm enabled sensitive comparison of the quantitative differences and the precise spatial mapping of the post-stroke axonal reorganization even in the regions with dense axonal projections. Using this method, we observed a substantial extent of the axonal sprouting from the RFA to the premotor cortex and the peri-infarct region caudal to the RFA. Therefore, the machine learningassisted quantitative axonal mapping developed in this study can be utilized to discover intracortical axonal plasticity that may mediate functional restoration following stroke.

Alternative complement pathway dysregulation plays a key role in glomerulonephritis (GN) and is associated with C3 deposition. Herein, we examined pathological and clinical differences between cases of primary GN with C3-dominant (C3D-GN) and nondominant (C3ND-GN) deposition. Methods: We extracted primary GN data from the Korean GlomeruloNEphritis sTudy (KoGNET). C3D-GN was defined as C3 staining two grades greater than C1q, C4, and immunoglobulin via immunofluorescence analysis. To overcome a large difference in the number of patients between the C3D-GN and C3ND-GN groups (31 vs. 9,689), permutation testing was used for analysis. Results: The C3D-GN group exhibited higher serum creatinine (p ≤ 0.001), a greater prevalence of estimated glomerular filtration rate of <60 mL/min/1.72 m2 (p ≤ 0.001), higher (but not significantly so) C-reactive protein level, and lower serum C3 level (p ≤ 0.001). Serum albumin, urine protein/creatinine ratio, number of patients who progressed to end-stage renal disease, and all-cause mortality were comparable between groups. Interstitial fibrosis and mesangial cellularity were greater in the C3D-GN group (p = 0.04 and p = 0.01, respectively) than in the C3ND-GN group. C3 deposition was dominant in the former group (p < 0.001), in parallel with increased subendothelial deposition (p ≤ 0.001). Conclusion: Greater progression of renal injury and higher mortality occurred in patients with C3D-GN than with C3ND-GN, along with pathologic differences in interstitial and mesangial changes.